Scool is a machine learning (ML) research group. Scool's research focuses on the study of the sequential decision making under uncertainty problem (SDMUP). In particular, we will consider bandit problems and the reinforcement learning (RL) problem. In a simplified way, RL considers the problem of learning an optimal policy in a Markov Decision Problem (MDP); when the set of states collapses to a single state, this is known as the bandit problem which focuses on the exploration/exploitation problem.

Bandit and RL problems are interesting to study on their own; both types of problems share a number of fundamental issues (convergence analysis, sample complexity, representation, safety, etc); both problems have real applications, different though closely related; the fact that while solving an RL problem, one faces an exploration/exploitation problem and has to solve a bandit problem in each state connects the two types of problems very intimately.

In our work, we also consider settings going beyond the Markovian assumption, in particular non-stationary settings, which represents a challenge common to bandits and RL. We also consider online learning where the goal is to learn a model from a stream of data, such as learning a compressed representation of a stream of data (each data may be a scalar, a vector, or even a more complex data structure such as a tree or a graph). A distinctive aspect of the SDMUP with regards to the rest of the field of ML is that the learning problem takes place within a closed-loop interaction between a learning agent and its environment. This feedback loop makes our field of research very different from the two other sub-fields of ML, supervised and unsupervised learning, even when they are defined in an incremental setting. Hence, SDMUP combines ML with control: the learner is not passive: the learner acts on its environment, and learns from the consequences of these interactions; hence, the learner can act in order to obtain information from the environment.

We wish to go on, studying applied questions and developing theory to come up with sound approaches to the practical resolution of SDMUP tasks, and guide their resolution. Non-stationary environments are a particularly interesting setting; we are studying this setting and developing new tools to approach it in a sound way, in order to have algorithms to detect environment changes as fast as possible, and as reliably as possible, adapt to them, and prove their behavior, in terms of their performance, measured with the regret for instance. We mostly consider non parametric statistical models, that is models in which the number of parameters is not fixed (a parameter may be of any type: a scalar, a vector, a function, etc), so that the model can adapt along learning, and to its changing environment; this also lets the algorithm learn a representation that fits its environment.

Our research is mostly dealing with bandit problems, and reinforcement learning problems. We investigate each thread separately and also in combination, since the management of the exploration/exploitation trade-off is a major issue in reinforcement learning.

On bandit problems, we focus on:

Regarding reinforcement learning, we focus on:

Beyond these objectives, we put a particular emphasis on the study of non-stationary environments. Another area of great concern is the combination of symbolic methods with numerical methods, be it to provide knowledge to the learning algorithm to improve its learning curve, or to better understand what the algorithm has learned and explain its behavior, or to rely on causality rather than on mere correlation.

We also put a particular emphasis on real applications and how to deal with their constraints: lack of a simulator, difficulty to have a realistic model of the problem, small amount of data, dealing with risks, availability of expert knowledge on the task.

Scool has 2 main topics of application:

In each of these two domains, we put forward the investigation and the application of the idea of sequential decision making under uncertainty. Though supervised and non supervised learning have already been studied and applied extensively, sequential decision making remains far less studied; bandits have already been used in many applications of e-commerce (e.g. for computational advertising and recommendation systems). However, in applications where human beings may be severely impacted, bandits and reinforcement learning have not been studied much; moreover, these applications come along with a scarcity of data, and the non availability of a simulator, which prevents heavy computational simulations to come up with safe automatic decision making.

In 2021, in health, we investigate patient follow-up with Prof. F. Pattou's research group (CHU Lille, INSERM, Université de Lille) in project B4H. This effort comes along with investigating how we may use medical data available locally at CHU Lille, and also the national social security data. We also investigate drug repurposing with Prof. A. Delahaye-Duriez (Inserm, Université de Paris) in project Repos. We also study catheter control by way of reinforcement learning with Inria Lille group Defrost, and company Robocath (Rouen).

Regarding sustainable development, we have a set of projects and collaborations regarding agriculture and gardening. With Cirad and CGIAR, we investigate how one may recommend agricultural practices to farmers in developing countries. Through an associate team with Bihar Agriculture University (India), we investigate data collection. Inria exploratory action SR4SG concerns recommender systems at the level of individual gardens.

There are two important aspects that are amply shared common by these two application fields. First, we consider that data collection is an active task: we do not passively observe and record data: we design methods and algorithms to search for useful data. This idea is exploited in most of these works oriented towards applications. Second, many of these projects include a careful management of risks for human beings. We have to take decisions taking care of their consequences on human beings, on eco-systems and life more generally.

Sustainable development is a major field of research and application of Scool. We investigate what machine learning can bring to sustainable development, identifiying challenges ad obstacles, and studying how to overcome them.

Let us mention here:

More details can be found in section .

We organize our research results in a set of categories. The main categories are: bandit problems, reinforcement learning problems, and applications.

Sample complexity bounds for stochastic shortest path with a generative model,

We consider the objective of computing an

Routine Bandits: Minimizing Regret on Recurring Problems,

We study a variant of the multi-armed bandit problem in which a learner faces every day one of B many bandit instances, and call it a routine bandit. More specifically, at each period h

Non-Asymptotic Sequential Tests for Overlapping Hypotheses and application to near optimal arm identification in bandit models,

In this paper, we study sequential testing problems with overlapping hypotheses. We first focus on the simple problem of assessing if the mean µ of a Gaussian distribution is

From Optimality to Robustness: Dirichlet Sampling Strategies in Stochastic Bandits,

The stochastic multi-arm bandit problem has been extensively studied under standard assumptions on the arm's distribution (e.g bounded with known support, exponential family, etc). These assumptions are suitable for many real-world problems but sometimes they require knowledge (on tails for instance) that may not be precisely accessible to the practitioner, raising the question of the robustness of bandit algorithms to model misspecification. In this paper we study a generic Dirichlet Sampling (DS) algorithm, based on pairwise comparisons of empirical indices computed with re-sampling of the arms' observations and a data-dependent exploration bonus. We show that different variants of this strategy achieve provably optimal regret guarantees when the distributions are bounded and logarithmic regret for semi-bounded distributions with a mild quantile condition. We also show that a simple tuning achieve robustness with respect to a large class of unbounded distributions, at the cost of slightly worse than logarithmic asymptotic regret. We finally provide numerical experiments showing the merits of DS in a decision-making problem on synthetic agriculture data.

Top-m identification for linear bandits,

Motivated by an application to drug repurposing, we propose the first algorithms to tackle the identification of the m

Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification,

We study the problem of the identification of m arms with largest means under a fixed error rate

On Limited-Memory Subsampling Strategies for Bandits,

There has been a recent surge of interest in nonparametric bandit algorithms based on subsampling. One drawback however of these approaches is the additional complexity required by random subsampling and the storage of the full history of rewards. Our first contribution is to show that a simple deterministic subsampling rule, proposed in the recent work of Baudry et al. (2020) under the name of ”last-block subsampling”, is asymptotically optimal in one-parameter exponential families. In addition, we prove that these guarantees also hold when limiting the algorithm memory to a polylogarithmic function of the time horizon. These findings open up new perspectives, in particular for non-stationary scenarios in which the arm distributions evolve over time. We propose a variant of the algorithm in which only the most recent observations are used for subsampling, achieving optimal regret guarantees under the assumption of a known number of abrupt changes. Extensive numerical simulations highlight the merits of this approach, particularly when the changes are not only affecting the means of the rewards.
Optimal Thompson Sampling strategies for support-aware CVaR bandits,

In this paper we study a multi-arm bandit problem in which the quality of each arm is measured by the Conditional Value at Risk (CVaR) at some level alpha of the reward distribution. While existing works in this setting mainly focus on Upper Confidence Bound algorithms, we introduce a new Thompson Sampling approach for CVaR bandits on bounded rewards that is flexible enough to solve a variety of problems grounded on physical resources. Building on a recent work by Riou & Honda (2020), we introduce B-CVTS for continuous bounded rewards and M-CVTS for multinomial distributions. On the theoretical side, we provide a non-trivial extension of their analysis that enables to theoretically bound their CVaR regret minimization performance. Strikingly, our results show that these strategies are the first to provably achieve asymptotic optimality in CVaR bandits, matching the corresponding asymptotic lower bounds for this setting. Further, we illustrate empirically the benefit of Thompson Sampling approaches both in a realistic environment simulating a use-case in agriculture and on various synthetic examples.

Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits,

In the fixed budget thresholding bandit problem, an algorithm sequentially allocates a budgeted number of samples to different distributions. It then predicts whether the mean of each distribution is larger or lower than a given threshold. We introduce a large family of algorithms (containing most existing relevant ones), inspired by the Frank-Wolfe algorithm, and provide a thorough yet generic analysis of their performance. This allowed us to construct new explicit algorithms, for a broad class of problems, whose losses are within a small constant factor of the non-adaptive oracle ones. Quite interestingly, we observed that adaptive methods empirically greatly out-perform non-adaptive oracles, an uncommon behavior in standard online learning settings, such as regret minimization. We explain this surprising phenomenon on an insightful toy problem.

Stochastic bandits with groups of similar arms,

We consider a variant of the stochastic multi-armed bandit problem where arms are known to be organized into different groups having the same mean. The groups are unknown but a lower bound q on their size is known. This situation typically appears when each arm can be described with a list of categorical attributes, and the (unknown) mean reward function only depends on a subset of them, the others being redundant. In this case, q is linked naturally to the number of attributes considered redundant, and the number of categories of each attribute. For this structured problem of practical relevance, we first derive the asymptotic regret lower bound and corresponding constrained optimization problem. They reveal the achievable regret can be substantially reduced when compared to the unstructured setup, possibly by a factor q. However, solving exactly the exact constrained optimization problem involves a combinatorial problem. We introduce a lowerbound inspired strategy involving a computationally efficient relaxation that is based on a sorting mechanism. We further prove it achieves a lower bound close to the optimal one up to a controlled factor, and achieves an asymptotic regret q times smaller than the unstructured one. We believe this shows it is a valuable strategy for the practitioner. Last, we illustrate the performance of the considered strategy on numerical experiments involving a large number of arms.

Indexed Minimum Empirical Divergence for Unimodal Bandits,

We consider a multi-armed bandit problem specified by a set of one-dimensional family exponential distributions endowed with a unimodal structure. We introduce IMED-UB, a algorithm that optimally exploits the unimodal-structure, by adapting to this setting the Indexed Minimum Empirical Divergence (IMED) algorithm introduced by Honda and Takemura [2015]. Owing to our proof technique, we are able to provide a concise finite-time analysis of IMED-UB algorithm. Numerical experiments show that IMED-UB competes with the state-of-the-art algorithms.

Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-fidelity Feedback,

In black-box optimization problems, we aim to maximize an unknown objective function, where the function is only accessible through feedbacks of an evaluation or simulation oracle. In real-life, the feedbacks of such oracles are often noisy and available after some unknown delay that may depend on the computation time of the oracle. Additionally, if the exact evaluations are expensive but coarse approximations are available at a lower cost, the feedbacks can have multi-fidelity. In order to address this problem, we propose a generic extension of hierarchical optimistic tree search (HOO), called ProCrastinated Tree Search (PCTS), that flexibly accommodates a delay and noise-tolerant bandit algorithm. We provide a generic proof technique to quantify regret of PCTS under delayed, noisy, and multi-fidelity feedbacks. Specifically, we derive regret bounds of PCTS enabled with delayed-UCB1 (DUCB1) and delayed-UCB-V (DUCBV) algorithms. Given a horizon

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study,

In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such “choices” in a unified on-policy RL framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for on-policy training of RL agents.

Show me the Way: Intrinsic Motivation from Demonstrations,

The study of exploration in the domain of decision making has a long history but remains actively debated. From the vast literature that addressed this topic for decades under various points of view (e.g., developmental psychology, experimental design, artificial intelligence), intrinsic motivation emerged as a concept that can practically be transferred to artificial agents. Especially, in the recent field of Deep Reinforcement Learning (RL), agents implement such a concept (mainly using a novelty argument) in the shape of an exploration bonus, added to the task reward, that encourages visiting the whole environment. This approach is supported by the large amount of theory on RL for which convergence to optimality assumes exhaustive exploration. Yet, Human Beings and mammals do not exhaustively explore the world and their motivation is not only based on novelty but also on various other factors (e.g., curiosity, fun, style, pleasure, safety, competition, etc.). They optimize for life-long learning and train to learn transferable skills in playgrounds without obvious goals. They also apply innate or learned priors to save time and stay safe. For these reasons, we propose to learn an exploration bonus from demonstrations that could transfer these motivations to an artificial agent with little assumptions about their rationale. Using an inverse RL approach, we show that complex exploration behaviors, reflecting different motivations, can be learnt and efficiently used by RL agents to solve tasks for which exhaustive exploration is prohibitive.

Primal Wasserstein Imitation Learning,

Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert. In the present work, we propose a new IL method based on a conceptually simple algorithm: Primal Wasserstein Imitation Learning (PWIL), which ties to the primal form of the Wasserstein distance between the expert and the agent state-action distributions. We present a reward function which is derived offline, as opposed to recent adversarial IL algorithms that learn a reward function through interactions with the environment, and which requires little fine-tuning. We show that we can recover expert behavior on a variety of continuous control tasks of the MuJoCo domain in a sample efficient manner in terms of agent interactions and of expert interactions with the environment. Finally, we show that the behavior of the agent we train matches the behavior of the expert with the Wasserstein distance, rather than the commonly used proxy of performance.

Self-Imitation Advantage Learning,

Self-imitation learning is a Reinforcement Learning (RL) method that encourages actions whose returns were higher than expected, which helps in hard exploration and sparse reward problems. It was shown to improve the performance of on-policy actor-critic methods in several discrete control tasks. Nevertheless, applying self-imitation to the mostly action-value based off-policy RL methods is not straightforward. We propose SAIL, a novel generalization of self-imitation learning for off-policy RL, based on a modification of the Bellman optimality operator that we connect to Advantage Learning. Crucially, our method mitigates the problem of stale returns by choosing the most optimistic return estimate between the observed return and the current action-value for self-imitation. We demonstrate the empirical effectiveness of SAIL on the Arcade Learning Environment, with a focus on hard exploration games.

Kernel-based reinforcement Learning: A finite-time analysis,

We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning problems whose state-action space is endowed with a metric. We introduce Kernel-UCBVI, a model-based optimistic algorithm that leverages the smoothness of the MDP and a non-parametric kernel estimator of the rewards and transitions to efficiently balance exploration and exploitation. Unlike existing approaches with regret guarantees, it does not use any kind of partitioning of the state-action space. For problems with

A kernel-based approach to non-stationary reinforcement learning in metric spaces,

In this work, we propose KeRNS: an algorithm for episodic reinforcement learning in nonstationary Markov Decision Processes (MDPs) whose state-action set is endowed with a metric. Using a non-parametric model of the MDP built with time-dependent kernels, we prove a regret bound that scales with the covering dimension of the state-action space and the total variation of the MDP with time, which quantifies its level of non-stationarity. Our method generalizes previous approaches based on sliding windows and exponential discounting used to handle changing environments. We further propose a practical implementation of KeRNS, we analyze its regret and validate it experimentally.

Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness,

Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize. Intrinsic motivation guidances have thus been developed toward alleviating the resulting exploration problem. They usually incentivize agents to look for new states through novelty signals. Yet, such methods encourage exhaustive exploration of the state space rather than focusing on the environment's salient interaction opportunities. We propose a new exploration method, called Don't Do What Doesn't Matter (DoWhaM), shifting the emphasis from state novelty to state with relevant actions. While most actions consistently change the state when used, e.g. moving the agent, some actions are only effective in specific states, e.g., opening a door, grabbing an object. DoWhaM detects and rewards actions that seldom affect the environment. We evaluate DoWhaM on the procedurallygenerated environment MiniGrid, against state-ofthe-art methods. Experiments consistently show that DoWhaM greatly reduces sample complexity, installing the new state-of-the-art in MiniGrid.

Learning Value Functions in Deep Policy Gradients using Residual Variance,

Policy gradient algorithms have proven to be successful in diverse decision making and control tasks. However, these methods suffer from high sample complexity and instability issues. In this paper, we address these challenges by providing a different approach for training the critic in the actor-critic framework. Our work builds on recent studies indicating that traditional actor-critic algorithms do not succeed in fitting the true value function, calling for the need to identify a better objective for the critic. In our method, the critic uses a new state-value (resp. state-action-value) function approximation that learns the value of the states (resp. state-action pairs) relative to their mean value rather than the absolute value as in conventional actor-critic. We prove the theoretical consistency of the new gradient estimator and observe dramatic empirical improvement across a variety of continuous control tasks and algorithms. Furthermore, we validate our method in tasks with sparse rewards, where we provide experimental evidence and theoretical insights.

READYS: A Reinforcement Learning Based Strategy for Heterogeneous Dynamic Scheduling,

In this paper, we propose READYS, a reinforcement learning algorithm for the dynamic scheduling of computations modeled as a Directed Acyclic Graph (DAGs). Our goal is to develop a scheduling algorithm in which allocation and scheduling decisions are made at runtime, based on the state of the system, as performed in runtime systems such as StarPU or ParSEC. Reinforcement Learning is a natural candidate to achieve this task, since its general principle is to build step by step a strategy that, given the state of the system (the state of the resources and a view of the ready tasks and their successors in our case), makes a decision to optimize a global criterion. Moreover, the use of Reinforcement Learning is natural in a context where the duration of tasks (and communications) is stochastic. We propose READYS that combines Graph Convolutional Networks (GCN) with an Actor-Critic Algorithm (A2C): it builds an adaptive representation of the scheduling problem on the fly and learns a scheduling strategy, aiming at minimizing the makespan. A crucial point is that READYS builds a general scheduling strategy which is neither limited to only one specific application or task graph nor one particular problem size, and that can be used to schedule any DAG. We focus on different types of task graphs originating from linear algebra factorization kernels (CHOLESKY, LU, QR) and we consider heterogeneous platforms made of a few CPUs and GPUs. We first propose to analyze the performance of READYS when learning is performed on a given (platform, kernel, problem size) combination. Using simulations, we show that the scheduling agent obtains performances very similar or even superior to algorithms from the literature, and that it is especially powerful when the scheduling environment contains a lot of uncertainty. We additionally demonstrate that our agent exhibits very promising generalization capabilities. To the best of our knowledge, this is the first paper which shows that reinforcement learning can really be used for dynamic DAG scheduling on heterogeneous resources.

There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning,

We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL). From theoretical considerations, we show that approximate reversibility can be learned through a simple surrogate task: ranking randomly sampled trajectory events in chronological order. Intuitively, pairs of events that are always observed in the same order are likely to be separated by an irreversible sequence of actions. Conveniently, learning the temporal order of events can be done in a fully self-supervised way, which we use to estimate the reversibility of actions from experience, without any priors. We propose two different strategies that incorporate reversibility in RL agents, one strategy for exploration (RAE) and one strategy for control (RAC). We demonstrate the potential of reversibility-aware agents in several environments, including the challenging Sokoban game. In synthetic tasks, we show that we can learn control policies that never fail and reduce to zero the side-effects of interactions, even without access to the reward function.

Adaptive reward-free exploration,

Reward-free exploration is a reinforcement learning setting recently studied by Jin et al., who address it by running several algorithms with regret guarantees in parallel. In our work, we instead propose a more adaptive approach for reward-free exploration which directly reduces upper bounds on the maximum MDP estimation error. We show that, interestingly, our reward-free UCRL algorithm can be seen as a variant of an algorithm of Fiechter from 1994 [11], originally proposed for a different objective that we call best-policy identification. We prove that RF-UCRL needs O (SAH 4 /

Fast active learning for pure exploration in reinforcement learning,

Realistic environments often provide agents with very limited feedback. When the environment is initially unknown, the feedback, in the beginning, can be completely absent, and the agents may first choose to devote all their effort on <i>exploring efficiently</i>. The exploration remains a challenge while it has been addressed with many hand-tuned heuristics with different levels of generality on one side, and a few theoretically backed exploration strategies on the other. Many of them are incarnated by intrinsic motivation and in particular explorations bonuses. A common rule of thumb for exploration bonuses is to use best-policy identification setting, which is another pure-exploration objective, where the environment provides rewards but the agent is not penalized for its behavior during the exploration phase.

UCB Momentum Q-learning: Correcting the bias without forgetting,

We propose UCBMQ, Upper Confidence Bound Momentum Q-learning, a new algorithm for reinforcement learning in tabular and possibly stagedependent, episodic Markov decision process. UCBMQ is based on Q-learning where we add a momentum term and rely on the principle of optimism in face of uncertainty to deal with exploration. Our new technical ingredient of UCBMQ is the use of momentum to correct the bias that Q-learning suffers while, at the same time, limiting the impact it has on the the second-order term of the regret. For UCBMQ, we are able to guarantee a regret of at most

Episodic reinforcement learning in finite MDPs: Minimax lower bounds revisited,

In this paper, we propose new problem-independent lower bounds on the sample complexity and regret in episodic MDPs, with a particular focus on the non-stationary case in which the transition kernel is allowed to change in each stage of the episode. Our main contribution is a lower bound of

Adversarially Guided Actor-Critic,

Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tasks where efficient exploration is a bottleneck. These methods consider a policy (the actor) and a value function (the critic) whose respective losses are built using different motivations and approaches. This paper introduces a third protagonist: the adversary. While the adversary mimics the actor by minimizing the KL-divergence between their respective action distributions, the actor, in addition to learning to solve the task, tries to differentiate itself from the adversary predictions. This novel objective stimulates the actor to follow strategies that could not have been correctly predicted from previous trajectories, making its behavior innovative in tasks where the reward is extremely rare. Our experimental analysis shows that the resulting Adversarially Guided Actor-Critic (AGAC) algorithm leads to more exhaustive exploration. Notably, AGAC outperforms current state-of-the-art methods on a set of various hard-exploration and procedurally-generated tasks.

SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning,

In this paper, we consider risk-sensitive sequential decision-making in model-based reinforcement learning (RL). We introduce a novel quantification of risk, namely composite risk, which takes into account both aleatory and epistemic risk during the learning process.Previous works have considered aleatory or epistemic risk individually, or, an additive combination of the two. We demonstrate that the additive formulation is a particular case of the composite risk, which underestimates the actual CVaR risk even while learning a mixture of Gaussians. In contrast, the composite risk provides a more accurate estimate. We propose to use a bootstrapping method, SENTINEL-K, for distributional RL. SENTINEL-K uses an ensemble of K learners to estimate the return distribution and additionally uses follow the regularized leader (FTRL) from bandit literature for providing a better estimate of the risk on the return distribution. Finally, we experimentally verify that SENTINEL-K estimates the return distribution better, and while used with composite risk estimate,demonstrates better risk-sensitive performance than competing RL algorithms.

Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning,

UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO can consider a variety of tuning choices, reaching from picking transaction code variants over index selections up to database system parameter tuning. UDO uses reinforcement learning to converge to near-optimal configurations, creating and evaluating different configurations via actual query executions (instead of relying on simplifying cost models). To cater to different parameter types, UDO distinguishes heavy parameters (which are expensive to change, e.g. physical design parameters) from light parameters. Specifically for optimizing heavy parameters, UDO uses reinforcement learning algorithms that allow delaying the point at which reward feedback becomes available. This gives us the freedom to optimize the point in time and the order in which different configurations are created and evaluated (by benchmarking a workload sample). UDO uses a cost-based planner to minimize configuration switching overheads. For instance, it aims to amortize the creation of expensive data structures by consecutively evaluating configurations using them. We demonstrate UDO on Postgres as well as MySQL and on TPC-H as well as TPC-C, optimizing a variety of light and heavy parameters concurrently.

UDO: Universal Database Optimization using Reinforcement Learning,

UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO can consider a variety of tuning choices, reaching from picking transaction code variants over index selections up to database system parameter tuning. UDO uses reinforcement learning to converge to near-optimal configurations, creating and evaluating different configurations via actual query executions (instead of relying on simplifying cost models). To cater to different parameter types, UDO distinguishes heavy parameters (which are expensive to change, e.g. physical design parameters) from light parameters. Specifically for optimizing heavy parameters, UDO uses reinforcement learning algorithms that allow delaying the point at which the reward feedback becomes available. This gives us the freedom to optimize the point in time and the order in which different configurations are created and evaluated (by benchmarking a workload sample). UDO uses a cost-based planner to minimize reconfiguration overheads. For instance, it aims to amortize the creation of expensive data structures by consecutively evaluating configurations using them. We evaluate UDO on Postgres as well as MySQL and on TPC-H as well as TPC-C, optimizing a variety of light and heavy parameters concurrently.

Evaluating DAS3H on the EdNet Dataset,

The EdNet dataset is a massive English language dataset that poses unique challenges for student performance prediction. In this paper, we describe and comment the results of our award-winning model DAS3H in the context of knowledge tracing in EdNet.

Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences,

Driven by the unprecedented availability of data, machine learning has become a pervasive and transformative technology across industry and science. Its importance to marine science has been codified as one goal of the UN Ocean Decade. While increasing amounts of, for example, acoustic marine data are collected for research and monitoring purposes, and machine learning methods can achieve automatic processing and analysis of acoustic data, they require large training datasets annotated or labelled by experts. Consequently, addressing the relative scarcity of labelled data is, besides increasing data analysis and processing capacities, one of the main thrust areas. One approach to address label scarcity is the expert-in-the-loop approach which allows analysis of limited and unbalanced data efficiently. Its advantages are demonstrated with our novel deep learning-based expert-in-the-loop framework for automatic detection of turbulent wake signatures in echo sounder data. Using machine learning algorithms, such as the one presented in this study, greatly increases the capacity to analyse large amounts of acoustic data. It would be a first step in realising the full potential of the increasing amount of acoustic data in marine sciences.

On Multi-Armed Bandit Designs for Dose-Finding Trials,

We study the problem of finding the optimal dosage in early stage clinical trials through the multi-armed bandit lens. We advocate the use of the Thompson Sampling principle, a flexible algorithm that can accommodate different types of monotonicity assumptions on the toxicity and efficacy of the doses. For the simplest version of Thompson Sampling, based on a uniform prior distribution for each dose, we provide finite-time upper bounds on the number of sub-optimal dose selections, which is unprecedented for dose-finding algorithms. Through a large simulation study, we then show that variants of Thompson Sampling based on more sophisticated prior distributions outperform state-of-the-art dose identification algorithms in different types of dose-finding studies that occur in phase I or phase I/II trials.

On Meritocracy in Optimal Set Selection,

We consider the problem of selecting a set of individuals from a candidate population in order to maximise utility. When the utility function is defined over sets, this raises the question of how to define meritocracy. We define and analyse an appropriate notion of meritocracy derived from the utility function. We introduce the notion of expected marginal contributions of individuals and analyse its links to the underlying optimisation problem, our notion of meritocracy, and other notions of fairness such as the Shapley value. We also experimentally analyse the effect of different policy structures on the utility and meritocracy in a simulated college admission setting including constraints on statistical parity.

Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge,

We consider the problem of online linear regression in the stochastic setting. We derive high probability regret bounds for online ridge regression and the forward algorithm. This enables us to compare online regression algorithms more accurately and eliminate assumptions of bounded observations and predictions. Our study advocates for the use of the forward algorithm in lieu of ridge due to its enhanced bounds and robustness to the regularization parameter. Moreover, we explain how to integrate it in algorithms involving linear function approximation to remove a boundedness assumption without deteriorating theoretical bounds. We showcase this modification in linear bandit settings where it yields improved regret bounds. Last, we provide numerical experiments to illustrate our results and endorse our intuitions.

Interferometric Graph Transform for Community Labeling,

We present a new approach for learning unsupervised node representations in community graphs. We significantly extend the Interferometric Graph Transform (IGT) to community labeling: this non-linear operator iteratively extracts features that take advantage of the graph topology through demodulation operations. An unsupervised feature extraction step cascades modulus non-linearity with linear operators that aim at building relevant invariants for community labeling. Via a simplified model, we show that the IGT concentrates around the E-IGT: those two representations are related through some ergodicity properties. Experiments on community labeling tasks show that this unsupervised representation achieves performances at the level of the state of the art on the standard and challenging datasets Cora, Citeseer, Pubmed and WikiCS.

Low-Rank Projections of GCNs Laplacian,

In this work, we study the behavior of standard models for community detection under spectral manipulations. Through various ablation experiments, we evaluate the impact of bandpass filtering on the performance of a GCN: we empirically show that most of the necessary and used information for nodes classification is contained in the low-frequency domain, and thus contrary to images, high frequencies are less crucial to community detection. In particular, it is sometimes possible to obtain accuracies at a state-of-the-art level with simple classifiers that rely only on a few low frequencies.

Fast sampling from beta-ensembles,

We study sampling algorithms for

Justicia: A Stochastic SAT Approach to Formally Verify Fairness,

As a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up to propose multiple mathematical definitions, algorithms, and systems to ensure different notions of fairness in ML applications. Given the multitude of propositions, it has become imperative to formally verify the fairness metrics satisfied by different algorithms on different datasets. In this paper, we propose a stochastic satisfiability (SSAT) framework, Justicia, that formally verifies different fairness measures of supervised learning algorithms with respect to the underlying data distribution. We instantiate Justicia on multiple classification and bias mitigation algorithms, and datasets to verify different fairness metrics, such as disparate impact, statistical parity, and equalized odds. Justicia is scalable, accurate, and operates on non-Boolean and compound sensitive attributes unlike existing distribution-based verifiers, such as FairSquare and VeriFair. Being distribution-based by design, Justicia is more robust than the verifiers, such as AIF360, that operate on specific test samples. We also theoretically bound the finite-sample error of the verified fairness measure.

Scool is involved in 1 ANR project:

Scool is involved in some Inria projects:

, headed by B. Raffin, Datamove, Grenoble.

In this challenge, we collaborate with:

In this challenge, we collaborate with L. Gallaraga, CR Inria Rennes, about the combination of statistical and symbolic approaches in machine learning.

Other collaborations in France: