Alessandro Lazaric

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 132

Top Venues

Publications

Arpit Agarwal, Nicolas Usunier, Alessandro Lazaric, Maximilian Nickel
System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes. CoRR (2024)
Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, Yann Ollivier
Fast Imitation via Behavior Foundation Models. ICLR (2024)
Arpit Agarwal, Nicolas Usunier, Alessandro Lazaric, Maximilian Nickel
System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes. FAccT (2024)
Ayoub Ghriss, Masashi Sugiyama, Alessandro Lazaric
Reinforcement Learning with Options and State Representation. CoRR (2024)
Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati
Simple Ingredients for Offline Reinforcement Learning. CoRR (2024)
Rui Yuan, Simon Shaolei Du, Robert M. Gower, Alessandro Lazaric, Lin Xiao
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. ICLR (2023)
Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier
Contextual bandits with concave rewards, and an application to fair ranking. ICLR (2023)
Harsh Satija, Alessandro Lazaric, Matteo Pirotta, Joelle Pineau
Group Fairness in Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)
Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path. ALT (2023)
Lina Mezghani, Sainbayar Sukhbaatar, Piotr Bojanowski, Alessandro Lazaric, Karteek Alahari
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping. CoRR (2023)
Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta
Layered State Discovery for Incremental Autonomous Exploration. ICML (2023)
Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
On the Complexity of Representation Learning in Contextual Linear Bandits. AISTATS (2023)
Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta
Layered State Discovery for Incremental Autonomous Exploration. CoRR (2023)
Lina Mezghani, Sainbayar Sukhbaatar, Piotr Bojanowski, Alessandro Lazaric, Karteek Alahari
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping. CoRL (2022)
Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning. ICLR (2022)
Akram Erraqabi, Marlos C. Machado, Mingde Zhao, Sainbayar Sukhbaatar, Alessandro Lazaric, Ludovic Denoyer, Yoshua Bengio
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL. CoRR (2022)
Rui Yuan, Simon S. Du, Robert M. Gower, Alessandro Lazaric, Lin Xiao
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. CoRR (2022)
Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier
Contextual bandits with concave rewards, and an application to fair ranking. CoRR (2022)
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Adaptive Multi-Goal Exploration. AISTATS (2022)
Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. NeurIPS (2022)
Rui Yuan, Robert M. Gower, Alessandro Lazaric
A general sample complexity analysis of vanilla policy gradient. AISTATS (2022)
Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, Lerrel Pinto
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning. CoRR (2022)
Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon Shaolei Du
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning. ICLR (2022)
Pierre-Alexandre Kamienny, Jean Tarbouriech, Sylvain Lamprier, Alessandro Lazaric, Ludovic Denoyer
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching. ICLR (2022)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. CoRR (2022)
Akram Erraqabi, Marlos C. Machado, Mingde Zhao, Sainbayar Sukhbaatar, Alessandro Lazaric, Ludovic Denoyer, Yoshua Bengio
Temporal abstractions-augmented temporally contrastive learning: An alternative to the Laplacian in RL. UAI (2022)
Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. CoRR (2022)
Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path. CoRR (2022)
Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. AISTATS (2022)
Rui Yuan, Alessandro Lazaric, Robert M. Gower
Sketched Newton-Raphson. SIAM J. Optim. 32 (3) (2022)
Yifang Chen, Karthik Abinav Sankararaman, Alessandro Lazaric, Matteo Pirotta, Dmytro Karamshuk, Qifan Wang, Karishma Mandyam, Sinong Wang, Han Fang
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler. CoRR (2022)
Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
On the Complexity of Representation Learning in Contextual Linear Bandits. CoRR (2022)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. ICML (2022)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. ALT (2021)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
A Provably Efficient Sample Collection Strategy for Reinforcement Learning. NeurIPS (2021)
Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. NeurIPS (2021)
Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon S. Du
A Unified Framework for Conservative Exploration. CoRR (2021)
Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Leveraging Good Representations in Linear Contextual Bandits. CoRR (2021)
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Adaptive Multi-Goal Exploration. CoRR (2021)
Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto
Reinforcement Learning with Prototypical Representations. ICML (2021)
Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. CoRR (2021)
Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. CoRR (2021)
Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. NeurIPS (2021)
Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto
Reinforcement Learning with Prototypical Representations. CoRR (2021)
Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs. CoRR (2021)
Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. CoRR (2021)
Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning. CoRR (2021)
Pierre-Alexandre Kamienny, Jean Tarbouriech, Alessandro Lazaric, Ludovic Denoyer
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching. CoRR (2021)
Paul Luyo, Evrard Garcelon, Alessandro Lazaric, Matteo Pirotta
Differentially Private Exploration in Reinforcement Learning with Linear Representation. CoRR (2021)
Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Leveraging Good Representations in Linear Contextual Bandits. ICML (2021)
Rui Yuan, Robert M. Gower, Alessandro Lazaric
A general sample complexity analysis of vanilla policy gradient. CoRR (2021)
Andrea Tirinzoni, Alessandro Lazaric, Marcello Restelli
A Novel Confidence-Based Algorithm for Structured Bandits. AISTATS (2020)
Marc Abeille, Alessandro Lazaric
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation. ICML (2020)
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Learning Near Optimal Policies with Low Inherent Bellman Error. ICML (2020)
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta
Improved Algorithms for Conservative Exploration in Bandits. AAAI (2020)
Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Concentration Inequalities for Multinoulli Random Variables. CoRR (2020)
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta
Conservative Exploration in Reinforcement Learning. CoRR (2020)
Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric
Active Model Estimation in Markov Decision Processes. CoRR (2020)
Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits. NeurIPS (2020)
Pierre-Alexandre Kamienny, Matteo Pirotta, Alessandro Lazaric, Thibault Lavril, Nicolas Usunier, Ludovic Denoyer
Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization. CoRR (2020)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification. CoRR (2020)
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Improved Analysis of UCRL2 with Empirical Bernstein Inequality. CoRR (2020)
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. CoRR (2020)
Rui Yuan, Alessandro Lazaric, Robert M. Gower
Sketched Newton-Raphson. CoRR (2020)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. NeurIPS (2020)
Andrea Tirinzoni, Alessandro Lazaric, Marcello Restelli
A Novel Confidence-Based Algorithm for Structured Bandits. CoRR (2020)
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta
Improved Algorithms for Conservative Exploration in Bandits. CoRR (2020)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Near-linear time Gaussian process optimization with adaptive batching and resparsification. ICML (2020)
Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
Meta-learning with Stochastic Linear Bandits. ICML (2020)
Marc Abeille, Alessandro Lazaric
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation. CoRR (2020)
Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko
A single algorithm for both restless and rested rotting bandits. AISTATS (2020)
Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. AISTATS (2020)
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. NeurIPS (2020)
Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric
No-Regret Exploration in Goal-Oriented Reinforcement Learning. ICML (2020)
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta
Conservative Exploration in Reinforcement Learning. AISTATS (2020)
Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits. CoRR (2020)
Evrard Garcelon, Baptiste Rozière, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta
Adversarial Attacks on Linear Contextual Bandits. NeurIPS (2020)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. CoRR (2020)
Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric
Active Model Estimation in Markov Decision Processes. UAI (2020)
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Learning Near Optimal Policies with Low Inherent Bellman Error. CoRR (2020)
Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
Meta-learning with Stochastic Linear Bandits. CoRR (2020)
Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
A Provably Efficient Sample Collection Strategy for Reinforcement Learning. CoRR (2020)
Evrard Garcelon, Baptiste Rozière, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta
Adversarial Attacks on Linear Contextual Bandits. CoRR (2020)
Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric
No-Regret Exploration in Goal-Oriented Reinforcement Learning. CoRR (2019)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. COLT (2019)
Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs. NeurIPS (2019)
Jean Tarbouriech, Alessandro Lazaric
Active Exploration in Markov Decision Processes. CoRR (2019)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. CoRR (2019)
Nicolas Carion, Gabriel Synnaeve, Alessandro Lazaric, Nicolas Usunier
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning. CoRR (2019)
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Limiting Extrapolation in Linear Approximate Value Iteration. NeurIPS (2019)
Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard
Regret Bounds for Learning State Representations in Reinforcement Learning. NeurIPS (2019)
Nicolas Carion, Nicolas Usunier, Gabriel Synnaeve, Alessandro Lazaric
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning. NeurIPS (2019)
Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko
Rotting bandits are no harder than stochastic ones. AISTATS (2019)
Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric, Emmanuel Dupoux, Marco Baroni
Word-order biases in deep-agent emergent communication. CoRR (2019)
Jean Tarbouriech, Alessandro Lazaric
Active Exploration in Markov Decision Processes. AISTATS (2019)
Andrea Zanette, David Brandfonbrener, Matteo Pirotta, Alessandro Lazaric
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. CoRR (2019)
Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric, Emmanuel Dupoux, Marco Baroni
Word-order Biases in Deep-agent Emergent Communication. ACL (1) (2019)
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Distributed Adaptive Sampling for Kernel Matrix Approximation. CoRR (2018)
Romain Warlop, Alessandro Lazaric, Jérémie Mary
Fighting Boredom in Recommender Systems with Linear Reinforcement Learning. NeurIPS (2018)
Marc Abeille, Alessandro Lazaric
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems. ICML (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Alessandro Lazaric