298 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de LilleFR364 papers
- Centre National de la Recherche ScientifiqueFR257 papers
- École Centrale de LilleFR212 papers
- Centre de Recherche en InformatiqueFR203 papers
- Université Paris-SaclayFR74 papers
- Institut d'Astrophysique SpatialeFR64 papers
- Sorbonne UniversitéFR59 papers
- Institut de Recherche en Astrophysique et PlanétologieFR58 papers
- Université Paris CitéFR57 papers
- Université Paris Sciences et LettresFR53 papers
- Ludwig-Maximilians-Universität MünchenDE52 papers
- CEA Paris-SaclayFR51 papers
27 papers · 1 filter
Indexed Minimum Empirical Divergence for Unimodal Bandits
Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
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…
Projection-based Classification of Surfaces for 3D Human Mesh Sequence Retrieval
Emery Pierson, Juan-Carlos Alvarez Paiva, Mohamed Daoudi
We analyze human poses and motion by introducing three sequences of easily calculated surface descriptors that are invariant under reparametrizations and Euclidean transformations.…
From Optimality to Robustness: Dirichlet Sampling Strategies in Stochastic Bandits
Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard
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).…
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge
Reda Ouhamma, Odalric Maillard, Vianney Perchet
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…
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework
Natalia Tomashenko, Salima Mdhaffar, Marc Tommasi +2
This paper investigates methods to effectively retrieve speaker information from the personalized speaker adapted neural network acoustic models (AMs) in automatic speech recogniti…
Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits
Reda Ouhamma, Rémy Degenne, Pierre Gaillard +1
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 e…
Edge-wise funnel output synchronization of heterogeneous agents with relative degree one
Jin Gyu Lee, Thomas Berger, Stephan Trenn +1
When a group of heterogeneous node dynamics are diffusively coupled with a high coupling gain, the group exhibits a collective emergent behavior which is governed by a simple algeb…
Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data
Barbara Pascal, Patrice Abry, Nelly Pustelnik +3
Daily pandemic surveillance, often achieved through the estimation of the reproduction number, constitutes a critical challenge for national health authorities to design countermea…
Explicit construction of the minimum error variance estimator for stochastic LTI state-space systems
Deividas Eringis, John Leth, Zheng-Hua Tan +2
In this short article, we showcase the derivation of the optimal (minimum error variance) estimator, when one part of the stochastic LTI system output is not measured but is able t…
A Survey of Monte Carlo Methods for Parameter Estimation
D. Luengo, L. Martino, M. Bugallo +2
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either…
Mining atmospheric data
Chaabane Djeraba, Jérôme Riedi
This paper overviews two interdependent issues important for mining remote sensing data (e.g. images) obtained from atmospheric monitoring missions. The first issue relates the bui…
On Limited-Memory Subsampling Strategies for Bandits
Dorian Baudry, Yoan Russac, Olivier Cappé
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…
Nonparametric estimation of continuous DPPs with kernel methods
Michaël Fanuel, Rémi Bardenet
Determinantal Point Process (DPPs) are statistical models for repulsive point patterns. Both sampling and inference are tractable for DPPs, a rare feature among models with negativ…
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
Nathan Grinsztajn, Johan Ferret, Olivier Pietquin +2
We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL). From theoretical considerations, we show…
A Study On the Effects of Pre-processing On Spatio-temporal Action Recognition Using Spiking Neural Networks Trained with STDP
El-Assal Mireille, Tirilly Pierre, Bilasco Ioan Marius
There has been an increasing interest in spiking neural networks in recent years. SNNs are seen as hypothetical solutions for the bottlenecks of ANNs in pattern recognition, such a…
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness
Mathieu Seurin, Florian Strub, Philippe Preux +1
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 alle…
Deep Spiking Convolutional Neural Network for Single Object Localization Based On Deep Continuous Local Learning
Sami Barchid, José Mennesson, Chaabane Djéraba
With the advent of neuromorphic hardware, spiking neural networks can be a good energy-efficient alternative to artificial neural networks. However, the use of spiking neural netwo…
Human Motion Prediction Using Manifold-Aware Wasserstein GAN
Baptiste Chopin, Naima Otberdout, Mohamed Daoudi +1
Human motion prediction aims to forecast future human poses given a prior pose sequence. The discontinuity of the predicted motion and the performance deterioration in long-term ho…
Concentration study of M-estimators using the influence function
Timothée Mathieu
We present a new finite-sample analysis of M-estimators of locations in using the tool of the influence function. In particular, we show that the deviations of an M-…
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel +2
Commonly, machine learning models minimize an empirical expectation. As a result, the trained models typically perform well for the majority of the data but the performance may det…
LPV Modeling of Nonlinear Systems: A Multi-Path Feedback Linearization Approach
Hossam S. Abbas, Roland Tóth, Mihály Petreczky +3
This paper introduces a systematic approach to synthesize linear parameter-varying (LPV) representations of nonlinear (NL) systems which are described by input affine state-space (…
Combining Forensics and Privacy Requirements for Digital Images
Pauline Puteaux, Vincent Itier, Patrick Bas
This paper proposes to study the impact of image selective encryption on both forensics and privacy preserving mechanisms. The proposed selective encryption scheme works independen…
Offline Reinforcement Learning with Pseudometric Learning
Robert Dadashi, Shideh Rezaeifar, Nino Vieillard +3
Offline Reinforcement Learning methods seek to learn a policy from logged transitions of an environment, without any interaction. In the presence of function approximation, and und…
Adversarially Guided Actor-Critic
Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin +2
Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tas…