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
The Hoare-fol Tool
Maxime Folschette
This document presents the tool named ''Application of Hoare Logic and Dijkstra's Weakest Proposition Calculus to Biological Regulatory Networks Using Path Programs with Branching…
RTj: a Java framework for detecting and refactoring rotten green test cases
Matias Martinez, Anne Etien, Stéphane Ducasse +1
Rotten green tests are passing tests which have, at least, one assertion not executed. They give developers a false confidence. In this paper, we present, RTj, a framework that ana…
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling
Cindy Trinh, Emilie Kaufmann, Claire Vernade +1
Stochastic Rank-One Bandits (Katarya et al, (2017a,b)) are a simple framework for regret minimization problems over rank-one matrices of arms. The initially proposed algorithms are…
A quasi-linear irreducibility test in K[[x]][y]
Adrien Poteaux, Martin Weimann
We provide an irreducibility test in the ring K[[x]][y] whose complexity is quasi-linear with respect to the discriminant valuation, assuming the input polynomial F square-free and…
Fixed-Confidence Guarantees for Bayesian Best-Arm Identification
Xuedong Shang, Rianne de Heide, Emilie Kaufmann +2
We investigate and provide new insights on the sampling rule called Top-Two Thompson Sampling (TTTS). In particular, we justify its use for fixed-confidence best-arm identification…
Model-Based Reinforcement Learning Exploiting State-Action Equivalence
Mahsa Asadi, Mohammad Sadegh Talebi, Hippolyte Bourel +1
Leveraging an equivalence property in the state-space of a Markov Decision Process (MDP) has been investigated in several studies. This paper studies equivalence structure in the r…
HIGhER : Improving instruction following with Hindsight Generation for Experience Replay
Geoffrey Cideron, Mathieu Seurin, Florian Strub +1
Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. While these characterizations may…
(Co)inductive Proof Systems for Compositional Proofs in Reachability Logic
Vlad Rusu, David Nowak
Reachability Logic is a formalism that can be used, among others, for expressing partial-correctness properties of transition systems. In this paper we present three proof systems…
MERL: Multi-Head Reinforcement Learning
Yannis Flet-Berliac, Philippe Preux
A common challenge in reinforcement learning is how to convert the agent's interactions with an environment into fast and robust learning. For instance, earlier work makes use of d…
Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition
Amor Ben Tanfous, Hassen Drira, Boulbaba Ben Amor
The detection and tracking of human landmarks in video streams has gained in reliability partly due to the availability of affordable RGB-D sensors. The analysis of such time-varyi…
SPOCC: Scalable POssibilistic Classifier Combination -- toward robust aggregation of classifiers
Mahmoud Albardan, John Klein, Olivier Colot
We investigate a problem in which each member of a group of learners is trained separately to solve the same classification task. Each learner has access to a training dataset (pos…
Feature-Model-Guided Online Learning for Self-Adaptive Systems
Andreas Metzger, Clément Quinton, Zoltán Ádám Mann +2
A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptiv…
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
Naima Otberdout, Mohamed Daoudi, Anis Kacem +2
In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling…
Reasoning about disclosure in data integration in the presence of source constraints
Michael Benedikt, Pierre Bourhis, Louis Jachiet +1
Data integration systems allow users to access data sitting in multiple sources by means of queries over a global schema, related to the sources via mappings. Data sources often co…
Bayesian Optimization using Deep Gaussian Processes
Ali Hebbal, Loic Brevault, Mathieu Balesdent +2
Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the station…
Learning Multiple Markov Chains via Adaptive Allocation
Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
We study the problem of learning the transition matrices of a set of Markov chains from a single stream of observations on each chain. We assume that the Markov chains are ergodic…
Impact of facial landmark localization on facial expression recognition
Romain Belmonte, Benjamin Allaert, Pierre Tirilly +3
Although facial landmark localization (FLL) approaches are becoming increasingly accurate for characterizing facial regions, one question remains unanswered: what is the impact of…
Facial Expressions Analysis Under Occlusions Based on Specificities of Facial Motion Propagation
Delphine Poux, Benjamin Allaert, Jose Mennesson +3
Although much progress has been made in the facial expression analysis field, facial occlusions are still challenging. The main innovation brought by this contribution consists in…
Optical Flow Techniques for Facial Expression Analysis -- a Practical Evaluation Study
Benjamin Allaert, Isaac Ronald Ward, Ioan Marius Bilasco +2
Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expressi…
Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL
Yannis Flet-Berliac, Philippe Preux
In reinforcement learning, policy gradient algorithms optimize the policy directly and rely on sampling efficiently an environment. Nevertheless, while most sampling procedures are…
Accurate reconstruction of EBSD datasets by a multimodal data approach using an evolutionary algorithm
Marie-Agathe Charpagne, Florian Strub, Tresa M. Pollock
A new method has been developed for the correction of the distortions and/or enhanced phase differentiation in Electron Backscatter Diffraction (EBSD) data. Using a multi-modal dat…
Diversity of Solutions: An Exploration Through the Lens of Fixed-Parameter Tractability Theory
Julien Baste, Michael R. Fellows, Lars Jaffke +4
When modeling an application of practical relevance as an instance of a combinatorial problem X, we are often interested not merely in finding one optimal solution for that instanc…
Budgeted Reinforcement Learning in Continuous State Space
Nicolas Carrara, Edouard Leurent, Romain Laroche +3
A Budgeted Markov Decision Process (BMDP) is an extension of a Markov Decision Process to critical applications requiring safety constraints. It relies on a notion of risk implemen…
Asymptotically exact data augmentation: models, properties and algorithms
Maxime Vono, Nicolas Dobigeon, Pierre Chainais
Data augmentation, by the introduction of auxiliary variables, has become an ubiquitous technique to improve convergence properties, simplify the implementation or reduce the compu…