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Section: New Software and Platforms
PACBayesianNMF
Keywords: Statistics - Machine learning
Functional Description: Implementing NMF with a PAC-Bayesian approach relying upon block gradient descent
Research Program
Application Domains
New Software and Platforms
New Results
- Axis 1: Data Units Selection in Statistics
- Axis 1: Model-Based Co-clustering for Ordinal Data of different dimensions
- Axis 1: Model-based co-clustering for mixed type data
- Axis 1: Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data
- Axis 1: Gaussian-based visualization of Gaussian and non-Gaussian model-based clustering
- Axis 1: Co-clustering: A versatile way to perform clustering
- Axis 1: Dealing with missing data in model-based clustering through a MNAR model
- Axis 1: Organized Co-Clustering for textual data synthesis
- Axis 1: Model-Based Co-clustering with Co-variables
- Axis 1: Linking canonical and spectral clustering
- Axis 1: Predictive clustering
- Axis 1: Ranking and synchronization from pairwise measurements via SVD
- Axis 1: SPONGE: A generalized eigenproblem for clustering signed networks
- Axis 2: Multi-kernel unmixing and super-resolution using the Modified Matrix Pencil method
- Axis 2: Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
- Axis 2: Learning general sparse additive models from point queries in high dimensions
- Axis 2: Sparse non-negative super-resolution - simplified and stabilized
- Axis 2: Pseudo-Bayesian learning with kernel Fourier transform as prior
- Axis 2: PAC-Bayesian binary activated deep neural networks
- Axis 2: Improved PAC-Bayesian Bounds for Linear Regression
- Axis 2: Multiview Boosting by controlling the diversity and the accuracy of view-specific voters
- Axis 2: PAC-Bayes and Domain Adaptation
- Axis 2: Interpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory
- Axis 2: Still no free lunches: the price to pay for tighter PAC-Bayes bounds
- Axis 2: PAC-Bayesian Contrastive Unsupervised Representation Learning
- Axis 2: Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly
- Axis 2: PAC-Bayes Un-Expected Bernstein Inequality
- Axis 2: Attributing and Referencing (Research) Software: Best Practices and Outlook from Inria
- Axis 2: Revisiting clustering as matrix factorisation on the Stiefel manifold
- Axis 2: A Primer on PAC-Bayesian Learning
- Axis 2: Perturbed Model Validation: A New Framework to Validate Model Relevance
- Axis 2: Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
- Axis 2: Online k-means Clustering
- Axis 2: Non-linear aggregation of filters to improve image denoising
- Axis 2: Multiple change-points detection with reproducing kernels
- Axis 2: Analysis of early stopping rules based on discrepancy principle
- Axis 3: Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models
- Axis 3: Mathematical Modeling and Study of Random or Deterministic Phenomena
- Axis 3: Categorical functional data analysis
- Axis 4: Proteomic signature of early death in heart failure patients
- Axis 4: Statistical analysis of high-throughput proteomic data
- Axis 4: Linking different kinds of Omics data through a model-based clustering approach
- Axis 4: Real-time Audio Sources Classification
- Axis 4: Matching of descriptors evolving over time
- Axis 4: Supervised multivariate discretization and levels merging for logistic regression
- Axis 4: MASSICCC Platform for SaaS Software Availability
- Axis 4: Domain adaptation from a pre-trained source model
- Axis 4: Reject Inference Methods in Credit Scoring: a rational review
- Other: Projection Under Pairwise Control
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
Research Program
Application Domains
New Software and Platforms
New Results
- Axis 1: Data Units Selection in Statistics
- Axis 1: Model-Based Co-clustering for Ordinal Data of different dimensions
- Axis 1: Model-based co-clustering for mixed type data
- Axis 1: Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data
- Axis 1: Gaussian-based visualization of Gaussian and non-Gaussian model-based clustering
- Axis 1: Co-clustering: A versatile way to perform clustering
- Axis 1: Dealing with missing data in model-based clustering through a MNAR model
- Axis 1: Organized Co-Clustering for textual data synthesis
- Axis 1: Model-Based Co-clustering with Co-variables
- Axis 1: Linking canonical and spectral clustering
- Axis 1: Predictive clustering
- Axis 1: Ranking and synchronization from pairwise measurements via SVD
- Axis 1: SPONGE: A generalized eigenproblem for clustering signed networks
- Axis 2: Multi-kernel unmixing and super-resolution using the Modified Matrix Pencil method
- Axis 2: Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
- Axis 2: Learning general sparse additive models from point queries in high dimensions
- Axis 2: Sparse non-negative super-resolution - simplified and stabilized
- Axis 2: Pseudo-Bayesian learning with kernel Fourier transform as prior
- Axis 2: PAC-Bayesian binary activated deep neural networks
- Axis 2: Improved PAC-Bayesian Bounds for Linear Regression
- Axis 2: Multiview Boosting by controlling the diversity and the accuracy of view-specific voters
- Axis 2: PAC-Bayes and Domain Adaptation
- Axis 2: Interpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory
- Axis 2: Still no free lunches: the price to pay for tighter PAC-Bayes bounds
- Axis 2: PAC-Bayesian Contrastive Unsupervised Representation Learning
- Axis 2: Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly
- Axis 2: PAC-Bayes Un-Expected Bernstein Inequality
- Axis 2: Attributing and Referencing (Research) Software: Best Practices and Outlook from Inria
- Axis 2: Revisiting clustering as matrix factorisation on the Stiefel manifold
- Axis 2: A Primer on PAC-Bayesian Learning
- Axis 2: Perturbed Model Validation: A New Framework to Validate Model Relevance
- Axis 2: Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
- Axis 2: Online k-means Clustering
- Axis 2: Non-linear aggregation of filters to improve image denoising
- Axis 2: Multiple change-points detection with reproducing kernels
- Axis 2: Analysis of early stopping rules based on discrepancy principle
- Axis 3: Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models
- Axis 3: Mathematical Modeling and Study of Random or Deterministic Phenomena
- Axis 3: Categorical functional data analysis
- Axis 4: Proteomic signature of early death in heart failure patients
- Axis 4: Statistical analysis of high-throughput proteomic data
- Axis 4: Linking different kinds of Omics data through a model-based clustering approach
- Axis 4: Real-time Audio Sources Classification
- Axis 4: Matching of descriptors evolving over time
- Axis 4: Supervised multivariate discretization and levels merging for logistic regression
- Axis 4: MASSICCC Platform for SaaS Software Availability
- Axis 4: Domain adaptation from a pre-trained source model
- Axis 4: Reject Inference Methods in Credit Scoring: a rational review
- Other: Projection Under Pairwise Control
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography