6.1.1 Cyanure

• Name:
  Cyanure: An Open-Source Toolbox for Empirical Risk Minimization
• Functional Description:
  Cyanure is an open-source C++ software package with a Python interface. The goal of Arsenic is to provide state-of-the-art solvers for learning linear models, based on stochastic variance-reduced stochastic optimization with acceleration mechanisms and Quasi-Newton principles. Arsenic can handle a large variety of loss functions (logistic, square, squared hinge, multinomial logistic) and regularization functions (l2, l1, elastic-net, fused Lasso, multi-task group Lasso). It provides a simple Python API, which is very close to that of scikit-learn, which should be extended to other languages such as R or Matlab in a near future.
• Release Contributions:
  packaging on conda and pipy + various improvements
• URL:
  http://­thoth.­inrialpes.­fr/­people/­mairal/­arsenic/­welcome.­html
• Contact:
  Julien Mairal
• Participants:
  Julien Mairal, Thomas Ryckeboer

6.1.2 HySUPP

• Keyword:
  Image processing
• Functional Description:
  Toolbox for hyperspectral unmixing. This is a Python package described in the following publication https://hal.science/hal-04180307v2/document
• URL:
  https://­github.­com/­BehnoodRasti/­HySUPP
• Contact:
  Julien Mairal

6.1.3 t-ReX

• Name:
  Software package for improving generalization in supervised models
• Keywords:
  Generalization, Supervised models
• Scientific Description:
  We consider the problem of training a deep neural network on a given classification task, e.g., ImageNet-1K (IN1K), so that it excels at both the training task as well as at other (future) transfer tasks. These two seemingly contradictory properties impose a trade-off between improving the model’s generalization and maintaining its performance on the original task. Models trained with self-supervised learning tend to generalize better than their supervised counterparts for transfer learning, yet, they still lag behind supervised models on IN1K. In this paper, we propose a supervised learning setup that leverages the best of both worlds. We extensively analyze supervised training using multi-scale crops for data augmentation and an expendable projector head, and reveal that the design of the projector allows us to control the trade-off between performance on the training task and transferability. We further replace the last layer of class weights with class prototypes computed on the fly using a memory bank and derive two models: t-ReX that achieves a new state of the art for transfer learning and outperforms top methods such as DINO and PAWS on IN1K, and t-ReX* that matches the highly optimized RSB-A1 model on IN1K while performing better on transfer tasks. Code and pretrained models: https://europe.naverlabs.com/t-rex
• Functional Description:
  In this repository, we provide: - Several pretrained t-ReX and t-ReX* models (proposed in Sariyildiz et al., ICLR 2023) in PyTorch. - Code for training our t-ReX and t-ReX* models on the ImageNet-1K dataset in PyTorch. - Code for running transfer learning evaluations of pretrained models via linear classification over pre-extracted features on 16 downstream datasets.
• URL:
  https://­github.­com/­naver/­trex
• Contact:
  Karteek Alahari

6.1.4 MLXP

• Name:
  Machine Learning eXperimentalist for Python
• Keywords:
  Reproducibility, Replication and consistency, Machine learning
• Functional Description:
  MLXP is an open-source, simple, and lightweight experiment management tool based on Python. It streamlines the experimental process with minimal practitioner overhead while ensuring a high level of reproducibility. As an open-source package, MLXP facilitates experiment launching, logging, and efficient result exploitation. Key components include automated job launching and hierarchical configuration files, logging of experiment outputs along with metadata, automated code and job version management, seamless multi-job submission to a HPC job scheduler, and intuitive result exploitation capabilities including querying results, grouping and aggregation operations.
• URL:
  https://­inria-thoth.­github.­io/­mlxp/­pages/­master/­index.­html
• Contact:
  Michael Arbel

Object-wise Distance Estimation for Event Camera Data

Participants: Nan Cai, Pia Bideau.

Event cameras provide a natural and data efficient representation of visual information, motivating novel computational strategies towards extracting visual information. Inspired by the biological vision system, in this work 23 propose a behavior driven approach for object-wise distance estimation from event camera data. This behavior-driven method mimics how biological systems, like the human eye, stabilize their view based on object distance: distant objects require minimal compensatory rotation to stay in focus, while nearby objects demand greater adjustments to maintain alignment. This adaptive strategy leverages natural stabilization behaviors to estimate relative distances effectively. Unlike traditional vision algorithms that estimate depth across the entire image, our approach targets local depth estimation within a specific region of interest. By aligning events within a small region, we estimate the angular velocity required to stabilize the image motion. We demonstrate that, under certain assumptions, the compensatory rotational flow is inversely proportional to the object's distance. The proposed approach achieves new state-of-the-art accuracy in distance estimation on the dataset EVIMO2.

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Recognizing Facial Expressions in Multi-Modal Context

Participants: Florian Blume, Runfeng Qu, Pia Bideau, Martin Maier, Rasha Abdel Rahman, Olaf Hellwich.

Facial expression perception in humans inherently relies on prior knowledge and contextual cues, contributing to efficient and flexible processing. For instance, multi-modal emotional context (such as voice color, affective text, body pose, etc.) can prompt people to perceive emotional expressions in objectively neutral faces. Drawing inspiration from this, we introduce in our work 20 a novel approach for facial expression classification that goes beyond simple classification tasks. Our model accurately classifies a perceived face and synthesizes the corresponding mental representation perceived by a human when observing a face in context. With this, our model offers visual insights into its internal decision-making process. We achieve this by learning two independent representations of content and context using a VAE-GAN architecture. Subsequently, we propose a novel attention mechanism for context-dependent feature adaptation. The adapted representation is used for classification and to generate a context-augmented expression. We evaluate synthesized expressions in a human study, showing that our model effectively produces approximations of human mental representations. We achieve state-of-the-art classification accuracies.

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Participants: Marah Halawa, Florian Blume, Pia Bideau, Martin Maier, Rasha Abdel Rahman, Olaf Hellwich.

Human communication is multi-modal; e.g., face-to-face interaction involves auditory signals (speech) and visual signals (face movements and hand gestures). Hence, it is essential to exploit multiple modalities when designing machine learning-based facial expression recognition systems. In addition, given the ever-growing quantities of video data that capture human facial expressions, such systems should utilize raw unlabeled videos without requiring expensive annotations. Therefore, in this work 25, we employ a multitask multi-modal self-supervised learning method for facial expression recognition from in-the-wild video data. Our model combines three self-supervised objective functions: First, a multi-modal contrastive loss, that pulls diverse data modalities of the same video together in the representation space. Second, a multi-modal clustering loss that preserves the semantic structure of input data in the representation space. Finally, a multi-modal data reconstruction loss. We conduct a comprehensive study on this multimodal multi-task self-supervised learning method on three facial expression recognition benchmarks. To that end, we examine the performance of learning through different combinations of self-supervised tasks on the facial expression recognition downstream task. Our model ConCluGen outperforms several multi-modal self-supervised and fully supervised baselines on the CMU-MOSEI dataset. Our results generally show that multi-modal self-supervision tasks offer large performance gains for challenging tasks such as facial expression recognition, while also reducing the amount of manual annotations required.

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Vision Transformers Need Registers

Participants: Timothee Darcet, Maxime Oquab, Julien Mairal, Piotr Bojanowski.

Transformers have recently emerged as a powerful tool for learning visual representations. In this paper, we identify and characterize artifacts in feature maps of both supervised and self-supervised ViT networks. The artifacts correspond to high-norm tokens appearing during inference primarily in low-informative background areas of images, that are repurposed for internal computations. In 24, we propose a simple yet effective solution based on providing additional tokens to the input sequence of the Vision Transformer to fill that role. We show that this solution fixes that problem entirely for both supervised and self-supervised models (see Fig 4), sets a new state of the art for self-supervised visual models on dense visual prediction tasks, enables object discovery methods with larger models, and most importantly leads to smoother feature maps and attention maps for downstream visual processing.

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LUDVIG: Learning-free Uplifting of 2D Visual features to Gaussian Splatting scenes.

Participants: Juliette Marrie, Romain Menegaux, Michael Arbel, Diane Larlus, Julien Mairal.

In 43, we address the problem of extending the capabilities of vision foundation models such as DINO, SAM, and CLIP, to 3D tasks. Specifically, we introduce a novel method to uplift 2D image features into 3D Gaussian Splatting scenes. Unlike traditional approaches that rely on minimizing a reconstruction loss, our method employs a simpler and more efficient feature aggregation technique, augmented by a graph diffusion mechanism. Graph diffusion enriches features from a given model, such as CLIP, by leveraging 3D geometry and pairwise similarities induced by another strong model such as DINOv2. Our approach achieves performance comparable to the state of the art on multiple downstream tasks while delivering significant speed-ups. Notably, we obtain competitive segmentation results using generic DINOv2 features, despite DINOv2 not being trained on millions of annotated segmentation masks like SAM. When applied to CLIP features, our method demonstrates strong performance in open-vocabulary object detection tasks, highlighting the versatility of our approach.

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On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models

Participants: Juliette Marrie, Michael Arbel, Julien Mairal, Diane Larlus.

Large pretrained visual models exhibit remarkable generalization across diverse recognition tasks. Yet, real-world applications often demand compact models tailored to specific problems. Variants of knowledge distillation have been devised for such a purpose, enabling task-specific compact models (the students) to learn from a generic large pretrained one (the teacher). In 9, we show that the excellent robustness and versatility of recent pretrained models challenge common practices established in the literature, calling for a new set of optimal guidelines for task-specific distillation. To address the lack of samples in downstream tasks, we also show that a variant of Mixup based on stable diffusion complements standard data augmentation. This strategy eliminates the need for engineered text prompts and improves distillation of generic models into streamlined specialized networks.

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DINOv2: Learning Robust Visual Features without Supervision

Participants: Maxime Oquab, Timothee Darcet, Julien Mairal, Piotr Bojanowski, Armand Joulin.

The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, i.e., features that work across image distributions and tasks without finetuning. This work 11 shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. We revisit existing approaches and combine different techniques to scale our pretraining in terms of data and model size. Most of the technical contributions aim at accelerating and stabilizing the training at scale. In terms of data, we propose an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature. In terms of models, we train a ViT model with 1B parameters and distill it into a series of smaller models that surpass the best available all-purpose features, OpenCLIP on most of the benchmarks at image and pixel levels.

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Learning Representations of Satellite Images From Metadata Supervision

Participants: Jules Bourcier, Gohar Dashyan, Karteek Alahari, Jocelyn Chanussot.

Self-supervised learning is increasingly applied to Earth observation problems that leverage satellite and other remotely sensed data. Within satellite imagery, metadata such as time and location often hold significant semantic information that improves scene understanding. In 21, we introduce Satellite Metadata-Image Pretraining (SatMIP) (see Figure 8), a new approach for harnessing metadata in the pretraining phase through a flexible and unified multimodal learning objective. SatMIP represents metadata as textual captions and aligns images with metadata in a shared embedding space by solving a metadata-image contrastive task. Our model learns a non-trivial image representation that can effectively handle recognition tasks. We further enhance this model by combining image self-supervision and metadata supervision, introducing SatMIPS. As a result,SatMIPS improves over its image-image pretraining baseline, SimCLR, and accelerates convergence. Comparison against four recent contrastive and masked autoencoding-based methods for remote sensing also highlight the efficacy of our approach. Furthermore, our framework enables multimodal classification with metadata to improve the performance of visual features, and yields more robust hierarchical pretraining. Code and pretrained models will be made available at: Satmip software.

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On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models

Participants: Tariq Berrada Ifriqi, Pietro Astolfi, Melissa Hall, Reyhane Askari-Hemmat, Yohann Benchetrit, Marton Havasi, Matthew Muckley, Karteek Alahari, Adriana Romero-Soriano, Jakob Verbeek, Michal Drozdzal.

Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, the key components of the best performing LDM training recipes are oftentimes not available to the research community, preventing apple-to-apple comparisons and hindering the validation of progress in the field. In 27, we perform an in-depth study of LDM training recipes focusing on the performance of models and their training efficiency. To ensure apple-to-apple comparisons, we re-implement five previously published models with their corresponding recipes. Through our study, we explore the effects of (i) the mechanisms used to condition the generative model on semantic information (e.g., text prompt) and control metadata (e.g., crop size, random flip flag, etc.) on the model performance, and (ii) the transfer of the representations learned on smaller and lower-resolution datasets to larger ones on the training efficiency and model performance. We then propose a novel conditioning mechanism that disentangles semantic and control metadata conditionings and sets a new state-of-the-art in class-conditional generation on the ImageNet-1k dataset – with FID improvements of 7% on 256 and 8% on 512 resolutions – as well as text-to-image generation on the CC12M dataset – with FID improvements of 8% on 256 and 23% on 512 resolution (see examples in Figure 9).

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Unlocking Pre-trained Image Backbones for Semantic Image Synthesis

Participants: Tariq Berrada, Jakob Verbeek, Camille Couprie, Karteek Alahari.

Semantic image synthesis, i.e., generating images from user-provided semantic label maps, is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images. Although diffusion models have pushed the state of the art in generative image modeling, the iterative nature of their inference process makes them computationally demanding. Other approaches such as GANs are more efficient as they only need a single feed-forward pass for generation, but the image quality tends to suffer on large and diverse datasets. In this work 19, we propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images by exploiting feature backbone networks pre-trained for tasks such as image classification. We also introduce a new generator architecture with better context modeling and using cross-attention to inject noise into latent variables, leading to more diverse generated images. Our model, which we dub DP-SIMS, achieves state-of-the-art results (see examples in Figure 10) in terms of image quality and consistency with the input label maps on ADE-20K, COCO-Stuff, and Cityscapes, surpassing recent diffusion models while requiring two orders of magnitude less compute for inference.

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Guided Distillation for Semi-Supervised Instance Segmentation

Participants: Tariq Berrada, Camille Couprie, Karteek Alahari, Jakob Verbeek.

Although instance segmentation methods have improved considerably, the dominant paradigm is to rely on fully-annotated training images, which are tedious to obtain. To alleviate this reliance, and boost results, semi-supervised approaches leverage unlabeled data as an additional training signal that limits overfitting to the labeled samples. In this context, we present novel design choices 18 to significantly improve teacher-student distillation models. In particular, we (i) improve the distillation approach by introducing a novel “guided burn-in” stage, and (ii) evaluate different instance segmentation architectures, as well as backbone networks and pre-training strategies. Contrary to previous work which uses only supervised data for the burn-in period of the student model, we also use guidance of the teacher model to exploit unlabeled data in the burn-in period. Our improved distillation approach leads to substantial improvements over previous state-of-the-art results. For example, on the Cityscapes dataset we improve mask-AP from 23.7 to 33.9 when using labels for 10% of images, and on the COCO dataset we improve mask-AP from 18.3 to 34.1 when using labels for only 1% of the training data (see Figure 11).

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Functional Bilevel Optimization for Machine Learning

Participants: Ieva Petrulionyte, Julien Mairal, Michael Arbel.

In 30, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space. These types of problems are most often solved by using methods developed in the parametric setting, where the inner objective is strongly convex with respect to the parameters of the prediction function. The functional point of view does not rely on this assumption and notably allows using over-parameterized neural networks as the inner prediction function. We propose scalable and efficient algorithms for the functional bilevel optimization problem and illustrate the benefits of our approach on instrumental regression and reinforcement learning tasks.

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From CNNs to Shift-Invariant Twin Wavelet Models

Participants: Hubert Leterme, Kévin Polisano, Valérie Perrier, Karteek Alahari.

In this paper 28, we propose a novel antialiasing method to increase shift invariance in convolutional neural networks (CNNs). More precisely, we replace the conventional combination “real-valued convolutions + max pooling” ($ℝ$Max) by “complex-valued convolutions + modulus” ($ℂ$Mod), which produce stable feature representations for band-pass filters with well-defined orientations. In an earlier work, we proved that, for such filters, the two operators yield similar outputs. Therefore, $ℂ$Mod can be viewed as a stable alternative to $ℝ$Max. To separate band-pass filters from other freely-trained kernels, in this paper, we designed a “twin” architecture based on the dual-tree complex wavelet packet transform (DT-$ℂ$WPT), which generates similar outputs as standard CNNs with fewer trainable parameters. In addition to improving stability to small shifts, our experiments on AlexNet and ResNet showed increased prediction accuracy on natural image datasets such as ImageNet and CIFAR10. Furthermore, our approach outperformed recent antialiasing methods based on low-pass filtering by preserving high-frequency information, while reducing memory usage. Figure 13 compares the accuracy and shift invariance of the various methods.

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Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits

Participants: Julien Zhou, Pierre Gaillard, Thibaud Rahier, Julyan Arbel.

In 44, we address the problem of stochastic combinatorial semi-bandits, where a player selects among actions from the power set of a set containing base items. Adaptivity to the problem's structure is essential in order to obtain optimal regret upper bounds. As estimating the coefficients of a covariance matrix can be manageable in practice, leveraging them should improve the regret. We design two “optimistic” covariance-adaptive algorithms relying on online estimations of the covariance structure, with improved gap-free regret. Our second algorithm is the first sampling-based algorithm satisfying a gap-free regret (up to poly-logs).

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Regret

Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit

Participants: Julien Zhou, Pierre Gaillard, Thibaud Rahier, Julyan Arbel.

In 32, we address the online unconstrained submodular maximization problem (Online USM), in a setting with stochastic bandit feedback. In this framework, a decision-maker receives noisy rewards from a nonmonotone submodular function, taking values in a known bounded interval. This paper proposes Double-Greedy - Explore-then-Commit (DG-ETC), adapting the Double-Greedy approach from the offline and online full-information settings. DG-ETC satisfies a $O\left(dlog\right(dT\left)\right)$ problem dependent upper bound for the $1/2$-approximate pseudo-regret, as well as a $O\left(d{T}^{2/3}log{\left(dT\right)}^{1/3}\right)$ problem-free one at the same time, outperforming existing approaches. To that end, we introduce a notion of hardness for submodular functions, characterizing how difficult it is to maximize them with this type of strategy.

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Hardness

MetaCURL: Non-stationary Concave Utility Reinforcement Learning

Participants: Bianca Marin Moreno, Pierre Gaillard, Margaux Brégère, Nadia Oudjane.

In 42, we explore online learning in episodic Markov decision processes on non-stationary environments (changing losses and probability transitions). Our focus is on the Concave Utility Reinforcement Learning problem (CURL), an extension of classical RL for handling convex performance criteria in state-action distributions induced by agent policies. While various machine learning problems can be written as CURL, its non-linearity invalidates traditional Bellman equations. Despite recent solutions to classical CURL, none address non-stationary MDPs. This paper introduces MetaCURL, the first CURL algorithm for non-stationary MDPs. It employs a meta-algorithm running multiple black-box algorithms instances over different intervals, aggregating outputs via a sleeping expert framework. The key hurdle is partial information due to MDP uncertainty. Under partial information on the probability transitions (uncertainty and non-stationarity coming only from external noise, independent of agent state-action pairs), the algorithm achieves optimal dynamic regret without prior knowledge of MDP changes. Unlike approaches for RL, MetaCURL handles adversarial losses. We believe our approach for managing non-stationarity with experts can be of interest to the RL community.

Adaptive Boosting for Online Nonparametric Regression

Participants: Paul Liautaud, Pierre Gaillard, Olivier Wintenberger.

In 41, we study online gradient boosting for nonparametric regression with arbitrary deterministic sequences and general convex losses. The procedure relies on sequentially training generic weak learners using the gradient step of the strong learner. Based on chaining-trees, we employ parameter-free online algorithms on specific weak learners to achieve, after $T$ iterations, optimal regret $𝒪\left(T^{(d-1)/d}\right),d>2$, across Hölder function classes. Moreover, by boosting chaining-trees as node experts within a core tree, our proposed algorithm competes effectively against any pruned tree prediction. Improved regret performances are achieved over oracle pruning, which is aware of the Hölder profile. As a result, we obtain the first computationally efficient algorithm with locally adaptive optimal rates for online regression in an adversarial setting.

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Regret

Online Learning Approach for Survival Analysis

Participants: Camila Fernandez, Pierre Gaillard, Olivier Wintenberger.

In 38, we introduce an online mathematical framework for survival analysis, allowing real time adaptation to dynamic environments and censored data. This framework enables the estimation of event time distributions through an optimal second order online convex optimization algorithm—Online Newton Step (ONS). This approach, previously unexplored, presents substantial advantages, including explicit algorithms with non-asymptotic convergence guarantees. Moreover, we analyze the selection of ONS hyperparameters, which depends on the exp-concavity property and has a significant influence on the regret bound. We introduce an adaptive aggregation method that ensures robustness in hyperparameter selection while maintaining fast regret bounds. These findings can extend beyond the survival analysis field, and are relevant for any case characterized by poor exp-concavity and unstable ONS. Additionally, we propose a stochastic approach for ONS that guarantees logarithmic regret in the case of an exponential hazard model. Next, these assertions are illustrated by simulation experiments, followed by an application to a real dataset. 37 also provides some experimental comparaison of existing algorithms for survival analysis.

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Structured Prediction in Online Learning

Participants: Pierre Boudard, Pierre Gaillard, Alessandro Rudi.

In 35, we study a theoretical and algorithmic framework for structured prediction in the online learning setting. The problem of structured prediction, i.e. estimating function where the output space lacks a vectorial structure, is well studied in the literature of supervised statistical learning. We show that our algorithm is a generalisation of optimal algorithms from the supervised learning setting, and achieves the same excess risk upper bound also when data are not i.i.d. Moreover, we consider a second algorithm designed especially for non-stationary data distributions, including adversarial data. We bound its stochastic regret in function of the variation of the data distributions.

deep PACO: Combining statistical models with deep learning for exoplanet detection and characterization in direct imaging at high contrast

Participants: Olivier Flasseur, Theo Bodrito, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange.

Direct imaging is an active research topic in astronomy for the detection and the characterization of young sub-stellar objects. The very high contrast between the host star and its companions makes the observations particularly challenging. In this context, post-processing methods combining several images recorded with the pupil tracking mode of telescope are needed. In previous works, we have presented a data-driven algorithm, PACO, capturing locally the spatial correlations of the data with a multi-variate Gaussian model. PACO delivers better detection sensitivity and confidence than the standard post-processing methods of the field. However, there is room for improvement due to the approximate fidelity of the PACO statistical model to the time evolving observations. In 5 we propose to combine the statistical model of PACO with supervised deep learning. The data are first pre-processed with the PACO framework to improve the stationarity and the contrast. A convolutional neural network (CNN) is then trained in a supervised fashion to detect the residual signature of synthetic sources. Finally, the trained network delivers a detection map. The photometry of detected sources is estimated by a second CNN. We apply the proposed approach to several datasets from the VLT/SPHERE instrument. Our results show that its detection stage performs significantly better than baseline methods (cADI, PCA), and leads to a contrast improvement up to half a magnitude compared to PACO. The characterization stage of the proposed method performs on average on par with or better than the comparative algorithms (PCA, PACO) for angular separation above 0.5. This approach is illustrated in Figure 18.

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MODEL&CO: Exoplanet detection in angular differential imaging by learning across multiple observations

Participants: Theo Bodrito, Olivier Flasseur, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange.

Direct imaging of exoplanets is particularly challenging due to the high contrast between the planet and the star luminosities, and their small angular separation. In addition to tailored instrumental facilities implementing adaptive optics and coronagraphy, post-processing methods combining several images recorded in pupil tracking mode are needed to attenuate the nuisances corrupting the signals of interest. Most of these post-processing methods build a model of the nuisances from the target observations themselves, resulting in strongly limited detection sensitivity at short angular separations due to the lack of angular diversity. To address this issue, we propose in 1 to build the nuisance model from an archive of multiple observations by leveraging supervised deep learning techniques. The proposed approach casts the detection problem as a reconstruction task and captures the structure of the nuisance from two complementary representations of the data. Unlike methods inspired by reference differential imaging, the proposed model is highly non-linear and does not resort to explicit image-to-image similarity measurements and subtractions. The proposed approach also encompasses statistical modelling of learnable spatial features. The latter is beneficial to improve both the detection sensitivity and the robustness against heterogeneous data. We apply the proposed algorithm to several data sets from the VLT/SPHERE instrument, and demonstrate a superior precision-recall trade-off compared to the PACO algorithm. Interestingly, the gain is especially important when the diversity induced by ADI is the most limited, thus supporting the ability of the proposed approach to learn information across multiple observations.

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Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package

Participants: Behnood Rasti, Alexandre Zouaoui, Julien Mairal, Jocelyn Chanussot.

Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and intimate mixtures of materials in the scenes. Unmixing estimates the fractional abundances of the endmembers within the pixel. Depending on the prior knowledge of endmembers, linear unmixing can be divided into three main groups: supervised, semi-supervised, and unsupervised (blind) linear unmixing. Advances in Image processing and machine learning substantially affected unmixing. This paper 14 provides an overview of advanced and conventional unmixing approaches. Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories. We compare the performance of the unmixing techniques on three simulated and two real datasets. The experimental results reveal the advantages of different unmixing categories for different unmixing scenarios. Moreover, we provide an open-source Python-based package available at github.com/BehnoodRasti/HySUPP to reproduce the results.

Fast Semi-supervised Unmixing using Non-convex Optimization

Participants: Behnood Rasti, Alexandre Zouaoui, Julien Mairal, Jocelyn Chanussot.

In this 13, we introduce a novel linear model tailored for semisupervised/library-based unmixing. Our model incorporates considerations for library mismatch while enabling the enforcement of the abundance sum-to-one constraint (ASC). Unlike conventional sparse unmixing methods, this model involves nonconvex optimization, presenting significant computational challenges. We demonstrate the efficacy of Alternating Methods of Multipliers (ADMM) in cyclically solving these intricate problems. We propose two semisupervised unmixing approaches, each relying on distinct priors applied to the new model in addition to the ASC: sparsity prior and convexity constraint. Our experimental results validate that enforcing the convexity constraint outperforms the sparsity prior for the endmember library. These results are corroborated across three simulated datasets (accounting for spectral variability and varying pixel purity levels) and the Cuprite dataset. Additionally, our comparison with conventional sparse unmixing methods showcases considerable advantages of our proposed model, which entails nonconvex optimization. Notably, our implementations of the proposed algorithms—fast semisupervised unmixing (FaSUn) and sparse unmixing using soft-shrinkage (SUnS)—prove considerably more efficient than traditional sparse unmixing methods. SUnS and FaSUn were implemented using PyTorch and provided in a dedicated Python package called Fast Semisupervised Unmixing (FUnmix), which is open-source and available at https://github.com/BehnoodRasti/FUnmix.

Enhancing Contrastive Learning with Positive Pair Mining for Few-shot Hyperspectral Image Classification

Participants: Nassim Ait Ali Braham, Julien Mairal, Jocelyn Chanussot, Lichao Mou, Xiao Xiang Zhu.

In recent years, deep learning has emerged as the dominant approach for hyperspectral image (HSI) classification. However, deep neural networks require large annotated datasets to generalize well. This limits the applicability of deep learning for real-world HSI classification problems, as manual labeling of thousands of pixels per scene is costly and time consuming. In this article, we tackle the problem of few-shot HSI classification by leveraging state-of-the-art self-supervised contrastive learning with an improved view-generation approach. Traditionally, contrastive learning algorithms heavily rely on hand-crafted data augmentations tailored for natural imagery to generate positive pairs. However, these augmentations are not directly applicable to HSIs, limiting the potential of self-supervised learning in the hyperspectral domain. To overcome this limitation, we introduce in 2 two positive pair-mining strategies for contrastive learning on HSIs. The proposed strategies mitigate the need for high-quality data augmentations, providing an effective solution for few-shot HSI classification. Through extensive experiments, we show that the proposed approach improves accuracy and label efficiency on four popular HSI classification benchmarks. Furthermore, we conduct a thorough analysis of the impact of data augmentation in contrastive learning, highlighting the advantage of our positive pair-mining approach.

Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits

Participants: Daniel Morales Brotons, Thijs Vogels, Hadrien Hendrikx.

Weight averaging of Stochastic Gradient Descent (SGD) iterates is a popular method for training deep learning models. While it is often used as part of complex training pipelines to improve generalization or serve as a ‘teacher’ model, weight averaging lacks proper evaluation on its own. In this work 10, we present a systematic study of the Exponential Moving Average (EMA) of weights. We first explore the training dynamics of EMA, give guidelines for hyperparameter tuning, and highlight its good early performance, partly explaining its success as a teacher. We also observe that EMA requires less learning rate decay compared to SGD since averaging naturally reduces noise, introducing a form of implicit regularization. Through extensive experiments, we show that EMA solutions differ from last-iterate solutions. EMA models not only generalize better but also exhibit improved i) robustness to noisy labels, ii) prediction consistency, iii) calibration and iv) transfer learning. Therefore, we suggest that an EMA of weights is a simple yet effective plug-in to improve the performance of deep learning models. Figure 20 shows the typical performance gap between SGD and EMA.

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The Relative Gaussian Mechanism and its Application to Private Gradient Descent

Participants: Hadrien Hendrikx, Paul Mangold, Aurélien Bellet.

The Gaussian Mechanism (GM), which consists in adding Gaussian noise to a vector-valued query before releasing it, is a standard privacy protection mechanism. In particular, given that the query respects some L2 sensitivity property (the L2 distance between outputs on any two neighboring inputs is bounded), GM guarantees Rényi Differential Privacy (RDP). Unfortunately, precisely bounding the L2 sensitivity can be hard, thus leading to loose privacy bounds. In this paper 26, we consider a Relative L2 sensitivity assumption, in which the bound on the distance between two query outputs may also depend on their norm. Leveraging this assumption, we introduce the Relative Gaussian Mechanism (RGM), in which the variance of the noise depends on the norm of the output. We prove tight bounds on the RDP parameters under relative L2 sensitivity, and characterize the privacy loss incurred by using output-dependent noise. In particular, we show that RGM naturally adapts to a latent variable that would control the norm of the output. Finally, we instantiate our framework to show tight guarantees for Private Gradient Descent, a problem that naturally fits our relative L2 sensitivity assumption. Figure 21 shows that enforcing privacy through the Relative Gaussian Mechanism is competitive with the standard Gaussian Mechanism applied to clipped gradients.

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The Relative Gaussian Mechanism and its Application to Private Gradient Descent

Participants: Renaud Gaucher, Aymeric Dieuleveut, Hadrien Hendrikx.

Distributed approaches have many computational benefits, but they are vulnerable to attacks from a subset of devices transmitting incorrect information. This work 39 investigates Byzantine-resilient algorithms in a decentralized setting, where devices communicate directly with one another. We investigate the notion of breakdown point, and show an upper bound on the number of adversaries that decentralized algorithms can tolerate. This is done through careful study of a specific graph topology, presented in Figure 22. We introduce CG + , an algorithm at the intersection of ClippedGossip and NNA, two popular approaches for robust decentralized learning. CG + meets our upper bound, and thus obtains optimal robustness guarantees, whereas neither of the existing two does. We provide experimental evidence for this gap by presenting an attack tailored to sparse graphs which breaks NNA but against which CG + is robust.

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Investigating Variance Definitions for Mirror Descent with Relative Smoothness

Participants: Hadrien Hendrikx.

Mirror Descent is a popular algorithm, that extends Gradients Descent (GD) beyond the Euclidean geometry. One of its benefits is to enable strong convergence guarantees through smooth-like analyses, even for objectives with exploding or vanishing curvature. This is achieved through the introduction of the notion of relative smoothness, which holds in many of the common use-cases of Mirror descent. While basic deterministic results extend well to the relative setting, most existing stochastic analyses require additional assumptions on the mirror, such as strong convexity (in the usual sense), to ensure bounded variance. In this work 40, we revisit Stochastic Mirror Descent (SMD) proofs in the (relatively-strongly-) convex and relatively-smooth setting, and introduce a new (less restrictive) definition of variance (see Figure 23), which can generally be bounded (globally) under mild regularity assumptions. We then investigate this notion in more details, and show that it naturally leads to strong convergence guarantees for stochastic mirror descent. Finally, we leverage this new analysis to obtain convergence guarantees for the Maximum Likelihood Estimator of a Gaussian with unknown mean and variance.

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Model-Based Super-Resolution for Sentinel-5P Data

Participants: Jocelyn Chanussot.

Sentinel-5P provides excellent spatial information, but its resolution is insufficient to characterize the complex distribution of air contaminants within limited areas. As physical constraints prevent significant advances beyond its nominal resolution, employing processing techniques like single-image super-resolution (SISR) can notably contribute to both research and air quality monitoring applications. This study 3 presents the very first use of such methodologies on Sentinel-5P data. We demonstrate that superior results may be obtained if the degrading filter used to simulate pairs of low- and high-resolution (HR) images is tailored to the acquisition technology at hand, an issue frequently ignored in the scientific literature on the subject. Because of this, as well as the fact that these data have never been deployed in any previous studies, the primary theoretical contribution of this article is the estimation of the degradation model of TROPOspheric Monitoring Instrument (TROPOMI), the sensor mounted on Sentinel-5P. Leveraging this model—which is essential for applications involving super-resolution—we additionally improve a well-known deconvolution-based strategy and present a brand-new neural network that outperforms both traditional super-resolution techniques and well-established neural networks in the field. The findings of this study, which are supported by experimental tests on real Sentinel-5P radiance images, using both full-scale and reduced-scale protocols, offer a baseline for enhancing algorithms that are driven by the understanding of the imaging model and provide an efficient way of evaluating innovative approaches on all the available images.

The code is available at https://github.com/alcarbone/S5P_SISR_Toolbox.

Spectral–Spatial Transformer for Hyperspectral Image Sharpening

Participants: Jocelyn Chanussot.

Convolutional neural networks (CNNs) have recently achieved outstanding performance for hyperspectral (HS) and multispectral (MS) image fusion. However, CNNs cannot explore the long-range dependence for HS and MS image fusion because of their local receptive fields. To overcome this limitation, a transformer is proposed to leverage the long-range dependence from the network inputs 4. Because of the ability of long-range modeling, the transformer overcomes the sole CNN on many tasks, whereas its use for HS and MS image fusion is still unexplored. In this article, we propose a spectral–spatial transformer (SST) to show the potentiality of transformers for HS and MS image fusion. We devise first two branches to extract spectral and spatial features in the HS and MS images by SST blocks, which can explore the spectral and spatial long-range dependence, respectively. Afterward, spectral and spatial features are fused feeding the result back to spectral and spatial branches for information interaction. Finally, the high-resolution (HR) HS image is reconstructed by dense links from all the fused features to make full use of them.

SpectralGPT: Spectral Remote Sensing FoundationModel

Participants: Jocelyn Chanussot.

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT 6, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS Big Data; 2) leverages 3D token generation for spatial-spectral coupling; 3) captures spectrally sequential patterns via multi-target reconstruction; and 4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS Big Data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection.

Quantum Information-Empowered Graph Neural Network for Hyperspectral Change Detection

Participants: Jocelyn Chanussot.

Change detection (CD) is a critical remote sensing technique for identifying changes in the Earth’s surface over time. The outstanding substance identifiability of hyperspectral images (HSIs) has significantly enhanced the detection accuracy, making hyperspectral CD (HCD) an essential technology. The detection accuracy can be further upgraded by leveraging the graph structure of HSIs, motivating us to adopt the graph neural networks (GNNs) in solving HCD. For the first time, this work introduces a quantum deep network (QUEEN) into HCD. Unlike GNN and CNN, both extracting the affine-computing features, QUEEN provides fundamentally different unitary-computing features 7. We demonstrate that through the unitary feature extraction procedure, QUEEN provides radically new information for deciding whether there is a change or not. Hierarchically, a graph feature learning (GFL) module exploits the graph structure of the bitemporal HSIs at the superpixel level, while a quantum feature learning (QFL) module learns the quantum features at the pixel level, as a complementary to GFL by preserving pixel-level detailed spatial information not retained in the superpixels. In the final classification stage, a quantum classifier is designed to cooperate with a traditional fully connected classifier.  

Cross-City Semantic Segmentation (C2Seg) in Multimodal Remote Sensing: Outcome of the 2023 IEEE WHISPERS C2Seg Challenge

Participants: Jocelyn Chanussot.

Given the ever-growing availability of remote sensing data (e.g., Gaofen in China, Sentinel in the EU, and Landsat in the USA), multimodal remote sensing techniques have been garnering increasing attention and have made extraordinary progress in various Earth observation (EO)-related tasks. The data acquired by different platforms can provide diverse and complementary information. The joint exploitation of multimodal remote sensing has been proven effective in improving the existing methods of land-use/land-cover segmentation in urban environments. To boost technical breakthroughs and accelerate the development of EO applications across cities and regions, one important task is to build novel cross-city semantic segmentation models based on modern artificial intelligence technologies and emerging multimodal remote sensing data. This leads to the development of better semantic segmentation models with high transferability among different cities and regions. The Cross-City Semantic Segmentation contest is organized in conjunction with the 13th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) 8.

Investigating Abiotic Sources of Spectral Variability From Multitemporal Hyperspectral Airborne Acquisitions Over the French Guyana Canopy

Participants: Jocelyn Chanussot.

Classifiers trained on airborne hyperspectral imagery are proficient in identifying tree species in hyperdiverse tropical rainforests. However, spectral fluctuations, influenced by intrinsic and environmental factors, such as the heterogeneity of individual crown properties and atmospheric conditions, pose challenges for large-scale mapping. This study proposes an approach to assess the instability of airborne imaging spectroscopy reflectance in response to environmental variability. Through repeated overflights of two tropical forest sites in French Guiana, we explore factors that affect the spectral similarity between dates and acquisitions. By decomposing acquisitions into subsets and analyzing different sources of variability, we analyze the stability of reflectance and various vegetation indices with respect to specific sources of variability. Factors such as the variability of the viewing and sun angles or the variability of the atmospheric state shed light on the impact of sources of spectral instability, informing processing strategies. Our experiments conclude that the environmental factors that affect the canopy reflectance the most vary according to the considered spectral domain. In the short wave infrared (SWIR) domain, solar angle variation is the main source of variability, followed by atmospheric and viewing angles. In the visible and near infrared (VNIR) domain, atmospheric variability dominates, followed by solar angle and viewing angle variabilities. Despite efforts to address these variabilities, significant spectral instability persists, highlighting the need for more robust representations and improved correction methods for reliable species-specific signatures. 12

VOGTNet: Variational Optimization-Guided Two-Stage Network for Multispectral and Panchromatic Image Fusion

Participants: Jocelyn Chanussot.

Multispectral image (MS) and panchromatic image (PAN) fusion, which is also named as multispectral pansharpening, aims to obtain MS with high spatial resolution and high spectral resolution. However, due to the usual neglect of noise and blur generated in the imaging and transmission phases of data during training, many deep learning (DL) pansharpening methods fail to perform on the dataset containing noise and blur. To tackle this problem, a variational optimization-guided two-stage network (VOGTNet) for multispectral pansharpening is proposed in this work, and the performance of variational optimization (VO)-based pansharpening methods relies on prior information and estimates of spatial-spectral degradation from the target image to other two original images. Concretely, we propose a dual-branch fusion network (DBFN) based on supervised learning and train it by using the datasets containing noise and blur to generate the prior fusion result as the prior information that can remove noise and blur in the initial stage. Subsequently, we exploit the estimated spectral response function (SRF) and point spread function (PSF) to simulate the process of spatial-spectral degradation, respectively, thereby making the prior fusion result and the adaptive recovery model (ARM) jointly perform unsupervised learning on the original dataset to restore more image details and results in the generation of the high-resolution MSs in the second stage. Experimental results indicate that the proposed VOGTNet improves pansharpening performance and shows strong robustness against noise and blur. Furthermore, the proposed VOGTNet can be extended to be a general pansharpening framework, which can improve the ability to resist noise and blur of other supervised learning-based pansharpening methods. 15

The source code is available at https://github.com/HZC-1998/VOGTNet.

MeSAM: Multiscale Enhanced Segment Anything Model for Optical Remote Sensing Images

Participants: Jocelyn Chanussot.

Segment anything model (SAM) has been widely applied to various downstream tasks for its excellent performance and generalization capability. However, SAM exhibits three limitations related to remote sensing (RS) semantic segmentation task: 1) the image encoders excessively lose high-frequency information, such as object boundaries and textures, resulting in rough segmentation masks; 2) due to being trained on natural images, SAM faces difficulty in accurately recognizing objects with large-scale variations and uneven distribution in RS images; and 3) the output tokens used for mask prediction are trained on natural images and not applicable to RS image segmentation. In this article, we explore an efficient paradigm for applying SAM to the semantic segmentation of RS images. Furthermore, we propose multiscale enhanced SAM (MeSAM), a new SAM fine-tuning method more suitable for RS images to adapt it to semantic segmentation tasks. Our method first introduces an inception mixer into the image encoder to effectively preserve high-frequency features. Second, by designing a mask decoder with RS correction and incorporating multiscale connections, we make up the difference in SAM from natural images to RS images. Experimental results demonstrated that our method significantly improves the segmentation accuracy of SAM for RS images, outperforming some state-of-the-art (SOTA) methods. 16

The code will be available at https://github.com/Magic-lem/MeSAM.

S2MAE: A Spatial-Spectral Pretraining Foundation Model for Spectral Remote Sensing Data

Participants: Jocelyn Chanussot.

In the expansive domain of computer vision, a myriad of pre-trained models are at our disposal. However, most of these models are designed for natural RGB images and prove inadequate for spectral remote sensing (RS) images. Spectral RS images have two main traits: (1) multiple bands capturing diverse feature information, (2) spatial alignment and consistent spectral sequencing within the spatial-spectral dimension. In this paper, we introduce Spatial-SpectralMAE (S2MAE), a specialized pre-trained architecture for spectral RS imagery. S2MAE employs a 3D transformer for masked autoencoder modeling, integrating learnable spectral-spatial embeddings with a 90% masking ratio. The model efficiently captures local spectral consistency and spatial invariance using compact cube tokens, demonstrating versatility to diverse input characteristics. This adaptability facilitates progressive pretraining on extensive spectral datasets. The effectiveness of S2MAE is validated through continuous pretraining on two sizable datasets, totaling over a million training images. The pre-trained model is subsequently applied to three distinct downstream tasks, with in-depth ablation studies conducted to emphasize its efficacy.29

9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria International Program

9.2.1 Horizon Europe

9.3.1 ANR Project BONSAI

Participants: Michael Arbel.

• Project BONSAI is a multi-disciplinary project aiming at integrating knowledge produced by experts, in the form of simulators, into current machine learning frameworks through bilevel optimization for accurate and efficient inference. We address three challenges. The first one is to develop a deep learning-based approach to simulation-based inference that can adapt to data using bilevel optimization. A second challenge is to depoly the methods to real-world problems which have their specificities. A third challenge is to develop bilevel optimization methods that can handle the non-convexity and over-parameterization arising from using deep learning. The principal investigator is Michael Arbel, and the project involves participants from Toulouse School of Economics, TIMC team at UGA and other INRIA teams (Statify). This project started in April 2024.

9.3.2 MIAI chair: Towards More Data Efficiency in Machine Learning

Participants: Julien Mairal, Karteek Alahari, Massih-Reza Amini, Margot Selosse, Juliette Marrie, Romain Menegaux, Ieva Petrulyonite.

• Training deep neural networks when the amount of annotated data is small or in the presence of adversarial perturbations is challenging. More precisely, for convolutional neural networks, it is possible to engineer visually imperceptible perturbations that can lead to arbitrarily different model predictions. Such a robustness issue is related to the problem of regularization and to the ability to generalizing with few training examples. Our objective is to develop theoretically-grounded approaches that will solve the data efficiency issues of such huge-dimensional models. The principal investigator is Julien Mairal.

9.3.3 MIAI chair: Learning Visual Representations from Interaction for Robot Manipulation Tasks

Participants: Pia Bideau, Karteek Alahari.

• How to grasp an object has been studied in computer vision and robotics and several approaches to this problem exist - either given a 3D shape of an object contact points are determined that lead to a stable hand object configuration or an other line of work aims at reconstructing stable hand object configurations modelling the reconstruction process of hand pose and object pose jointly. In both cases many solutions are possible, although a majority might not be the natural approach that humans would chose - mainly because the intention behind the grasp is omitted. This project aims at learning visual representations from interaction that encode activity information. Encoding such contextual information appears not only to be relevant to synthesise feasible grasps furthermore this is likely to enhance future generalisation skills facilitating adaptation across the same activity but different objects - grasping a cup to pour something into something shares similar motion pattern as grasping a bottle to pour something into something. Inspired by the effectiveness of human grasping, we aim at finding similarly adaptable representations that are capable of guiding complex manipulation skills. To this end we will fuse ideas relying on classical probabilistic modeling of distributions over possible motion trajectories and latent action representations from a conditional variational autoencoder (CVAE). Both of these directions come with complementary strengths and thus provide promising capabilities of modulating the degree of action abstractions at test time to enable both coarse and fine-grained control for real world robot manipulation tasks. The chair is taking place in collaboration with Karteek Alahari, Xavier Alameda-Pineda, and Pierre-Brice Wieber. We have recruited one PhD student and have an intern starting in February 2025.

9.3.4 MIAI chair: Multiscale multimodal multitemporal remote sensing

Participants: Jocelyn Chanussot.

• J. Chanussot was the co-chair, with Wilfried Thuiller (Laboratory for Alpine Ecology) of this MIAI chair that focused on developping advanced methodologies for the exploitation of remote sensing data.

9.3.5 Deep Red

Participants: Jocelyn Chanussot.

• J. Chanussot is the chair of the Deep Red project from the Foundation Grenoble INP under the patronage of Lynred company (2022-2026). The project aims at popularizing the technology of infrared imaging for new usages.

9.3.6 PEPR project Numpex

Participants: Hadrien Hendrikx.

• The 'Numpex' programme's objectives are to design and develop the software building blocks required to equip future 'exascale machines' and to prepare the major application domains that aim to fully exploit the capabilities of such machines for scientific research and industry alike. This project is part of France's response to the next EuroHPC call for expressions of interest (Projet Exascale France) in hosting one of the two major exascale machines planned in Europe for 2024. In this way 'Numpex' will contribute to the creation of a set of tools, software, applications and training which will enable France to remain one of the leaders in the field of international competition through its national Exascale ecosystem that is in step with European strategy.

9.3.7 PEPR project Origins

Participants: Julien Mairal.

• Thoth is involved in the axis “Direct imaging and exoplanet characterization” of the PEPR Origins. This is an on-going collaboration with astronomers from Observatoire de Paris and Lyon and with the Willow team.

10.1.1 Scientific events: organisation

10.1.2 Scientific events: selection

10.1.3 Journal

10.1.4 Invited talks

• J. Mairal. Invited seminar at the Liphy laboratory (physics laboratory), Grenoble.
• J. Mairal. Invited talk at the CRAL laboratory (astrophysics laboratory), Lyon.
• J. Mairal. Invited talk at the LPLC laboratory (cosmology laboratory), Grenoble.
• J. Mairal. Invited talk at Bordeaux University.
• J. Mairal. Invited talk at a workshop organized by Academie des Sciences, Paris.
• J. Mairal. Invited talk at the DEEPK 2024 workshop. Leuven.
• J. Mairal. Invited talk at the LOL workshop, Luminy.
• J. Mairal. Keynote speaker at the Themosia conference (chemistry conference), Rouen.
• J. Mairal. Keynote speaker at the PEPR IA Kick-off day. Grenoble.
• K. Alahari. Invited talks at Minalogic, Grenoble.
• K. Alahari. Invited seminar at CHROMA team, Grenoble.
• P. Gaillard. Invited talk at Erice Summer School, Italy.
• M. Arbel. Invited talk at ACM conference on reproductibility and replicability, Rennes France.
• H. Hendrikx. Invited talk at plénière défi Inria FedMalin, Lyon.
• H. Hendrikx. Invited talk at Kickoff défi Nokia-Inria, Paris.
• H. Hendrikx. Invited talk at plénière PEPR Redeem, Autrans.
• H. Hendrikx. Invited talk at EUROPT, Lund University, Lund.
• H. Hendrikx. Invited talk at the CMAP (Applied math department of Ecole Polytechnique), Puy Saint-Vincent.
• H. Hendrikx. Invited talk at the WDCL workshop, Paris.
• J. Chanussot. Invited talk at the ML4Earth Foundation Models for Earth Observation workshop, Munich, Germany.
• J. Chanussot. Invited talk at IsTerre, Grenoble.
• S. Pesme. Invited talk at the DAO-GHOST seminar in Grenoble.
• S. Pesme. Invited talk at the Gipsa-lab seminar in Grenoble.
• S. Pesme. Invited talk at the MLSP seminar in Lyon.
• S. Pesme. Invited talk at the MALGA seminar in Genoa.
• P. Bideau invited talk at the French Korean Workshop on AI, Networks, Programming Languages and Mathematical Sciences at KAIST, Session: Generative AI.
• P. Bideau invited talk at the INRIA-DFKI Summer School on AI for Robotics, Saarbrücken (Germany).
• P. Bideau invited talk at the Multidisciplinary Institute for Artificial Intelligence (MIAI), Université Grenoble Alpes.

10.1.5 Scientific expertise

• K. Alahari was a member of the CRCN/ISFP 2024 recruitment committee at Grenoble.
• P. Gaillard was member of the recruitment committee for an Assistant Professor position at Univ. Paris-Cité.
• P. Gaillard was member of the recruitment commitee for a lecturer position at Sorbonne Univ.

10.1.6 Research administration

• J. Mairal is a member of the scientific committee (COS) of Inria Grenoble's research center, and also a member of the scientific committee of MIAI.
• K. Alahari is the deputy scientifc director in charge of AI at Inria.
• K. Alahari is one of the scientific directors of the PEPR AI research programme.
• K. Alahari is responsible for the Mathematics and Computer Science specialist field at the MSTII doctoral school.
• K. Alahari is a member of commission prospection postes at LJK.
• J. Chanussot is a member of the Commission Recherche, University Grenoble Alps.

10.2.1 Teaching

• Master: K. Alahari, Advanced Machine Learning, 9h eqTD, M2, UGA, Grenoble.
• Master: K. Alahari, Continually Learning Visual Representations, 15.75h eqTD, M2, Grenoble INP.
• Master: K. Alahari, Graphical Models Inference and Learning, 13.5h eqTD, MVA, M2, CentraleSupelec, Paris.
• Master/PhD: K. Alahari, African Computer Vision Summer School, 4.5h eqTD, Nairobi, Kenya.
• Master: P. Bideau, Advanced Machine Learning, 4.5h eqTD, M2, UGA, Grenoble.
• Master: J. Marrie, Kernel methods for statistical learning, 12h eqTD, M2, African Master on Machine Intelligence (AMMI).
• PhD: J. Mairal, Machine Learning for science, 6h eqTD, algoSB winter school, Cargese.
• Master: J. Mairal, Kernel methods for statistical learning, 12.5h eqTD, M2, ENS Paris-Saclay, France.
• Master: J. Mairal, Kernel methods for statistical learning, 13.5h eqTD, M2, UGA, Grenoble.
• Master: P. Gaillard, Sequential Learning, 13.5h eqTD, M2, Ecole Normale Supérieure, Cachan, France.
• Master: P. Gaillard, Kernel methods for statistical learning, 18h eqTD, M2, UGA, Grenoble.
• Master: P. Gaillard, Online Convex Opitmization, 18h eqTD, M2, Sorbonne Université, Paris.
• Master: P. Gaillard, Mathematical foundations of machine learning, 13.5eqTD, M2, UGA, Grenoble.
• Master: H. Hendrikx, Generalization Properties of Machine Learning Algorithms, 9h eqTD, M2, Université Paris Saclay, Orsay.
• Master: H. Hendrikx, Numerical Optimisation, 25h eqTD, M1, Université Grenoble Alpes, Grenoble.
• Master: M. Arbel, Kernel methods for statistical learning, 13.5h eqTD, M2, UGA, Grenoble.
• Master: M. Arbel, Kernel methods for statistical learning, 12.5h eqTD, M2 MVA, ENS Paris-Saclay.
• Master: J. Chanussot, Hyperspectral remote sensing, 25h eqTD, M2 SICOM, Grenoble INP PHELMA/ENSE3.
• Master: J. Chanussot, Infra-red image processing and artificial intelligence, 15h eqTD, M1 Grenoble INP programme kaléidoscope.
• Master: J. Chanussot, projet intégrateur, 20h eqTD, M2 ASI Grenoble INP ENSE3.
• Master: G. Meanti, lectures on Natural Language Processing, 12 eqTD, University of Genova
• Master: R. Menegaux. Introdiction to Deep Learning, 4.5h eqTD, Sacl-AI 4 Science Workshop, Université Paris-Saclay.
• Master: R. Menegaux. Kernel Methods in Machine Learning, 40h eqTD, African Master’s in Machine Intelligence, Mbour Senegal.
• Master: I. Petrulyonite. Programming Language Semantics and Compiler Design, 33h eqTD. UGA.

10.2.2 Supervision

• Jules Bourcier defended his PhD in Nov 2024. He was co-advised by K. Alahari, J. Chanussot and G. Dashyan (Preligens/Safran.AI).
• Gaspard Beugnot defended his PhD in Jan 2024. He was co-advised by J. Mairal and A. Rudi (Inria Sierra).

10.2.3 Juries

• J. Mairal was member of the PhD committee of Amim El Ahmad, Telecom ParisTech.
• J. Mairal was member of the PhD committee of Benjamin Lambert, UGA.
• J. Mairal was member of the PhD committee of Yingyi Chen, KU Leuven.
• J. Mairal was president of the PhD committee of Yangtao Wang, UGA.
• J. Mairal was president of the PhD committee of Aurelien Gauffre, UGA.
• J. Mairal was reviewer and president of the jury for the PhD thesis of Leon Zheng, ENS Lyon.
• J. Mairal was reviewer for the PhD thesis of Michaël Sander, ENS-PSL.
• J. Mairal was reviewer for the PhD thesis of Charles Laroche, Université Paris-Cité.
• K. Alahari was reviewer for the PhD thesis of Pierre Marza, INSA Lyon.
• K. Alahari was reviewer for the PhD thesis of Louis Fournier, Sorbonne Univ.
• K. Alahari was reviewer and president of the jury for the PhD thesis of Karim Radouane, IMT Mines Alès.
• K. Alahari was reviewer for the PhD thesis of Di Yang, Uôiv. Cote d'Azur.
• K. Alahari was reviewer for the PhD thesis of Harsh Rangawi, IISc Bangalore, India.
• K. Alahari was reviewer for the PhD thesis of Guillaume Lacharme, Univ. Tours.
• K. Alahari was member of the PhD committee of Deniz Engin, Univ. Rennes.
• P. Gaillard was member of the PhD committee of Victor Boone, UGA.
• P. Gaillard was member of the PhD committee of Clara Carlier, IPP Paris.
• H. Hendrikx was member of the PhD committee of Edwige Cyffers, Université de Lille.
• J. Chanussot was reviewer for the PhD thesis of Muhammad Saad Shaikh, Mid Sweden University (Sweden).
• J. Chanussot was reviewer for the PhD thesis of Alex Levering, Wageningen University (The Netherlands).
• J. Chanussot was reviewer for the PhD thesis of Mohamed Abderrazak CHERIF, Université Côte d’Azur, Géoazur.
• J. Chanussot was reviewer for the PhD thesis of Manohar Kumar C.V.S.S., Indian Institute of Space Science and Technology (India).
• J. Chanussot was reviewer for the PhD thesis of Sadia Hussain Dar, Indian Institute of Technology Delhi (India).
• J. Chanussot was reviewer for the PhD thesis of Joseph Jenkins, Université de Toulon.
• J. Chanussot was reviewer for the PhD thesis of Kinan Abbas, Université Littoral Côte d’Opale.
• J. Chanussot was reviewer for the PhD thesis of Mohamed Ali Ghannami, ENSTA Bretagne
• J. Chanussot was reviewer for the PhD thesis of P. Shalmiya, SASTRA, (India)
• J. Chanussot was reviewer for the PhD thesis of Hamza Rami, Institut Polytechnique de Paris
• J. Chanussot was president of the jury for the PhD thesis of Sara Arioli, Université Grenoble Alpes
• J. Chanussot was reviewer for the PhD thesis of Cheick Tidiani Cissé, Université de Technologie de Belfort-Montbéliard
• J. Chanussot was president of the jury for the PhD thesis of Tristan Montagnon, Université Grenoble Alpes
• J. Chanussot was reviewer for the PhD thesis of Max Muzeau, Université Paris Saclay

10.3.1 Others science outreach relevant activities

• J. Mairal participated in a round table on “IA et numérique : Éthique, impact sur la société et sur la science” at Inria's scientific days.
• P. Gaillard participated in a round table on “IA et énergie” at Tech&Fest, Grenoble
• J. Chanussot constructed from scratch an escape game, named “l’affaire Pélican” for the Tech&Fest, Grenoble. The preparation of the game involved 18 students from 5 engineering schools, a 160sqm space was specifically organized featuring different rooms with challenges that needed the use of infrared sensing to be solved. 260 participants could experience the game in teams of 5. The project received some media coverage (TV Grenoble, Dauphiné Libéré, Blogs…).

International journals

• 1 articleT.Théo Bodrito, O.Olivier Flasseur, J.Julien Mairal, J.Jean Ponce, M.Maud Langlois and A.-M.Anne-Marie Lagrange. MODEL&CO: Exoplanet detection in angular differential imaging by learning across multiple observations.Monthly Notices of the Royal Astronomical Society5342September 2024, 1569-1596HALDOIback to text
• 2 articleN. A.Nassim Ait Ali Braham, J.Julien Mairal, J.Jocelyn Chanussot, L.Lichao Mou and X. X.Xiao Xiang Zhu. Enhancing Contrastive Learning With Positive Pair Mining for Few-Shot Hyperspectral Image Classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing17March 2024, 8509-8526HALDOIback to text
• 3 articleA.Alessia Carbone, R.Rocco Restaino, G.Gemine Vivone and J.Jocelyn Chanussot. Model-Based Super-Resolution for Sentinel-5P Data.IEEE Transactions on Geoscience and Remote Sensing62April 2024, 1-16HALDOIback to text
• 4 articleL.Lihui Chen, G.Gemine Vivone, J.Jiayi Qin, J.Jocelyn Chanussot and X.Xiaomin Yang. Spectral–Spatial Transformer for Hyperspectral Image Sharpening.IEEE Transactions on Neural Networks and Learning Systems3511November 2024, 16733-16747HALDOIback to text
• 5 articleO.Olivier Flasseur, T.Théo Bodrito, J.Julien Mairal, J.Jean Ponce, M.Maud Langlois and A.-M.Anne-Marie Lagrange. deep PACO: Combining statistical models with deep learning for exoplanet detection and characterization in direct imaging at high contrast.Monthly Notices of the Royal Astronomical Society5271January 2024, 1534-1562HALDOIback to text
• 6 articleD.Danfeng Hong, B.Bing Zhang, X.Xuyang Li, Y.Yuxuan Li, C.Chenyu Li, J.Jing Yao, N.Naoto Yokoya, H.Hao Li, P.Pedram Ghamisi, X.Xiuping Jia, A.Antonio Plaza, P.Paolo Gamba, J. A.Jon Atli Benediktsson and J.Jocelyn Chanussot. SpectralGPT: Spectral Remote Sensing Foundation Model.IEEE Transactions on Pattern Analysis and Machine Intelligence468August 2024, 5227-5244HALDOIback to text
• 7 articleC.-H.Chia-Hsiang Lin, T.-H.Tzu-Hsuan Lin and J.Jocelyn Chanussot. Quantum Information-Empowered Graph Neural Network for Hyperspectral Change Detection.IEEE Transactions on Geoscience and Remote Sensing62November 2024, 1-15HALDOIback to text
• 8 articleY.Yuheng Liu, Y.Ye Wang, Y.Yifan Zhang, S.Shaohui Mei, J.Jiaqi Zou, Z.Zhuohong Li, F.Fangxiao Lu, W.Wei He, H.Hongyan Zhang, H.Huilin Zhao, C.Chuan Chen, C.Cong Xia, H.Hao Li, G.Gemine Vivone, R.Ronny Hänsch, G.Gulsen Taskin, J.Jing Yao, A.A. Qin, B.Bing Zhang, J.Jocelyn Chanussot and D.Danfeng Hong. Cross-City Semantic Segmentation (C2Seg) in Multimodal Remote Sensing: Outcome of the 2023 IEEE WHISPERS C2Seg Challenge.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing17May 2024, 8851-8862HALDOIback to text
• 9 articleJ.Juliette Marrie, M.Michael Arbel, J.Julien Mairal and D.Diane Larlus. On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models.Transactions on Machine Learning Research JournalMay 2024, 1-24HALback to text
• 10 articleD.Daniel Morales Brotons, T.Thijs Vogels and H.Hadrien Hendrikx. Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits.Transactions on Machine Learning Research JournalApril 2024, 1-27HALback to text
• 11 articleM.Maxime Oquab, T.Timothée Darcet, T.Théo Moutakanni, H.Huy Vo, M.Marc Szafraniec, V.Vasil Khalidov, P.Pierre Fernandez, D.Daniel Haziza, F.Francisco Massa, A.Alaaeldin El-Nouby, M.Mahmoud Assran, N.Nicolas Ballas, W.Wojciech Galuba, R.Russell Howes, P.-Y.Po-Yao Huang, S.-W.Shang-Wen Li, I.Ishan Misra, M.Michael Rabbat, V.Vasu Sharma, G.Gabriel Synnaeve, H.Hu Xu, H.Hervé Jegou, J.Julien Mairal, P.Patrick Labatut, A.Armand Joulin and P.Piotr Bojanowski. DINOv2: Learning Robust Visual Features without Supervision.Transactions on Machine Learning Research Journal2024HALDOIback to text
• 12 articleC.Colin Prieur, A.Antony Laybros, G.Giovanni Frati, D.Daniel Schläpfer, J.Jocelyn Chanussot and G.Grégoire Vincent. Investigating Abiotic Sources of Spectral Variability From Multitemporal Hyperspectral Airborne Acquisitions Over the French Guyana Canopy.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing17October 2024, 18751 - 18768HALDOIback to text
• 13 articleB.Behnood Rasti, A.Alexandre Zouaoui, J.Julien Mairal and J.Jocelyn Chanussot. Fast Semisupervised Unmixing Using Nonconvex Optimization.IEEE Transactions on Geoscience and Remote Sensing62August 2024, 5526713:1-13HALDOIback to text
• 14 articleB.Behnood Rasti, A.Alexandre Zouaoui, J.Julien Mairal and J.Jocelyn Chanussot. Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package.IEEE Transactions on Geoscience and Remote Sensing6255176312024, 1-31HALDOIback to text
• 15 articleP.Peng Wang, Z.Zhongchen He, B.Bo Huang, M. D.Mauro Dalla Mura, H.Henry Leung and J.Jocelyn Chanussot. VOGTNet: Variational Optimization-Guided Two-Stage Network for Multispectral and Panchromatic Image Fusion.IEEE Transactions on Neural Networks and Learning Systems2024, 1-15HALDOIback to text
• 16 articleX.Xichuan Zhou, F.Fu Liang, L.Lihui Chen, H.Haijun Liu, Q.Qianqian Song, G.Gemine Vivone and J.Jocelyn Chanussot. MeSAM: Multiscale Enhanced Segment Anything Model for Optical Remote Sensing Images.IEEE Transactions on Geoscience and Remote Sensing622024, 1-15HALDOIback to text

International peer-reviewed conferences

• 17 inproceedingsT.Tariq Berrada, P.Pietro Astolfi, M.Melissa Hall, M.Marton Havasi, Y.Yohann Benchetrit, A.Adriana Romero-Soriano, K.Karteek Alahari, M.Michal Drozdzal and J.Jakob Verbeek. Boosting Latent Diffusion with Perceptual Objectives.ICLR 2025 - International Conference on Learning RepresentationsSingapore, SingaporeApril 2025HAL
• 18 inproceedingsT.Tariq Berrada, C.Camille Couprie, K.Karteek Alahari and J.Jakob Verbeek. Guided Distillation for Semi-Supervised Instance Segmentation.WACV 2024 - IEEE/CVF Winter Conference on Applications of Computer VisionWaikola, Hawaii, United StatesIEEE2024, 1-11HALback to text
• 19 inproceedingsT.Tariq Berrada, J.Jakob Verbeek, C.Camille Couprie and K.Karteek Alahari. Unlocking Pre-trained Image Backbones for Semantic Image Synthesis.CVPR 2024 - IEEE/CVF Conference on Computer Vision and Pattern RecognitionSeattle, United StatesIEEEJune 2024, 1-21HALback to text
• 20 inproceedingsF.Florian Blume, R.Runfeng Qu, P.Pia Bideau, M.Martin Maier, R. A.Rasha Abdel Rahman and O.Olaf Hellwich. How Do You Perceive My Face? Recognizing Facial Expressions in Multi-Modal Context by Modeling Mental Representations.GCPR 2024 - German Conference on Pattern RecognitionMunich, GermanySeptember 2024, 1-28HALback to text
• 21 inproceedingsJ.Jules Bourcier, G.Gohar Dashyan, K.Karteek Alahari and J.Jocelyn Chanussot. Learning Representations of Satellite Images From Metadata Supervision.ECCV 2024 - 18th European Conference on Computer VisionMilano, ItalySpringer2024, 1-30HALback to text
• 22 inproceedingsA.Antoine Bralet, T. N.Trong Nghia Ngo, E.Emmanuel Trouvé, J.Jocelyn Chanussot and A.Abdourrahmane Atto. DEM-assisted neural network for SAR-to-optical image translation.IGARSS 2024 - IEEE International Geoscience and Remote Sensing SymposiumAthens, GreeceIEEE2024, 7649-7652HALDOI
• 23 inproceedingsN.Nan Cai and P.Pia Bideau. Active Event Alignment for Monocular Distance Estimation.WACV 2024 - IEEE/CVF Winter Conference on Applications of Computer VisionWaikoloa (Hawaï), United StatesIEEE2024, 1-10HALback to text
• 24 inproceedingsT.Timothée Darcet, M.Maxime Oquab, J.Julien Mairal and P.Piotr Bojanowski. Vision Transformers Need Registers.International Conference on Learning Representations (ICLR)Vienne, Austria2024HALback to text
• 25 inproceedingsM.Marah Halawa, F.Florian Blume, P.Pia Bideau, M.Martin Maier, R. A.Rasha Abdel Rahman and O.Olaf Hellwich. Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition.CVPR 2024 - IEEE/CVF Conference on Computer Vision and Pattern RecognitionSeattle, United StatesIEEEApril 2024, 1-12HALDOIback to text
• 26 inproceedingsH.Hadrien Hendrikx, P.Paul Mangold and A.Aurélien Bellet. The Relative Gaussian Mechanism and its Application to Private Gradient Descent.PMLRAISTATS 2024 - 27th International Conference on Artificial Intelligence and Statistics238Valencia, SpainAugust 2024, 3079-3087HALback to text
• 27 inproceedingsT. B.Tariq Berrada Ifriqi, P.Pietro Astolfi, M.Melissa Hall, R.Reyhane Askari-Hemmat, Y.Yohann Benchetrit, M.Marton Havasi, M.Matthew Muckley, K.Karteek Alahari, A.Adriana Romero-Soriano, J.Jakob Verbeek and M.Michal Drozdzal. On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models.NeurIPS 2024 - Thirty-eighth Conference on Neural Information Processing SystemsVancouver, CanadaDecember 2024, 1-21HALback to text
• 28 inproceedingsH.Hubert Leterme, K.Kévin Polisano, V.Valérie Perrier and K.Karteek Alahari. From CNNs to Shift-Invariant Twin Models Based on Complex Wavelets.EUSIPCO 2024 - 32th European Signal Processing ConferenceLyon, FranceAugust 2024HALback to text
• 29 inproceedingsX.Xuyang Li, D.Danfeng Hong and J.Jocelyn Chanussot. S2MAE: A Spatial-Spectral Pretraining Foundation Model for Spectral Remote Sensing Data.CVPR 2024 - IEEE/CVF Conference on Computer Vision and Pattern RecognitionSeattle, United StatesIEEE2024, 27696-27705HALDOIback to text
• 30 inproceedingsI.Ieva Petrulionyte, J.Julien Mairal and M.Michael Arbel. Functional Bilevel Optimization for Machine Learning.Advances in Neural Information Processing System (NeurIPS)NeurIPS 2024 - Thirty-Eighth Annual Conference on Neural Information Processing SystemsVancouver, CanadaDecember 2024, 1-50HALback to text
• 31 inproceedingsD.Duc Pham, M.Matthew Hansen, F.Félicie Dhellemmens, J.Jens Krause and P.Pia Bideau. Watching Swarm Dynamics from Above: A Framework for Advanced Object Tracking in Drone Videos.CVPR 2024 - IEEE/CVF Conference on Computer Vision and Pattern RecognitionSeattle, United StatesIEEE2024, 1-7HAL
• 32 inproceedingsJ.Julien Zhou, P.Pierre Gaillard, T.Thibaud Rahier and J.Julyan Arbel. Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit.Proceedings of Machine Learning ResearchALT 2025 - 36th International Conference on Algorithmic Learning Theory272Milan, ItalyPMLR2025, 1-25HALDOIback to text

Conferences without proceedings

• 33 inproceedingsO.Olivier Flasseur, T.Théo Bodrito, J.Julien Mairal, J.Jean Ponce, M.M. Langlois and A.-M.Anne-Marie Lagrange. From statistical learning to deep learning for improved modeling of the nuisance component in high-contrast imaging.Exosystems IV 2024Lyon, FranceFebruary 2024HAL

Doctoral dissertations and habilitation theses

• 34 thesisA.Alexandre Zouaoui. Sparse and Archetypal Decomposition Algorithms for Hyperspectral Image Restoration and Spectral Unmixing.Université Grenoble Alpes [2020-....]March 2024HAL

Reports & preprints

• 35 miscP.Pierre Boudart, A.Alessandro Rudi and P.Pierre Gaillard. Structured Prediction in Online Learning.June 2024HALback to text
• 36 miscF.Fares El Khoury, E.Edouard Pauwels, S.Samuel Vaiter and M.Michael Arbel. Learning Theory for Kernel Bilevel Optimization.February 2025HAL
• 37 miscC.Camila Fernandez, C. S.Chung Shue Chen, P.Pierre Gaillard and A.Alonso Silva. Experimental Comparison of Ensemble Methods and Time-to-Event Analysis Models Through Integrated Brier Score and Concordance Index.2024HALback to text
• 38 miscC.Camila Fernandez, P.Pierre Gaillard, J.Joseph de Vilmarest and O.Olivier Wintenberger. Online Learning Approach for Survival Analysis.2024HALback to text
• 39 miscR.Renaud Gaucher, A.Aymeric Dieuleveut and H.Hadrien Hendrikx. Achieving Optimal Breakdown for Byzantine Robust Gossip.October 2024HALback to text
• 40 miscH.Hadrien Hendrikx. Investigating Variance Definitions for Stochastic Mirror Descent with Relative Smoothness.April 2024HALback to text
• 41 miscP.Paul Liautaud, P.Pierre Gaillard and O.Olivier Wintenberger. Minimax Adaptive Boosting for Online Nonparametric Regression.October 2024HALback to text
• 42 miscB.Bianca Marin Moreno, M.Margaux Brégère, P.Pierre Gaillard and N.Nadia Oudjane. MetaCURL: Non-stationary Concave Utility Reinforcement Learning.May 2024HALback to text
• 43 miscJ.Juliette Marrie, R.Romain Ménégaux, M.Michael Arbel, D.Diane Larlus and J.Julien Mairal. LUDVIG: Learning-free uplifting of 2d visual features to Gaussian splatting scenes.December 2024HALback to text
• 44 miscJ.Julien Zhou, P.Pierre Gaillard, T.Thibaud Rahier, H.Houssam Zenati and J.Julyan Arbel. Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits.2024HALback to text