Evidence-based Anomaly Detection in Clinical Domains. - Milos Hauskrecht; Michal Valko; Branislav Kveton; Shyam Visweswaran; Gregory F. Cooper (AMIA, 2007)

Distance Metric Learning for Conditional Anomaly Detection. - Michal Valko; Milos Hauskrecht (FLAIRS, 2008)

Online semi-supervised perception: Real-time learning without explicit feedback. - Branislav Kveton; Matthai Philipose; Michal Valko; Ling Huang (CVPR Workshops, 2010)

Feature importance analysis for patient management decisions. - Michal Valko; Milos Hauskrecht (MedInfo, 2010)

Online Semi-Supervised Learning on Quantized Graphs. - Michal Valko; Branislav Kveton; Ling Huang; Daniel Ting (UAI, 2010)

Semi-Supervised Learning with Max-Margin Graph Cuts. - Branislav Kveton; Michal Valko; Ali Rahimi; Ling Huang (AISTATS, 2010)

Conditional Anomaly Detection with Soft Harmonic Functions. - Michal Valko; Branislav Kveton; Hamed Valizadegan; Gregory F. Cooper; Milos Hauskrecht (ICDM, 2011)

Semi-Supervised Apprenticeship Learning. - Michal Valko; Mohammad Ghavamzadeh; Alessandro Lazaric (EWRL, 2012)

Learning from a single labeled face and a stream of unlabeled data. - Branislav Kveton; Michal Valko (FG, 2013)

Stochastic Simultaneous Optimistic Optimization. - Michal Valko; Alexandra Carpentier; Rémi Munos (ICML (2), 2013)

Finite-Time Analysis of Kernelised Contextual Bandits. - Michal Valko; Nathaniel Korda; Rémi Munos; Ilias N. Flaounas; Nello Cristianini (UAI, 2013)

Outlier detection for patient monitoring and alerting. - Milos Hauskrecht; Iyad Batal; Michal Valko; Shyam Visweswaran; Gregory F. Cooper; Gilles Clermont (J. Biomed. Informatics, 2013)

Spectral Thompson Sampling. - Tomás Kocák; Michal Valko; Rémi Munos; Shipra Agrawal 001 (AAAI, 2014)

Bandits attack function optimization. - Philippe Preux; Rémi Munos; Michal Valko (IEEE Congress on Evolutionary Computation, 2014)

Spectral Bandits for Smooth Graph Functions. - Michal Valko; Rémi Munos; Branislav Kveton; Tomás Kocák (ICML, 2014)

Extreme bandits. - Alexandra Carpentier; Michal Valko (NIPS, 2014)

Efficient learning by implicit exploration in bandit problems with side observations. - Tomás Kocák; Gergely Neu; Michal Valko; Rémi Munos (NIPS, 2014)

Online combinatorial optimization with stochastic decision sets and adversarial losses. - Gergely Neu; Michal Valko (NIPS, 2014)

Learning to Act Greedily: Polymatroid Semi-Bandits. - Branislav Kveton; Zheng Wen; Azin Ashkan; Michal Valko (CoRR, 2014)

Simple regret for infinitely many armed bandits. - Alexandra Carpentier; Michal Valko (ICML, 2015)

Cheap Bandits. - Manjesh Kumar Hanawal; Venkatesh Saligrama; Michal Valko; Rémi Munos (ICML, 2015)

Maximum Entropy Semi-Supervised Inverse Reinforcement Learning. - Julien Audiffren; Michal Valko; Alessandro Lazaric; Mohammad Ghavamzadeh (IJCAI, 2015)

Black-box optimization of noisy functions with unknown smoothness. - Jean-Bastien Grill; Michal Valko; Rémi Munos (NIPS, 2015)

Revealing Graph Bandits for Maximizing Local Influence. - Alexandra Carpentier; Michal Valko (AISTATS, 2016)

Online Learning with Noisy Side Observations. - Tomás Kocák; Gergely Neu; Michal Valko (AISTATS, 2016)

Pliable Rejection Sampling. - Akram Erraqabi; Michal Valko; Alexandra Carpentier; Odalric-Ambrym Maillard (ICML, 2016)

Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning. - Jean-Bastien Grill; Michal Valko; Rémi Munos (NIPS, 2016)

Analysis of Nyström method with sequential ridge leverage scores. - Daniele Calandriello; Alessandro Lazaric; Michal Valko (UAI, 2016)

Online learning with Erdos-Renyi side-observation graphs. - Tomás Kocák; Gergely Neu; Michal Valko (UAI, 2016)

Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting. - Daniele Calandriello; Alessandro Lazaric; Michal Valko (CoRR, 2016)

Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning. - Daniele Calandriello; Alessandro Lazaric; Michal Valko; Ioannis Koutis (CoRR, 2016)

Influence Maximization with Semi-Bandit Feedback. - Zheng Wen; Branislav Kveton; Michal Valko (CoRR, 2016)

Bayesian Policy Gradient and Actor-Critic Algorithms. - Mohammad Ghavamzadeh; Yaakov Engel; Michal Valko (J. Mach. Learn. Res., 2016)

Distributed Adaptive Sampling for Kernel Matrix Approximation. - Daniele Calandriello; Alessandro Lazaric; Michal Valko (AISTATS, 2017)

Trading off Rewards and Errors in Multi-Armed Bandits. - Akram Erraqabi; Alessandro Lazaric; Michal Valko; Emma Brunskill; Yun-En Liu (AISTATS, 2017)

Second-Order Kernel Online Convex Optimization with Adaptive Sketching. - Daniele Calandriello; Alessandro Lazaric; Michal Valko (ICML, 2017)

Zonotope Hit-and-run for Efficient Sampling from Projection DPPs. - Guillaume Gautier; Rémi Bardenet; Michal Valko (ICML, 2017)

Efficient Second-Order Online Kernel Learning with Adaptive Embedding. - Daniele Calandriello; Alessandro Lazaric; Michal Valko (NIPS, 2017)

Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback. - Zheng Wen; Branislav Kveton; Michal Valko; Sharan Vaswani (NIPS, 2017)

Best of both worlds: Stochastic & adversarial best-arm identification. - Yasin Abbasi-Yadkori; Peter L. Bartlett; Victor Gabillon; Alan Malek; Michal Valko (COLT, 2018)

Compressing the Input for CNNs with the First-Order Scattering Transform. - Edouard Oyallon; Eugene Belilovsky; Sergey Zagoruyko; Michal Valko (ECCV (9), 2018)

Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. - Daniele Calandriello; Ioannis Koutis; Alessandro Lazaric; Michal Valko (ICML, 2018)

Preface. - Fabrice Popineau; Michal Valko; Jill-Jênn Vie (ITS Workshops, 2018)

Optimistic optimization of a Brownian. - Jean-Bastien Grill; Michal Valko; Rémi Munos (NeurIPS, 2018)

DPPy: Sampling Determinantal Point Processes with Python. - Guillaume Gautier; Rémi Bardenet; Michal Valko (CoRR, 2018)

Active multiple matrix completion with adaptive confidence sets. - Andrea Locatelli; Alexandra Carpentier; Michal Valko (AISTATS, 2019)

Finding the bandit in a graph: Sequential search-and-stop. - Pierre Perrault; Vianney Perchet; Michal Valko (AISTATS, 2019)

Rotting bandits are no harder than stochastic ones. - Julien Seznec; Andrea Locatelli; Alexandra Carpentier; Alessandro Lazaric; Michal Valko (AISTATS, 2019)

A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. - Peter L. Bartlett; Victor Gabillon; Michal Valko (ALT, 2019)

General parallel optimization a without metric. - Xuedong Shang; Emilie Kaufmann; Michal Valko (ALT, 2019)

Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. - Daniele Calandriello; Luigi Carratino; Alessandro Lazaric; Michal Valko; Lorenzo Rosasco (COLT, 2019)

Scale-free adaptive planning for deterministic dynamics & discounted rewards. - Peter L. Bartlett; Victor Gabillon; Jennifer Healey; Michal Valko (ICML, 2019)

Exploiting structure of uncertainty for efficient matroid semi-bandits. - Pierre Perrault; Vianney Perchet; Michal Valko (ICML, 2019)

Exact sampling of determinantal point processes with sublinear time preprocessing. - Michal Derezinski; Daniele Calandriello; Michal Valko (NeurIPS, 2019)

On two ways to use determinantal point processes for Monte Carlo integration. - Guillaume Gautier; Rémi Bardenet; Michal Valko (NeurIPS, 2019)

Planning in entropy-regularized Markov decision processes and games. - Jean-Bastien Grill; Omar Darwiche Domingues; Pierre Ménard; Rémi Munos; Michal Valko (NeurIPS, 2019)

Multiagent Evaluation under Incomplete Information. - Mark Rowland 001; Shayegan Omidshafiei; Karl Tuyls; Julien Pérolat; Michal Valko; Georgios Piliouras; Rémi Munos (NeurIPS, 2019)

Exploiting Structure of Uncertainty for Efficient Combinatorial Semi-Bandits. - Pierre Perrault; Vianney Perchet; Michal Valko (CoRR, 2019)

DPPy: DPP Sampling with Python. - Guillaume Gautier; Guillermo Polito; Rémi Bardenet; Michal Valko (J. Mach. Learn. Res., 2019)

Derivative-Free & Order-Robust Optimisation. - Haitham Ammar; Victor Gabillon; Rasul Tutunov; Michal Valko (AISTATS, 2020)

Adaptive multi-fidelity optimization with fast learning rates. - Côme Fiegel; Victor Gabillon; Michal Valko (AISTATS, 2020)

A single algorithm for both restless and rested rotting bandits. - Julien Seznec; Pierre Ménard; Alessandro Lazaric; Michal Valko (AISTATS, 2020)

Fixed-confidence guarantees for Bayesian best-arm identification. - Xuedong Shang; Rianne de Heide; Pierre Ménard; Emilie Kaufmann; Michal Valko (AISTATS, 2020)

Covariance-adapting algorithm for semi-bandits with application to sparse outcomes. - Pierre Perrault; Michal Valko; Vianney Perchet (COLT, 2020)

Near-linear time Gaussian process optimization with adaptive batching and resparsification. - Daniele Calandriello; Luigi Carratino; Alessandro Lazaric; Michal Valko; Lorenzo Rosasco (ICML, 2020)

Gamification of Pure Exploration for Linear Bandits. - Rémy Degenne; Pierre Ménard; Xuedong Shang; Michal Valko (ICML, 2020)

Monte-Carlo Tree Search as Regularized Policy Optimization. - Jean-Bastien Grill; Florent Altché; Yunhao Tang; Thomas Hubert; Michal Valko; Ioannis Antonoglou; Rémi Munos (ICML, 2020)

Stochastic bandits with arm-dependent delays. - Anne Gael Manegueu; Claire Vernade; Alexandra Carpentier; Michal Valko (ICML, 2020)

Budgeted Online Influence Maximization. - Pierre Perrault; Jennifer Healey; Zheng Wen; Michal Valko (ICML, 2020)

Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards. - Aadirupa Saha; Pierre Gaillard; Michal Valko (ICML, 2020)

Taylor Expansion Policy Optimization. - Yunhao Tang; Michal Valko; Rémi Munos (ICML, 2020)

No-Regret Exploration in Goal-Oriented Reinforcement Learning. - Jean Tarbouriech; Evrard Garcelon; Michal Valko; Matteo Pirotta; Alessandro Lazaric (ICML, 2020)

Sampling from a k-DPP without looking at all items. - Daniele Calandriello; Michal Derezinski; Michal Valko (NeurIPS, 2020)

Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. - Jean-Bastien Grill; Florian Strub; Florent Altché; Corentin Tallec; Pierre H. Richemond; Elena Buchatskaya; Carl Doersch; Bernardo Ávila Pires; Zhaohan Guo; Mohammad Gheshlaghi Azar; Bilal Piot; Koray Kavukcuoglu; et al. (NeurIPS, 2020)

Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. - Anders Jonsson 001; Emilie Kaufmann; Pierre Ménard; Omar Darwiche Domingues; Edouard Leurent; Michal Valko (NeurIPS, 2020)

Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits. - Pierre Perrault; Etienne Boursier; Michal Valko; Vianney Perchet (NeurIPS, 2020)

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. - Jean Tarbouriech; Matteo Pirotta; Michal Valko; Alessandro Lazaric (NeurIPS, 2020)

Regret Bounds for Kernel-Based Reinforcement Learning. - Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Emilie Kaufmann; Michal Valko (CoRR, 2020)

Improved Sleeping Bandits with Stochastic Actions Sets and Adversarial Rewards. - Aadirupa Saha; Pierre Gaillard; Michal Valko (CoRR, 2020)

BYOL works even without batch statistics. - Pierre H. Richemond; Jean-Bastien Grill; Florent Altché; Corentin Tallec; Florian Strub; Andrew Brock; Samuel L. Smith; Soham De; Razvan Pascanu; Bilal Piot; Michal Valko (CoRR, 2020)

A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. - Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Emilie Kaufmann; Michal Valko (AISTATS, 2021)

Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. - Omar Darwiche Domingues; Pierre Ménard; Emilie Kaufmann; Michal Valko (ALT, 2021)

Adaptive Reward-Free Exploration. - Emilie Kaufmann; Pierre Ménard; Omar Darwiche Domingues; Anders Jonsson 001; Edouard Leurent; Michal Valko (ALT, 2021)

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. - Jean Tarbouriech; Matteo Pirotta; Michal Valko; Alessandro Lazaric (ALT, 2021)

Broaden Your Views for Self-Supervised Video Learning. - Adrià Recasens; Pauline Luc; Jean-Baptiste Alayrac; Luyu Wang; Florian Strub; Corentin Tallec; Mateusz Malinowski; Viorica Patraucean; Florent Altché; Michal Valko; Jean-Bastien Grill; Aäron van den Oord; et al. (ICCV, 2021)

Kernel-Based Reinforcement Learning: A Finite-Time Analysis. - Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Emilie Kaufmann; Michal Valko (ICML, 2021)

Online A-Optimal Design and Active Linear Regression. - Xavier Fontaine; Pierre Perrault; Michal Valko; Vianney Perchet (ICML, 2021)

Revisiting Peng's Q(λ) for Modern Reinforcement Learning. - Tadashi Kozuno; Yunhao Tang; Mark Rowland 001; Rémi Munos; Steven Kapturowski; Will Dabney; Michal Valko; David Abel (ICML, 2021)

Fast active learning for pure exploration in reinforcement learning. - Pierre Ménard; Omar Darwiche Domingues; Anders Jonsson 001; Emilie Kaufmann; Edouard Leurent; Michal Valko (ICML, 2021)

UCB Momentum Q-learning: Correcting the bias without forgetting. - Pierre Ménard; Omar Darwiche Domingues; Xuedong Shang; Michal Valko (ICML, 2021)

Taylor Expansion of Discount Factors. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Michal Valko (ICML, 2021)

Learning in two-player zero-sum partially observable Markov games with perfect recall. - Tadashi Kozuno; Pierre Ménard; Rémi Munos; Michal Valko (NeurIPS, 2021)

Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. - Ran Liu; Mehdi Azabou; Max Dabagia; Chi-Heng Lin; Mohammad Gheshlaghi Azar; Keith B. Hengen; Michal Valko; Eva L. Dyer (NeurIPS, 2021)

Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Rémi Munos; Michal Valko (NeurIPS, 2021)

A Provably Efficient Sample Collection Strategy for Reinforcement Learning. - Jean Tarbouriech; Matteo Pirotta; Michal Valko; Alessandro Lazaric (NeurIPS, 2021)

Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. - Jean Tarbouriech; Runlong Zhou; Simon S. Du; Matteo Pirotta; Michal Valko; Alessandro Lazaric (NeurIPS, 2021)

On the Approximation Relationship between Optimizing Ratio of Submodular (RS) and Difference of Submodular (DS) Functions. - Pierre Perrault; Jennifer Healey; Zheng Wen; Michal Valko (CoRR, 2021)

Geometric Entropic Exploration. - Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Alaa Saade; Shantanu Thakoor; Bilal Piot; Bernardo Ávila Pires; Michal Valko; Thomas Mesnard; Tor Lattimore; Rémi Munos (CoRR, 2021)

Bootstrapped Representation Learning on Graphs. - Shantanu Thakoor; Corentin Tallec; Mohammad Gheshlaghi Azar; Rémi Munos; Petar Velickovic; Michal Valko (CoRR, 2021)

Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction. - Mehdi Azabou; Mohammad Gheshlaghi Azar; Ran Liu; Chi-Heng Lin; Erik C. Johnson; Kiran Bhaskaran-Nair; Max Dabagia; Keith B. Hengen; William R. Gray Roncal; Michal Valko; Eva L. Dyer (CoRR, 2021)

Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall. - Tadashi Kozuno; Pierre Ménard; Rémi Munos; Michal Valko (CoRR, 2021)

Game Plan: What AI can do for Football, and What Football can do for AI. - Karl Tuyls; Shayegan Omidshafiei; Paul Muller; Zhe Wang 055; Jerome T. Connor; Daniel Hennes; Ian Graham; William Spearman; Tim Waskett; Dafydd Steele; Pauline Luc; Adrià Recasens; et al. (J. Artif. Intell. Res., 2021)

Fast sampling from β-ensembles. - Guillaume Gautier; Rémi Bardenet; Michal Valko (Stat. Comput., 2021)

Marginalized Operators for Off-policy Reinforcement Learning. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Michal Valko (AISTATS, 2022)

Adaptive Multi-Goal Exploration. - Jean Tarbouriech; Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Michal Valko; Alessandro Lazaric (AISTATS, 2022)

Large-Scale Representation Learning on Graphs via Bootstrapping. - Shantanu Thakoor; Corentin Tallec; Mohammad Gheshlaghi Azar; Mehdi Azabou; Eva L. Dyer; Rémi Munos; Petar Velickovic; Michal Valko (ICLR, 2022)

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. - Daniele Calandriello; Luigi Carratino; Alessandro Lazaric; Michal Valko; Lorenzo Rosasco (ICML, 2022)

Retrieval-Augmented Reinforcement Learning. - Anirudh Goyal; Abram L. Friesen; Andrea Banino; Theophane Weber; Nan Rosemary Ke; Adrià Puigdomènech Badia; Arthur Guez; Mehdi Mirza; Peter Conway Humphreys; Ksenia Konyushkova; Michal Valko; Simon Osindero; et al. (ICML, 2022)

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. - Daniil Tiapkin; Denis Belomestny; Eric Moulines; Alexey Naumov; Sergey Samsonov; Yunhao Tang; Michal Valko; Pierre Ménard (ICML, 2022)

BYOL-Explore: Exploration by Bootstrapped Prediction. - Zhaohan Guo; Shantanu Thakoor; Miruna Pislar; Bernardo Ávila Pires; Florent Altché; Corentin Tallec; Alaa Saade; Daniele Calandriello; Jean-Bastien Grill; Yunhao Tang; Michal Valko; Rémi Munos; et al. (NeurIPS, 2022)

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Mark Rowland 001; Michal Valko; Pierre Ménard (NeurIPS, 2022)

Retrieval-Augmented Reinforcement Learning. - Anirudh Goyal; Abram L. Friesen; Andrea Banino; Theophane Weber; Nan Rosemary Ke; Adrià Puigdomènech Badia; Arthur Guez; Mehdi Mirza; Ksenia Konyushkova; Michal Valko; Simon Osindero; Timothy P. Lillicrap; et al. (CoRR, 2022)

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. - Tadashi Kozuno; Wenhao Yang; Nino Vieillard; Toshinori Kitamura; Yunhao Tang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Michal Valko; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (CoRR, 2022)

Curiosity in hindsight. - Daniel Jarrett; Corentin Tallec; Florent Altché; Thomas Mesnard; Rémi Munos; Michal Valko (CoRR, 2022)

Half-Hop: A graph upsampling approach for slowing down message passing. - Mehdi Azabou; Venkataramana Ganesh; Shantanu Thakoor; Chi-Heng Lin; Lakshmi Sathidevi; Ran Liu; Michal Valko; Petar Velickovic; Eva L. Dyer (ICML, 2023)

Adapting to game trees in zero-sum imperfect information games. - Côme Fiegel; Pierre Ménard; Tadashi Kozuno; Rémi Munos; Vianney Perchet; Michal Valko (ICML, 2023)

Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. - Daniel Jarrett; Corentin Tallec; Florent Altché; Thomas Mesnard; Rémi Munos; Michal Valko (ICML, 2023)

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. - Toshinori Kitamura; Tadashi Kozuno; Yunhao Tang; Nino Vieillard; Michal Valko; Wenhao Yang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (ICML, 2023)

Quantile Credit Assignment. - Thomas Mesnard; Wenqi Chen; Alaa Saade; Yunhao Tang; Mark Rowland 001; Theophane Weber; Clare Lyle; Audrunas Gruslys; Michal Valko; Will Dabney; Georg Ostrovski; Eric Moulines; et al. (ICML, 2023)

Understanding Self-Predictive Learning for Reinforcement Learning. - Yunhao Tang; Zhaohan Daniel Guo; Pierre Harvey Richemond; Bernardo Ávila Pires; Yash Chandak; Rémi Munos; Mark Rowland 001; Mohammad Gheshlaghi Azar; Charline Le Lan; Clare Lyle; András György 001; Shantanu Thakoor; et al. (ICML, 2023)

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Anna Harutyunyan; Rémi Munos; Bernardo Ávila Pires; Michal Valko (ICML, 2023)

VA-learning as a more efficient alternative to Q-learning. - Yunhao Tang; Rémi Munos; Mark Rowland 001; Michal Valko (ICML, 2023)

Fast Rates for Maximum Entropy Exploration. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Pierre Perrault; Yunhao Tang; Michal Valko; Pierre Ménard (ICML, 2023)

Model-free Posterior Sampling via Learning Rate Randomization. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Ménard (NeurIPS, 2023)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Zhaohan Daniel Guo; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Andrea Michi; Marco Selvi; Sertan Girgin; et al. (CoRR, 2023)

A General Theoretical Paradigm to Understand Learning from Human Preferences. - Mohammad Gheshlaghi Azar; Zhaohan Daniel Guo; Bilal Piot; Rémi Munos; Mark Rowland 001; Michal Valko; Daniele Calandriello (AISTATS, 2024)

Patchhealer: Counterfactual Image Segment Transplants with chest X-ray domain check. - Hakan Lane; Michal Valko; Veda Sahaja Bandi; Nandini Lokesh Reddy; Stefan Kramer (BMVC Workshops, 2024)

CardioCNN: Gamification of counterfactual image transplants. - Hakan Lane; Michal Valko; Stefan Kramer (BMVC Workshops, 2024)

Unlocking the Power of Representations in Long-term Novelty-based Exploration. - Alaa Saade; Steven Kapturowski; Daniele Calandriello; Charles Blundell; Pablo Sprechmann; Leopoldo Sarra; Oliver Groth; Michal Valko; Bilal Piot (ICLR, 2024)

Demonstration-Regularized RL. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Ménard (ICLR, 2024)

Human Alignment of Large Language Models through Online Preference Optimisation. - Daniele Calandriello; Zhaohan Daniel Guo; Rémi Munos; Mark Rowland 001; Yunhao Tang; Bernardo Ávila Pires; Pierre Harvey Richemond; Charline Le Lan; Michal Valko; Tianqi Liu 002; Rishabh Joshi; Zeyu Zheng; et al. (ICML, 2024)

Decoding-time Realignment of Language Models. - Tianlin Liu; Shangmin Guo; Leonardo Bianco; Daniele Calandriello; Quentin Berthet; Felipe Llinares-López; Jessica Hoffmann; Lucas Dixon; Michal Valko; Mathieu Blondel (ICML, 2024)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Daniel Guo 001; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Côme Fiegel; Andrea Michi; Marco Selvi; et al. (ICML, 2024)

Generalized Preference Optimization: A Unified Approach to Offline Alignment. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Rémi Munos; Mark Rowland 001; Pierre Harvey Richemond; Michal Valko; Bernardo Ávila Pires; Bilal Piot (ICML, 2024)

Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. - Aniket Didolkar; Anirudh Goyal; Nan Rosemary Ke; Siyuan Guo; Michal Valko; Timothy P. Lillicrap; Danilo Jimenez Rezende; Yoshua Bengio; Michael C. Mozer; Sanjeev Arora (NeurIPS, 2024)

Local and Adaptive Mirror Descents in Extensive-Form Games. - Côme Fiegel; Pierre Ménard; Tadashi Kozuno; Rémi Munos; Vianney Perchet; Michal Valko (NeurIPS, 2024)

Understanding the performance gap between online and offline alignment algorithms. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Yuan Cao; Eugene Tarassov; Rémi Munos; Bernardo Ávila Pires; Michal Valko; Yong Cheng; Will Dabney (CoRR, 2024)

A New Bound on the Cumulant Generating Function of Dirichlet Processes. - Pierre Perrault; Denis Belomestny; Pierre Ménard; Éric Moulines; Alexey Naumov; Daniil Tiapkin; Michal Valko (CoRR, 2024)

Preference Optimization with Multi-Sample Comparisons. - Chaoqi Wang; Zhuokai Zhao; Chen Zhu; Karthik Abinav Sankararaman; Michal Valko; Xuefei Cao; Zhaorun Chen; Madian Khabsa; Yuxin Chen; Hao Ma 001; Sinong Wang (CoRR, 2024)

Optimal Design for Reward Modeling in RLHF. - Antoine Scheid; Etienne Boursier; Alain Durmus; Michael I. Jordan; Pierre Ménard; Eric Moulines; Michal Valko (CoRR, 2024)

RL-finetuning LLMs from on- and off-policy data with a single algorithm. - Yunhao Tang; Taco Cohen; David W. Zhang; Michal Valko; Rémi Munos (CoRR, 2025)

Accelerating Nash Learning from Human Feedback via Mirror Prox. - Daniil Tiapkin; Daniele Calandriello; Denis Belomestny; Eric Moulines; Alexey Naumov; Kashif Rasul; Michal Valko; Pierre Ménard (CoRR, 2025)