Alessandro Lazaric
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
69
Venues
13
Active years
2007–2025
Best venue rank
A*
Where they publish
Papers
69 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models. | Andrea Tirinzoni, Ahmed Touati, Jesse Farebrother, Mateusz Guzek, Anssi Kanervisto, Yingchen Xu, Alessandro Lazaric, Matteo Pirotta |
| 2025 | ICML | Temporal Difference Flows. | Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Rmi Munos, Alessandro Lazaric, Ahmed Touati |
| 2024 | ICLR | Fast Imitation via Behavior Foundation Models. | Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, Yann Ollivier |
| 2024 | ICML | Simple Ingredients for Offline Reinforcement Learning. | Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati |
| 2023 | AISTATS | On the Complexity of Representation Learning in Contextual Linear Bandits. | Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric |
| 2023 | ALT | Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path. | Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric |
| 2023 | ICLR | Contextual bandits with concave rewards, and an application to fair ranking. | Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier |
| 2023 | ICLR | Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. | Rui Yuan, Simon Shaolei Du, Robert M. Gower, Alessandro Lazaric, Lin Xiao |
| 2023 | ICML | Layered State Discovery for Incremental Autonomous Exploration. | Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta |
| 2022 | AISTATS | Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. | Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric, Matteo Pirotta |
| 2022 | AISTATS | Adaptive Multi-Goal Exploration. | Jean Tarbouriech, Omar Darwiche Domingues, Pierre Mnard, Matteo Pirotta, Michal Valko, Alessandro Lazaric |
| 2022 | AISTATS | A general sample complexity analysis of vanilla policy gradient. | Rui Yuan, Robert M. Gower, Alessandro Lazaric |
| 2022 | CoRL | Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping. | Lina Mezghani, Sainbayar Sukhbaatar, Piotr Bojanowski, Alessandro Lazaric, Karteek Alahari |
| 2022 | ICLR | Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching. | Pierre-Alexandre Kamienny, Jean Tarbouriech, Sylvain Lamprier, Alessandro Lazaric, Ludovic Denoyer |
| 2022 | ICLR | A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning. | Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon Shaolei Du |
| 2022 | ICLR | Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning. | Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto |
| 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 | UAI | Temporal abstractions-augmented temporally contrastive learning: An alternative to the Laplacian in RL. | Akram Erraqabi, Marlos C. Machado, Mingde Zhao, Sainbayar Sukhbaatar, Alessandro Lazaric, Ludovic Denoyer, Yoshua Bengio |
| 2021 | ALT | Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. | Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric |
| 2021 | ICML | Leveraging Good Representations in Linear Contextual Bandits. | Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta |
| 2021 | ICML | Reinforcement Learning with Prototypical Representations. | Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto |
| 2020 | AAAI | Improved Algorithms for Conservative Exploration in Bandits. | Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta |
| 2020 | AISTATS | Conservative Exploration in Reinforcement Learning. | Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta |
| 2020 | AISTATS | A single algorithm for both restless and rested rotting bandits. | Julien Seznec, Pierre Mnard, Alessandro Lazaric, Michal Valko |
| 2020 | AISTATS | A Novel Confidence-Based Algorithm for Structured Bandits. | Andrea Tirinzoni, Alessandro Lazaric, Marcello Restelli |
| 2020 | AISTATS | Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. | Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric |
| 2020 | ICML | Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation. | Marc Abeille, Alessandro Lazaric |
| 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 | Meta-learning with Stochastic Linear Bandits. | Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil |
| 2020 | ICML | No-Regret Exploration in Goal-Oriented Reinforcement Learning. | Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric |
| 2020 | ICML | Learning Near Optimal Policies with Low Inherent Bellman Error. | Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill |
| 2020 | UAI | Active Model Estimation in Markov Decision Processes. | Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric |
| 2019 | ACL | Word-order Biases in Deep-agent Emergent Communication. | Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric, Emmanuel Dupoux, Marco Baroni |
| 2019 | AISTATS | Rotting bandits are no harder than stochastic ones. | Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko |
| 2019 | AISTATS | Active Exploration in Markov Decision Processes. | Jean Tarbouriech, Alessandro Lazaric |
| 2019 | COLT | Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. | Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco |
| 2018 | ICML | Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems. | Marc Abeille, Alessandro Lazaric |
| 2018 | ICML | Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. | Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko |
| 2018 | ICML | Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. | Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner |
| 2017 | AAAI | Parallel Higher Order Alternating Least Square for Tensor Recommender System. | Romain Warlop, Alessandro Lazaric, Jrmie Mary |
| 2017 | AISTATS | Linear Thompson Sampling Revisited. | Marc Abeille, Alessandro Lazaric |
| 2017 | AISTATS | Thompson Sampling for Linear-Quadratic Control Problems. | Marc Abeille, Alessandro Lazaric |
| 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 | AISTATS | Exploration-Exploitation in MDPs with Options. | Ronan Fruit, Alessandro Lazaric |
| 2017 | ICML | Second-Order Kernel Online Convex Optimization with Adaptive Sketching. | Daniele Calandriello, Alessandro Lazaric, Michal Valko |
| 2017 | ICML | Active Learning for Accurate Estimation of Linear Models. | Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric |
| 2016 | AISTATS | Improved Learning Complexity in Combinatorial Pure Exploration Bandits. | Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter L. Bartlett |
| 2016 | COLT | Reinforcement Learning of POMDPs using Spectral Methods. | Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar |
| 2016 | COLT | Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies. | Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar |
| 2016 | UAI | Analysis of Nystrm method with sequential ridge leverage scores. | Daniele Calandriello, Alessandro Lazaric, Michal Valko |
| 2015 | IJCAI | Maximum Entropy Semi-Supervised Inverse Reinforcement Learning. | Julien Audiffren, Michal Valko, Alessandro Lazaric, Mohammad Ghavamzadeh |
| 2015 | IJCAI | Direct Policy Iteration with Demonstrations. | Jessica Chemali, Alessandro Lazaric |
| 2015 | ISIT | The replacement bootstrap for dependent data. | Amir Sani, Alessandro Lazaric, Daniil Ryabko |
| 2014 | ICML | Online Stochastic Optimization under Correlated Bandit Feedback. | Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill |
| 2012 | AAAI | Conservative and Greedy Approaches to Classification-Based Policy Iteration. | Mohammad Ghavamzadeh, Alessandro Lazaric |
| 2012 | AAMAS | A truthful learning mechanism for multi-slot sponsored search auctions with externalities. | Nicola Gatti, Alessandro Lazaric, Francesco Trov |
| 2012 | ICML | A Dantzig Selector Approach to Temporal Difference Learning. | Matthieu Geist, Bruno Scherrer, Alessandro Lazaric, Mohammad Ghavamzadeh |
| 2011 | ALT | Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits. | Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rmi Munos, Peter Auer |
| 2011 | ICML | Classification-based Policy Iteration with a Critic. | Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Bruno Scherrer |
| 2011 | ICML | Finite-Sample Analysis of Lasso-TD. | Mohammad Ghavamzadeh, Alessandro Lazaric, Rmi Munos, Matthew W. Hoffman |
| 2010 | ICML | Bayesian Multi-Task Reinforcement Learning. | Alessandro Lazaric, Mohammad Ghavamzadeh |
| 2010 | ICML | Analysis of a Classification-based Policy Iteration Algorithm. | Alessandro Lazaric, Mohammad Ghavamzadeh, Rmi Munos |
| 2010 | ICML | Finite-Sample Analysis of LSTD. | Alessandro Lazaric, Mohammad Ghavamzadeh, Rmi Munos |
| 2009 | COLT | Hybrid Stochastic-Adversarial On-line Learning. | Alessandro Lazaric, Rmi Munos |
| 2009 | ICML | Workshop summary: On-line learning with limited feedback. | Jean-Yves Audibert, Peter Auer, Alessandro Lazaric, Rmi Munos, Daniil Ryabko, Csaba Szepesvri |
| 2008 | ICML | Transfer of samples in batch reinforcement learning. | Alessandro Lazaric, Marcello Restelli, Andrea Bonarini |
| 2007 | AAMAS | Bifurcation Analysis of Reinforcement Learning Agents in the Selten's Horse Game. | Alessandro Lazaric, Enrique Munoz de Cote, Fabio Dercole, Marcello Restelli |
| 2007 | ICINCO | Piecewise constant reinforcement learning for robotic applications. | Andrea Bonarini, Alessandro Lazaric, Marcello Restelli |