Online Algorithms & Learning
Prophets, Secretaries, Matchings and Scheduling
Online matching in geometric random graphs (F. Sentenac, N. Noiry, M. Lerasle, L. Ménard, V. Perchet), Mathematics of Operations Research, à paraître.
On tradeoffs in learning-augmented algorithms (Z. Benomar, V. Perchet), AISTATS, 2025.
Prophet inequalities: Competing with the top l items is easy (M. Molina, N. Gast, P. Loiseau, V. Perchet), SODA, 2025.
Dynamic online matching with budget refills (M. Cherifa, C. Calauzènes, V. Perchet), arXiv preprint arXiv:2405.09920, 2024.
Advice querying under budget constraint for online algorithms (Z. Benomar, V. Perchet), NeurIPS, 2024.
Non-clairvoyant scheduling with partial predictions (Z. Benomar, V. Perchet), NeurIPS, 2024.
Lookback prophet inequalities (Z. Benomar, D. Baudry, V. Perchet), NeurIPS, 2024.
The value of reward lookahead in reinforcement learning (N. Merlis, D. Baudry, V. Perchet), NeurIPS, 2024.
Advice querying under budget constraint for online algorithms (V. Perchet, Z. Benomar), NeurIPS, 2023.
On preemption and learning in stochastic scheduling (N. Merlis, H. Richard, F. Sentenac, C. Odic, M. Molina, V. Perchet), ICML, 2023.
Online matching in sparse random graphs: Non-asymptotic performances of greedy algorithm (N. Noiry, V. Perchet, F. Sentenac), NeurIPS, 2021.
Multi-Armed Bandits
Multi-armed bandits with guaranteed revenue per arm (D. Baudry, N. Merlis, M. B. Molina, H. Richard, V. Perchet), AISTATS, 2024.
Active ranking and matchmaking, with perfect matchings (H. El Ferchichi, M. Lerasle, V. Perchet), ICML, 2024.
Optimizing the coalition gain in online auctions with greedy structured bandits (D. Baudry, H. Richard, M. Cherifa, C. Calauzènes, V. Perchet), NeurIPS, 2024.
Addressing bias in online selection with limited budget of comparisons (Z. Benomar, E. Chzhen, N. Schreuder, V. Perchet), NeurIPS, 2024.
Trading-off price for data quality to achieve fair online allocation (M. Molina, N. Gast, P. Loiseau, V. Perchet), NeurIPS, 2023.
Privacy amplification via shuffling for linear contextual bandits (E. Garcelon, K. Chaudhuri, V. Perchet, M. Pirotta), ALT, 2022.
Encrypted linear contextual bandit (E. Garcelon, M. Pirotta, V. Perchet), AISTATS, 2022.
Pure exploration and regret minimization in matching bandits (F. Sentenac, J. Yi, C. Calauzènes, V. Perchet, M. Vojnovic), ICML, 2021.
Online sign identification: Minimization of the number of errors in thresholding bandits (R. Ouhamma, R. Degenne, P. Gaillard, V. Perchet), NeurIPS, 2021.
Stochastic online linear regression: The forward algorithm to replace ridge (R. Ouhamma, O.-A. Maillard, V. Perchet), NeurIPS, 2021.
Making the most of your day: Online learning for optimal allocation of time (E. Boursier, T. Garrec, V. Perchet, M. Scarsini), NeurIPS, 2021.
Be greedy in multi-armed bandits (M. Jedor, J. Louëdec, V. Perchet), arXiv preprint arXiv:2101.01086, 2021.
Lifelong learning in multi-armed bandits (M. Jedor, J. Louëdec, V. Perchet), arXiv preprint arXiv:2012.14264, 2020.
Statistical efficiency of Thompson sampling for combinatorial semi-bandits (P. Perrault, E. Boursier, M. Valko, V. Perchet), NeurIPS, 2020.
Covariance-adapting algorithm for semi-bandits with application to sparse outcomes (P. Perrault, M. Valko, V. Perchet), COLT, 2020.
Exploiting structure of uncertainty for efficient matroid semi-bandits (P. Perrault, V. Perchet, M. Valko), ICML, 2019.
Finding the bandit in a graph: Sequential search-and-stop (P. Perrault, V. Perchet, M. Valko), AISTATS, 2019.
Categorized bandits (M. Jedor, V. Perchet, J. Louedec), NeurIPS, 2019.
Bridging the gap between regret minimization and best arm identification, with application to A/B tests (R. Degenne, T. Nedelec, C. Calauzènes, V. Perchet), AISTATS, 2019.
Regularized contextual bandits (X. Fontaine, Q. Berthet, V. Perchet), AISTATS, 2019.
Bandits with side observations: Bounded vs. logarithmic regret (R. Degenne, E. Garcelon, V. Perchet), UAI, 2018.
Stochastic bandit models for delayed conversions (C. Vernade, O. Cappé, V. Perchet), UAI, 2017.
Sparse stochastic bandits (J. Kwon, V. Perchet, C. Vernade), COLT, 2017.
Anytime optimal algorithms in stochastic multi-armed bandits (R. Degenne, V. Perchet), ICML, 2016.
Gains and losses are fundamentally different in regret minimization: The sparse case (J. Kwon, V. Perchet), JMLR, 2016.
Combinatorial semi-bandit with known covariance (R. Degenne, V. Perchet), NeurIPS, 2016.
Batched bandit problems (V. Perchet, P. Rigollet, S. Chassang, E. Snowberg), Annals of Statistics, 2016.
Gaussian process optimization with mutual information (E. Contal, V. Perchet, N. Vayatis), International Conference on Machine Learning, 2014.
Bounded regret in stochastic multi-armed bandits (S. Bubeck, V. Perchet, P. Rigollet), Conference on Learning Theory, 2013.
The multi-armed bandit problem with covariates (V. Perchet, P. Rigollet), The Annals of Statistics, 41(2):693–721, 2013.
Approachability and Partial Monitoring
A differential game on Wasserstein space: application to weak approachability with partial monitoring (V. Perchet, M. Quincampoix), Journal of Dynamics and Games, 6(1):65–85, 2019.
Approachability of convex sets in generalized quitting games (J. Flesch, R. Laraki, V. Perchet), Games and Economic Behaviour, 108:411–431, 2018.
Online learning and Blackwell approachability with partial monitoring: optimal convergence rates (J. Kwon, V. Perchet), Artificial Intelligence and Statistics, 2017.
Exponential weight approachability, applications to calibration and regret minimization (V. Perchet), Dynamic Games and Applications, 5:136–153, 2015.
On a unified framework for approachability with full or partial monitoring (V. Perchet, M. Quincampoix), Mathematics of Operations Research, 40(3):596–610, 2015.
Approachability in unknown games: Online learning meets multi-objective optimization (S. Mannor, V. Perchet, G. Stoltz), Conference on Learning Theory, 2014.
Set-valued approachability and online learning with partial monitoring (S. Mannor, V. Perchet, G. Stoltz), The Journal of Machine Learning Research, 15(1):3247–3295, 2014.
Approachability, fast and slow (S. Mannor, V. Perchet), Conference on Learning Theory, 2013.
A primal condition for approachability with partial monitoring (S. Mannor, V. Perchet, G. Stoltz), Journal of Dynamics and Games, 2–27, 2013.
Approachability of convex sets in games with partial monitoring (V. Perchet), Journal of Optimization Theory and Applications, 149:665–677, 2011.
Internal regret with partial monitoring: Calibration-based optimal algorithms (V. Perchet), Journal of Machine Learning Research, 12(6), 2011.
Calibration and internal no-regret with random signals (V. Perchet), International Conference on Algorithmic Learning Theory, 2009.
Eco - ML
Auctions, Pricings and Mechanisms
Feature-based online bilateral trade (S. Gaucher, M. Bernasconi, M. Castiglioni, A. Celli, V. Perchet), The International Conference on Learning Representations, 2025.
Du-Shapley: A Shapley value proxy for efficient dataset valuation (F. Garrido, B. Heymann, M. Vono, P. Loiseau, V. Perchet), Advances in Neural Information Processing Systems, 2024.
Improved learning rates in multi-unit uniform price auctions (M. Potfer, D. Baudry, H. Richard, V. Perchet, C. Wan), Advances in Neural Information Processing Systems, 2024.
Improved algorithms for contextual dynamic pricing (M. Tullii, S. Gaucher, N. Merlis, V. Perchet), Advances in Neural Information Processing Systems, 2024.
Learning in repeated auctions (T. Nedelec, C. Calauzènes, N. El Karoui, V. Perchet, et al.), Foundations and Trends® in Machine Learning, 15(3):176–334, 2022.
Revenue-maximizing auctions: A bidder’s standpoint (T. Nedelec, C. Calauzènes, V. Perchet, N. El Karoui), Operations Research, 70(5):2767–2783, 2022.
Adversarial learning for revenue-maximizing auctions (T. Nedelec, J. Baudet, V. Perchet, N. El Karoui), 20th International Conference on Autonomous Agents and Multiagent Systems, 2021.
Robust Stackelberg buyers in repeated auctions (T. Nedelec, C. Calauzènes, V. Perchet, N. El Karoui), International Conference on Artificial Intelligence and Statistics, 2020.
Learning to bid in revenue-maximizing auctions (T. Nedelec, N. El Karoui, V. Perchet), International Conference on Machine Learning, 2019.
Dynamic pricing with finitely many unknown valuations (N. Cesa-Bianchi, T. Cesari, V. Perchet), Algorithmic Learning Theory, 2019.
Thresholding the virtual value: a simple method to increase welfare and lower reserve prices in online auction systems (T. Nedelec, M. Abeille, C. Calauzènes, N. El Karoui, B. Heymann, V. Perchet), arXiv preprint arXiv:1808.06979, 2018.
Online learning in repeated auctions (J. Weed, V. Perchet, P. Rigollet), Conference on Learning Theory, 1562–1583, 2016.
Multiplayer, Strategic and Social Learning
Calibrated forecasting and persuasion (A. Jain, V. Perchet), ACM Conference on Economics and Computation, 2024.
Local and adaptive mirror descents in extensive-form games (C. Fiegel, P. Ménard, T. Kozuno, R. Munos, V. Perchet, M. Valko), Local and adaptive mirror descents in extensive-form games, 2024.
Constant or logarithmic regret in asynchronous multiplayer bandits with limited communication (H. Richard, E. Boursier, V. Perchet), International Conference on Artificial Intelligence and Statistics, 2024.
Strategic arms with side communication prevail over low-regret MAB algorithms (A. Ben Yahmed, C. Calauzènes, V. Perchet), IEEE International Conference on Acoustics, Speech and Signal Processing, 2024.
Strategic multi-armed bandit problems under debt-free reporting (A. Ben Yahmed, C. Calauzènes, V. Perchet), Advances in Neural Information Processing Systems, 2024.
A survey on multi-player bandits (E. Boursier, V. Perchet), Journal of Machine Learning Research, 25(137):1–45, 2024.
Utility/privacy trade-off as regularized optimal transport (E. Boursier, V. Perchet), Mathematical Programming, 203(1):703–726, 2024.
Adapting to game trees in zero-sum imperfect information games (C. Fiegel, P. Ménard, T. Kozuno, R. Munos, V. Perchet, M. Valko), International Conference on Machine Learning, 2023.
Social learning in non-stationary environments (E. Boursier, V. Perchet, M. Scarsini), International Conference on Algorithmic Learning Theory, 2022.
A new theoretical framework for fast and accurate online decision-making (N. Cesa-Bianchi, T. Cesari, Y. Mansour, V. Perchet), Advances in Neural Information Processing Systems, 2021.
Decentralized learning in online queuing systems (F. Sentenac, E. Boursier, V. Perchet), Advances in Neural Information Processing Systems, 2021.
A practical algorithm for multiplayer bandits when arm means vary among players (A. Mehrabian, E. Boursier, E. Kaufmann, V. Perchet), International Conference on Artificial Intelligence and Statistics, 2020.
Selfish robustness and equilibria in multi-player bandits (E. Boursier, V. Perchet), Conference on Learning Theory, 2020.
Sic-mmab: Synchronisation involves communication in multiplayer multi-armed bandits (E. Boursier, V. Perchet), Advances in Neural Information Processing Systems, 2019.
Matchings
The price of fairness in bipartite matching (R. Castera, F. Garrido-Lucero, M. Molina, S. Mauras, P. Loiseau, V. Perchet), arXiv preprint arXiv:2403.00397, 2024.
Optimal unimodular matching (N. Enriquez, M. Liu, L. Ménard, V. Perchet), arXiv preprint arXiv:2407.03141, 2024.
Stable matching with ties: Approximation ratios and learning (S. Lin, S. Mauras, N. Merlis, V. Perchet), arXiv preprint arXiv:2411.03270, 2024.
Game Theory, Optimization and Reinforcement Learning
Game Theory
An algorithmic solution to the Blotto game using multi-marginal couplings (V. Perchet, P. Rigollet, T. Le Gouic), Operations Research, 72, 2024.
Finding robust Nash equilibria (V. Perchet), Algorithmic Learning Theory, 2020.
Online learning and game theory: A quick overview with recent results and applications (M. Faure, P. Gaillard, B. Gaujal, V. Perchet), ESAIM: Proceedings and Surveys, 51:246–271, 2015.
A minmax theorem for concave-convex mappings with no regularity assumptions (V. Perchet, G. Vigeral), Journal of Convex Analysis, 22(2):537–540, 2015.
A note on robust Nash equilibria with uncertainties (V. Perchet), RAIRO-Operations Research, 48(3):365–371, 2014.
Optimization and Active Learning
Mode estimation with partial feedback (C. Arnal, V. Cabannes, V. Perchet), Conference on Learning Theory, 2024.
Stochastic mirror descent for large-scale sparse recovery (S. Ilandarideva, Y. Bekri, A. Iouditski, V. Perchet), International Conference on Artificial Intelligence and Statistics, 2023.
Active labeling: streaming stochastic gradients (V. Cabannes, F. Bach, V. Perchet, A. Rudi), Advances in Neural Information Processing Systems, 2022.
Online a-optimal design and active linear regression (X. Fontaine, P. Perrault, M. Valko, V. Perchet), International Conference on Machine Learning, 2021.
An adaptive stochastic optimization algorithm for resource allocation (X. Fontaine, S. Mannor, V. Perchet), Algorithmic Learning Theory, 2020.
Fast rates for bandit optimization with upper-confidence Frank-Wolfe (Q. Berthet, V. Perchet), Advances in Neural Information Processing Systems, 2017.
Highly-smooth zero-th order online optimization (F. Bach, V. Perchet), Conference on Learning Theory, 2016.
Reinforcement Learning
Local differential privacy for regret minimization in reinforcement learning (E. Garcelon, V. Perchet, C. Pike-Burke, M. Pirotta), Advances in Neural Information Processing Systems, 2021.
Quickest change detection for multi-task problems under unknown parameters (F. Jarboui, V. Perchet), arXiv preprint arXiv:2106.05061, 2021.
A generalized inverse reinforcement learning framework (F. Jarboui, V. Perchet), arXiv preprint arXiv:2105.11812, 2021.
Offline inverse reinforcement learning (F. Jarboui, V. Perchet), arXiv preprint arXiv:2106.05068, 2021.
Trajectory representation learning for multi-task NMRDP planning (F. Jarboui, V. Perchet), International Conference on Pattern Recognition, 2021.
Unsupervised neural hidden Markov models with a continuous latent state space (F. Jarboui, V. Perchet), arXiv preprint arXiv:2106.06536, 2021.
Markov decision process for MOOC users behavioral inference (F. Jarboui, C. Gruson-Daniel, A. Durmus, V. Perchet), European MOOCs Stakeholders Summit, 2019.
A comparative study of counterfactual estimators (T. Nedelec, N. Le Roux, V. Perchet), arXiv preprint arXiv:1704.00773, 2017.
Others
Others
Open research challenges for private advertising systems under local differential privacy (M. Tullii, S. Gaucher, H. Richard, E. Diemert, V. Perchet, A. Rakotomamonjy, C. Calauzènes, M. Vono), 2024.
Artificial intelligence. What is it, exactly? (F. Alexandre, C. Bessiere, J.-F Bonnefon, T. Cazenave, R. Chatila, A. Cornuejols, F. Cuppens, S. Destercke, B. Daille, J. Euzenat, J., et al.), 2021.
Robustness of community detection to random geometric perturbations (S. Péché, V. Perchet), Advances in Neural Information Processing Systems, 2020.
L’intelligence artificielle. De quoi s’agit-il vraiment? (F. Alexandre, L. Amgoud, C. Bessiere, J.-F. Bonnefon, T. Cazenave, R. Chatila, A. Cornuejols, F. Cuppens, S. Destercke, B. Daille, et al.), 2020.
Quantitative analysis of dynamic fault trees based on the coupling of structure functions and Monte Carlo simulation (G. Merle, J.-M. Roussel, J.-J. Lesage, V. Perchet, N. Vayatis), Quality and Reliability Engineering International, 32(1):7–18, 2016.
Craft-critical risks analysis by fault trees (P.-Y. Chauy, L. Fribourg, J.-J. Lesage, V. Perchet, J.-M. Roussel, R. Soulat, N.Vayatis), JN–MACS, 2011.