Publications

2020

Journal articles

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Tomáš Kocák, Rémi Munos, Branislav Kveton, Shipra Agrawal, Michal Valko. Spectral bandits. Journal of Machine Learning Research, 2020. ⟨hal-03084249⟩

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2019

Conference papers

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Peter Bartlett, Victor Gabillon, Michal Valko. A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. Algorithmic Learning Theory, 2019, Chicago, United States. ⟨hal-01885368v2⟩

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Xuedong Shang, Emilie Kaufmann, Michal Valko. General parallel optimization without a metric. Algorithmic Learning Theory, 2019, Chicago, United States. ⟨hal-02047225v2⟩

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Theses

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Ronan Fruit. Exploration-exploitation dilemma in Reinforcement Learning under various form of prior knowledge. Artificial Intelligence [cs.AI]. Université de Lille 1, Sciences et Technologies; CRIStAL UMR 9189, 2019. English. ⟨NNT : ⟩. ⟨tel-02388395v2⟩

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2018

Conference papers

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Ronan Fruit, Matteo Pirotta, Alessandro Lazaric. Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes. 32nd Conference on Neural Information Processing Systems, Dec 2018, Montréal, Canada. ⟨hal-01941220⟩

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Xuedong Shang, Emilie Kaufmann, Michal Valko. Adaptive black-box optimization got easier: HCT only needs local smoothness. European Workshop on Reinforcement Learning, Oct 2018, Lille, France. ⟨hal-01874637⟩

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Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli. Stochastic Variance-Reduced Policy Gradient. ICML 2018 – 35th International Conference on Machine Learning, Jul 2018, Stockholm, Sweden. pp.4026-4035. ⟨hal-01940394⟩

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Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner. Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. ICML 2018 – The 35th International Conference on Machine Learning, Jul 2018, Stockholm, Sweden. pp.1578-1586. ⟨hal-01941206⟩

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Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli. Importance Weighted Transfer of Samples in Reinforcement Learning. ICML 2018 – The 35th International Conference on Machine Learning, Jul 2018, Stockholm, Sweden. pp.4936-4945. ⟨hal-01941213⟩

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Yasin Abbasi-Yadkori, Peter Bartlett, Victor Gabillon, Alan Malek, Michal Valko. Best of both worlds: Stochastic & adversarial best-arm identification. Conference on Learning Theory, 2018, Stockholm, Sweden. ⟨hal-01808948v6⟩

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2017

Conference papers

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Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill. Regret Minimization in MDPs with Options without Prior Knowledge. NIPS 2017 – Neural Information Processing Systems, Dec 2017, Long Beach, United States. pp.1-36. ⟨hal-01649082⟩

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Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric. Active Learning for Accurate Estimation of Linear Models. ICML 2017 – 34th International Conference on Machine Learning, Aug 2017, Sydney, Australia. pp.36. ⟨hal-01538762⟩

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Marc Abeille, Alessandro Lazaric. Linear Thompson Sampling Revisited. AISTATS 2017 – 20th International Conference on Artificial Intelligence and Statistics, Apr 2017, Fort Lauderdale, United States. ⟨hal-01493561⟩

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Ronan Fruit, Alessandro Lazaric. Exploration–Exploitation in MDPs with Options. AISTATS 2017 – 20th International Conference on Artificial Intelligence and Statistics, Apr 2017, Fort Lauderdale, United States. ⟨hal-01493567v2⟩

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Marc Abeille, Alessandro Lazaric. Thompson Sampling for Linear-Quadratic Control Problems. AISTATS 2017 – 20th International Conference on Artificial Intelligence and Statistics, Apr 2017, Fort Lauderdale, United States. ⟨hal-01493564⟩

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Daniele Calandriello, Alessandro Lazaric, Michal Valko. Efficient second-order online kernel learning with adaptive embedding. Neural Information Processing Systems, 2017, Long Beach, United States. ⟨hal-01643961⟩

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Daniele Calandriello, Alessandro Lazaric, Michal Valko. Second-Order Kernel Online Convex Optimization with Adaptive Sketching. International Conference on Machine Learning, 2017, Sydney, Australia. ⟨hal-01537799⟩

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Guillaume Gautier, Rémi Bardenet, Michal Valko. Zonotope hit-and-run for efficient sampling from projection DPPs. International Conference on Machine Learning, 2017, Sydney, Australia. ⟨hal-01526577v2⟩

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Daniele Calandriello, Alessandro Lazaric, Michal Valko. Distributed adaptive sampling for kernel matrix approximation. International Conference on Artificial Intelligence and Statistics, 2017, Fort Lauderdale, United States. ⟨hal-01482760⟩

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Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu. Trading off rewards and errors in multi-armed bandits. International Conference on Artificial Intelligence and Statistics, 2017, Fort Lauderdale, United States. ⟨hal-01482765⟩

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2016

Conference papers

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Jean-Bastien Grill, Michal Valko, Rémi Munos. Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning. Neural Information Processing Systems, Dec 2016, Barcelona, Spain. ⟨hal-01389107v3⟩

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Daniele Calandriello, Alessandro Lazaric, Michal Valko. Analysis of Nyström method with sequential ridge leverage score sampling. Uncertainty in Artificial Intelligence, Jun 2016, New York City, United States. ⟨hal-01343674⟩

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Tomáš Kocák, Gergely Neu, Michal Valko. Online learning with Erdős-Rényi side-observation graphs. Uncertainty in Artificial Intelligence, Jun 2016, New York City, United States. ⟨hal-01320588⟩

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Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard. Pliable rejection sampling. International Conference on Machine Learning, Jun 2016, New York City, United States. ⟨hal-01322168⟩

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Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar. Reinforcement Learning of POMDPs using Spectral Methods. Proceedings of the 29th Annual Conference on Learning Theory (COLT2016), Jun 2016, New York City, United States. ⟨hal-01322207⟩

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Tomáš Kocák, Gergely Neu, Michal Valko. Online learning with noisy side observations. International Conference on Artificial Intelligence and Statistics, May 2016, Seville, Spain. ⟨hal-01303377⟩

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Alexandra Carpentier, Michal Valko. Revealing graph bandits for maximizing local influence. International Conference on Artificial Intelligence and Statistics, May 2016, Seville, Spain. ⟨hal-01304020v3⟩

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Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter Bartlett. Improved Learning Complexity in Combinatorial Pure Exploration Bandits. Proceedings of the 19th International Conference on Artificial Intelligence (AISTATS), May 2016, Cadiz, Spain. ⟨hal-01322198⟩

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2015

Conference papers

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Jessica Chemali, Alessandro Lazaric. Direct Policy Iteration with Demonstrations. IJCAI – 24th International Joint Conference on Artificial Intelligence, Jul 2015, Buenos Aires, Argentina. ⟨hal-01237659⟩

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Julien Audiffren, Michal Valko, Alessandro Lazaric, Mohammad Ghavamzadeh. Maximum Entropy Semi-Supervised Inverse Reinforcement Learning. International Joint Conference on Artificial Intelligence, Jul 2015, Bueons Aires, Argentina. ⟨hal-01146187⟩

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Daniele Calandriello, Alessandro Lazaric, Michal Valko. Large-scale semi-supervised learning with online spectral graph sparsification. Resource-Efficient Machine Learning workshop at International Conference on Machine Learning, Jul 2015, Lille, France. ⟨hal-01544929⟩

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Alexandra Carpentier, Michal Valko. Simple regret for infinitely many armed bandits. International Conference on Machine Learning, Jul 2015, Lille, France. ⟨hal-01153538⟩

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Manjesh Kumar Hanawal Hanawal, Venkatesh Saligrama, Michal Valko, Rémi Munos. Cheap Bandits. International Conference on Machine Learning, 2015, Lille, France. ⟨hal-01153540⟩

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Jean-Bastien Grill, Michal Valko, Rémi Munos. Black-box optimization of noisy functions with unknown smoothness. Neural Information Processing Systems, 2015, Montréal, Canada. ⟨hal-01222915v4⟩

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2014

Conference papers

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Daniele Calandriello, Alessandro Lazaric, Marcello Restelli. Sparse Multi-task Reinforcement Learning. NIPS – Advances in Neural Information Processing Systems 26, Dec 2014, Montreal, Canada. ⟨hal-01073513⟩

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Julien Audiffren, Michal Valko, Alessandro Lazaric, Mohammad Ghavamzadeh. MESSI: Maximum Entropy Semi-Supervised Inverse Reinforcement Learning. NIPS Workshop on Novel Trends and Applications in Reinforcement Learning, 2014, Montreal, Canada. ⟨hal-01177446⟩

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