Michal Valko
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
83
Venues
17
Active years
2007–2025
Best venue rank
A*
Where they publish
Papers
83 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICML | The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback. | Cme Fiegel, Pierre Mnard, Tadashi Kozuno, Michal Valko, Vianney Perchet |
| 2024 | AISTATS | A General Theoretical Paradigm to Understand Learning from Human Preferences. | Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rmi Munos, Mark Rowland, Michal Valko, Daniele Calandriello |
| 2024 | BMVC | Patchhealer: Counterfactual Image Segment Transplants with chest X-ray domain check. | Hakan Lane, Michal Valko, Veda Sahaja Bandi, Nandini Lokesh Reddy, Stefan Kramer |
| 2024 | BMVC | CardioCNN: Gamification of counterfactual image transplants. | Hakan Lane, Michal Valko, Stefan Kramer |
| 2024 | ICLR | 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 |
| 2024 | ICLR | Demonstration-Regularized RL. | Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Mnard |
| 2024 | ICML | Human Alignment of Large Language Models through Online Preference Optimisation. | Daniele Calandriello, Zhaohan Daniel Guo, Rmi Munos, Mark Rowland, Yunhao Tang, Bernardo vila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot |
| 2024 | ICML | Decoding-time Realignment of Language Models. | Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-Lpez, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel |
| 2024 | ICML | Nash Learning from Human Feedback. | Rmi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Cme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot |
| 2024 | ICML | Generalized Preference Optimization: A Unified Approach to Offline Alignment. | Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rmi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo vila Pires, Bilal Piot |
| 2023 | ICML | 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 |
| 2023 | ICML | Adapting to game trees in zero-sum imperfect information games. | Cme Fiegel, Pierre Mnard, Tadashi Kozuno, Rmi Munos, Vianney Perchet, Michal Valko |
| 2023 | ICML | Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. | Daniel Jarrett, Corentin Tallec, Florent Altch, Thomas Mesnard, Rmi Munos, Michal Valko |
| 2023 | ICML | 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 Mnard, Mohammad Gheshlaghi Azar, Rmi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvri, Wataru Kumagai, Yutaka Matsuo |
| 2023 | ICML | Quantile Credit Assignment. | Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rmi Munos |
| 2023 | ICML | Understanding Self-Predictive Learning for Reinforcement Learning. | Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo vila Pires, Yash Chandak, Rmi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, Andrs Gyrgy, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko |
| 2023 | ICML | DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. | Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rmi Munos, Bernardo vila Pires, Michal Valko |
| 2023 | ICML | VA-learning as a more efficient alternative to Q-learning. | Yunhao Tang, Rmi Munos, Mark Rowland, Michal Valko |
| 2023 | ICML | Fast Rates for Maximum Entropy Exploration. | Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rmi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Mnard |
| 2022 | AISTATS | Marginalized Operators for Off-policy Reinforcement Learning. | Yunhao Tang, Mark Rowland, Rmi Munos, Michal Valko |
| 2022 | AISTATS | Adaptive Multi-Goal Exploration. | Jean Tarbouriech, Omar Darwiche Domingues, Pierre Mnard, Matteo Pirotta, Michal Valko, Alessandro Lazaric |
| 2022 | ICLR | Large-Scale Representation Learning on Graphs via Bootstrapping. | Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rmi Munos, Petar Velickovic, Michal Valko |
| 2022 | ICML | Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. | Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco |
| 2022 | ICML | Retrieval-Augmented Reinforcement Learning. | Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adri Puigdomnech Badia, Arthur Guez, Mehdi Mirza, Peter Conway Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell |
| 2022 | ICML | 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 Mnard |
| 2021 | AISTATS | A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. | Omar Darwiche Domingues, Pierre Mnard, Matteo Pirotta, Emilie Kaufmann, Michal Valko |
| 2021 | ALT | Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. | Omar Darwiche Domingues, Pierre Mnard, Emilie Kaufmann, Michal Valko |
| 2021 | ALT | Adaptive Reward-Free Exploration. | Emilie Kaufmann, Pierre Mnard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko |
| 2021 | ALT | Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. | Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric |
| 2021 | ICCV | Broaden Your Views for Self-Supervised Video Learning. | Adri Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Patraucean, Florent Altch, Michal Valko, Jean-Bastien Grill, Aron van den Oord, Andrew Zisserman |
| 2021 | ICML | Kernel-Based Reinforcement Learning: A Finite-Time Analysis. | Omar Darwiche Domingues, Pierre Mnard, Matteo Pirotta, Emilie Kaufmann, Michal Valko |
| 2021 | ICML | Online A-Optimal Design and Active Linear Regression. | Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet |
| 2021 | ICML | Revisiting Peng's Q(λ) for Modern Reinforcement Learning. | Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rmi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel |
| 2021 | ICML | Fast active learning for pure exploration in reinforcement learning. | Pierre Mnard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko |
| 2021 | ICML | UCB Momentum Q-learning: Correcting the bias without forgetting. | Pierre Mnard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko |
| 2021 | ICML | Taylor Expansion of Discount Factors. | Yunhao Tang, Mark Rowland, Rmi Munos, Michal Valko |
| 2020 | AISTATS | Derivative-Free & Order-Robust Optimisation. | Haitham Ammar, Victor Gabillon, Rasul Tutunov, Michal Valko |
| 2020 | AISTATS | Adaptive multi-fidelity optimization with fast learning rates. | Cme Fiegel, Victor Gabillon, Michal Valko |
| 2020 | AISTATS | A single algorithm for both restless and rested rotting bandits. | Julien Seznec, Pierre Mnard, Alessandro Lazaric, Michal Valko |
| 2020 | AISTATS | Fixed-confidence guarantees for Bayesian best-arm identification. | Xuedong Shang, Rianne de Heide, Pierre Mnard, Emilie Kaufmann, Michal Valko |
| 2020 | COLT | Covariance-adapting algorithm for semi-bandits with application to sparse outcomes. | Pierre Perrault, Michal Valko, Vianney Perchet |
| 2020 | ICML | Near-linear time Gaussian process optimization with adaptive batching and resparsification. | Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco |
| 2020 | ICML | Gamification of Pure Exploration for Linear Bandits. | Rmy Degenne, Pierre Mnard, Xuedong Shang, Michal Valko |
| 2020 | ICML | Monte-Carlo Tree Search as Regularized Policy Optimization. | Jean-Bastien Grill, Florent Altch, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rmi Munos |
| 2020 | ICML | Stochastic bandits with arm-dependent delays. | Anne Gael Manegueu, Claire Vernade, Alexandra Carpentier, Michal Valko |
| 2020 | ICML | Budgeted Online Influence Maximization. | Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko |
| 2020 | ICML | Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards. | Aadirupa Saha, Pierre Gaillard, Michal Valko |
| 2020 | ICML | Taylor Expansion Policy Optimization. | Yunhao Tang, Michal Valko, Rmi Munos |
| 2020 | ICML | No-Regret Exploration in Goal-Oriented Reinforcement Learning. | Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric |
| 2019 | AISTATS | Active multiple matrix completion with adaptive confidence sets. | Andrea Locatelli, Alexandra Carpentier, Michal Valko |
| 2019 | AISTATS | Finding the bandit in a graph: Sequential search-and-stop. | Pierre Perrault, Vianney Perchet, Michal Valko |
| 2019 | AISTATS | Rotting bandits are no harder than stochastic ones. | Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko |
| 2019 | ALT | A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. | Peter L. Bartlett, Victor Gabillon, Michal Valko |
| 2019 | ALT | General parallel optimization a without metric. | Xuedong Shang, Emilie Kaufmann, Michal Valko |
| 2019 | COLT | Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. | Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco |
| 2019 | ICML | Scale-free adaptive planning for deterministic dynamics & discounted rewards. | Peter L. Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko |
| 2019 | ICML | Exploiting structure of uncertainty for efficient matroid semi-bandits. | Pierre Perrault, Vianney Perchet, Michal Valko |
| 2018 | COLT | Best of both worlds: Stochastic & adversarial best-arm identification. | Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek, Michal Valko |
| 2018 | ECCV | Compressing the Input for CNNs with the First-Order Scattering Transform. | Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko |
| 2018 | ICML | Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. | Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko |
| 2018 | ITS | Preface. | Fabrice Popineau, Michal Valko, Jill-Jnn Vie |
| 2017 | AISTATS | Distributed Adaptive Sampling for Kernel Matrix Approximation. | Daniele Calandriello, Alessandro Lazaric, Michal Valko |
| 2017 | AISTATS | Trading off Rewards and Errors in Multi-Armed Bandits. | Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu |
| 2017 | ICML | Second-Order Kernel Online Convex Optimization with Adaptive Sketching. | Daniele Calandriello, Alessandro Lazaric, Michal Valko |
| 2017 | ICML | Zonotope Hit-and-run for Efficient Sampling from Projection DPPs. | Guillaume Gautier, Rmi Bardenet, Michal Valko |
| 2016 | AISTATS | Revealing Graph Bandits for Maximizing Local Influence. | Alexandra Carpentier, Michal Valko |
| 2016 | AISTATS | Online Learning with Noisy Side Observations. | Toms Kock, Gergely Neu, Michal Valko |
| 2016 | ICML | Pliable Rejection Sampling. | Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard |
| 2016 | UAI | Analysis of Nystrm method with sequential ridge leverage scores. | Daniele Calandriello, Alessandro Lazaric, Michal Valko |
| 2016 | UAI | Online learning with Erdos-Renyi side-observation graphs. | Toms Kock, Gergely Neu, Michal Valko |
| 2015 | ICML | Simple regret for infinitely many armed bandits. | Alexandra Carpentier, Michal Valko |
| 2015 | ICML | Cheap Bandits. | Manjesh Kumar Hanawal, Venkatesh Saligrama, Michal Valko, Rmi Munos |
| 2015 | IJCAI | Maximum Entropy Semi-Supervised Inverse Reinforcement Learning. | Julien Audiffren, Michal Valko, Alessandro Lazaric, Mohammad Ghavamzadeh |
| 2014 | AAAI | Spectral Thompson Sampling. | Toms Kock, Michal Valko, Rmi Munos, Shipra Agrawal |
| 2014 | CEC | Bandits attack function optimization. | Philippe Preux, Rmi Munos, Michal Valko |
| 2014 | ICML | Spectral Bandits for Smooth Graph Functions. | Michal Valko, Rmi Munos, Branislav Kveton, Toms Kock |
| 2013 | ICML | Stochastic Simultaneous Optimistic Optimization. | Michal Valko, Alexandra Carpentier, Rmi Munos |
| 2013 | UAI | Finite-Time Analysis of Kernelised Contextual Bandits. | Michal Valko, Nathaniel Korda, Rmi Munos, Ilias N. Flaounas, Nello Cristianini |
| 2011 | ICDM | Conditional Anomaly Detection with Soft Harmonic Functions. | Michal Valko, Branislav Kveton, Hamed Valizadegan, Gregory F. Cooper, Milos Hauskrecht |
| 2010 | CVPR | Online semi-supervised perception: Real-time learning without explicit feedback. | Branislav Kveton, Matthai Philipose, Michal Valko, Ling Huang |
| 2010 | UAI | Online Semi-Supervised Learning on Quantized Graphs. | Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting |
| 2008 | FlAIRS | Distance Metric Learning for Conditional Anomaly Detection. | Michal Valko, Milos Hauskrecht |
| 2007 | AMIA | Evidence-based Anomaly Detection in Clinical Domains. | Milos Hauskrecht, Michal Valko, Branislav Kveton, Shyam Visweswaran, Gregory F. Cooper |