Learning to cooperate in multi-agent social dilemmas. - Enrique Munoz de Cote; Alessandro Lazaric; Marcello Restelli (AAMAS, 2006)

Incremental Skill Acquisition for Self-motivated Learning Animats. - Andrea Bonarini; Alessandro Lazaric; Marcello Restelli (SAB, 2006)

Bifurcation Analysis of Reinforcement Learning Agents in the Selten's Horse Game. - Alessandro Lazaric; Enrique Munoz de Cote; Fabio Dercole; Marcello Restelli (Adaptive Agents and Multi-Agents Systems, 2007)

Reinforcement Learning in Complex Environments Through Multiple Adaptive Partitions. - Andrea Bonarini; Alessandro Lazaric; Marcello Restelli (AI*IA, 2007)

Reinforcement learning in extensive form games with incomplete information: the bargaining case study. - Alessandro Lazaric; Enrique Munoz de Cote; Nicola Gatti 001 (AAMAS, 2007)

Piecewise constant reinforcement learning for robotic applications. - Andrea Bonarini; Alessandro Lazaric; Marcello Restelli (ICINCO-ICSO, 2007)

Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods. - Alessandro Lazaric; Marcello Restelli; Andrea Bonarini (NIPS, 2007)

Towards Automated Bargaining in Electronic Markets: A Partially Two-Sided Competition Model. - Nicola Gatti 001; Alessandro Lazaric; Marcello Restelli (AMEC/TADA, 2008)

Transfer of task representation in reinforcement learning using policy-based proto-value functions. - Eliseo Ferrante; Alessandro Lazaric; Marcello Restelli (AAMAS (3), 2008)

On the usefulness of opponent modeling: the Kuhn Poker case study. - Alessandro Lazaric; Mario Quaresimale; Marcello Restelli (AAMAS (3), 2008)

Transfer of samples in batch reinforcement learning. - Alessandro Lazaric; Marcello Restelli; Andrea Bonarini (ICML, 2008)

Batch Reinforcement Learning for Controlling a Mobile Wheeled Pendulum Robot. - Andrea Bonarini; Claudio Caccia; Alessandro Lazaric; Marcello Restelli (IFIP AI, 2008)

Improving Batch Reinforcement Learning Performance through Transfer of Samples. - Alessandro Lazaric; Marcello Restelli; Andrea Bonarini (STAIRS, 2008)

Hybrid Stochastic-Adversarial On-line Learning. - Alessandro Lazaric; Rémi Munos (COLT, 2009)

Workshop summary: On-line learning with limited feedback. - Jean-Yves Audibert; Peter Auer; Alessandro Lazaric; Rémi Munos; Daniil Ryabko; Csaba Szepesvári (ICML, 2009)

Reinforcement distribution in fuzzy Q-learning. - Andrea Bonarini; Alessandro Lazaric; Francesco Montrone; Marcello Restelli (Fuzzy Sets Syst., 2009)

Bayesian Multi-Task Reinforcement Learning. - Alessandro Lazaric; Mohammad Ghavamzadeh (ICML, 2010)

Analysis of a Classification-based Policy Iteration Algorithm. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (ICML, 2010)

Finite-Sample Analysis of LSTD. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (ICML, 2010)

LSTD with Random Projections. - Mohammad Ghavamzadeh; Alessandro Lazaric; Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2010)

Finite-sample Analysis of Bellman Residual Minimization. - Odalric-Ambrym Maillard; Rémi Munos; Alessandro Lazaric; Mohammad Ghavamzadeh (ACML, 2010)

Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits. - Alexandra Carpentier; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Peter Auer (ALT, 2011)

Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization. - Matthew W. Hoffman; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (EWRL, 2011)

Classification-based Policy Iteration with a Critic. - Victor Gabillon; Alessandro Lazaric; Mohammad Ghavamzadeh; Bruno Scherrer (ICML, 2011)

Finite-Sample Analysis of Lasso-TD. - Mohammad Ghavamzadeh; Alessandro Lazaric; Rémi Munos; Matthew W. Hoffman (ICML, 2011)

Multi-Bandit Best Arm Identification. - Victor Gabillon; Mohammad Ghavamzadeh; Alessandro Lazaric; Sébastien Bubeck (NIPS, 2011)

Transfer from Multiple MDPs. - Alessandro Lazaric; Marcello Restelli (NIPS, 2011)

Least-Squares Methods for Policy Iteration. - Lucian Busoniu; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Robert Babuska; Bart De Schutter (Reinforcement Learning, 2012)

Transfer in Reinforcement Learning: A Framework and a Survey. - Alessandro Lazaric (Reinforcement Learning, 2012)

Conservative and Greedy Approaches to Classification-Based Policy Iteration. - Mohammad Ghavamzadeh; Alessandro Lazaric (AAAI, 2012)

A truthful learning mechanism for multi-slot sponsored search auctions with externalities. - Nicola Gatti 001; Alessandro Lazaric; Francesco Trovò (AAMAS, 2012)

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

A Dantzig Selector Approach to Temporal Difference Learning. - Matthieu Geist; Bruno Scherrer; Alessandro Lazaric; Mohammad Ghavamzadeh (ICML, 2012)

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence. - Victor Gabillon; Mohammad Ghavamzadeh; Alessandro Lazaric (NIPS, 2012)

Risk-Aversion in Multi-armed Bandits. - Amir Sani; Alessandro Lazaric; Rémi Munos (NIPS, 2012)

A truthful learning mechanism for contextual multi-slot sponsored search auctions with externalities. - Nicola Gatti 001; Alessandro Lazaric; Francesco Trovò (EC, 2012)

Learning with stochastic inputs and adversarial outputs. - Alessandro Lazaric; Rémi Munos (J. Comput. Syst. Sci., 2012)

Finite-sample analysis of least-squares policy iteration. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (J. Mach. Learn. Res., 2012)

Sequential Transfer in Multi-armed Bandit with Finite Set of Models. - Mohammad Gheshlaghi Azar; Alessandro Lazaric; Emma Brunskill (NIPS, 2013)

Regret Bounds for Reinforcement Learning with Policy Advice. - Mohammad Gheshlaghi Azar; Alessandro Lazaric; Emma Brunskill (ECML/PKDD (1), 2013)

Online Stochastic Optimization under Correlated Bandit Feedback. - Mohammad Gheshlaghi Azar; Alessandro Lazaric; Emma Brunskill (ICML, 2014)

Exploiting easy data in online optimization. - Amir Sani; Gergely Neu; Alessandro Lazaric (NIPS, 2014)

Best-Arm Identification in Linear Bandits. - Marta Soare; Alessandro Lazaric; Rémi Munos (NIPS, 2014)

Stochastic Optimization of a Locally Smooth Function under Correlated Bandit Feedback. - Mohammad Gheshlaghi Azar; Alessandro Lazaric; Emma Brunskill (CoRR, 2014)

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

Direct Policy Iteration with Demonstrations. - Jessica Chemali; Alessandro Lazaric (IJCAI, 2015)

The replacement bootstrap for dependent data. - Amir Sani; Alessandro Lazaric; Daniil Ryabko (ISIT, 2015)

Truthful learning mechanisms for multi-slot sponsored search auctions with externalities. - Nicola Gatti 001; Alessandro Lazaric; Marco Rocco; Francesco Trovò (Artif. Intell., 2015)

Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits. - Alexandra Carpentier; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Peter Auer; András Antos (CoRR, 2015)

Sparse multi-task reinforcement learning. - Daniele Calandriello; Alessandro Lazaric; Marcello Restelli (Intelligenza Artificiale, 2015)

Improved Learning Complexity in Combinatorial Pure Exploration Bandits. - Victor Gabillon; Alessandro Lazaric; Mohammad Ghavamzadeh; Ronald Ortner; Peter L. Bartlett (AISTATS, 2016)

Reinforcement Learning of POMDPs using Spectral Methods. - Kamyar Azizzadenesheli; Alessandro Lazaric; Animashree Anandkumar (COLT, 2016)

Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies. - Kamyar Azizzadenesheli; Alessandro Lazaric; Animashree Anandkumar (COLT, 2016)

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

Reinforcement Learning of POMDP's using Spectral Methods. - Kamyar Azizzadenesheli; Alessandro Lazaric; Animashree Anandkumar (CoRR, 2016)

Reinforcement Learning of Contextual MDPs using Spectral Methods. - Kamyar Azizzadenesheli; Alessandro Lazaric; Animashree Anandkumar (CoRR, 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)

Analysis of Classification-based Policy Iteration Algorithms. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (J. Mach. Learn. Res., 2016)

Parallel Higher Order Alternating Least Square for Tensor Recommender System. - Romain Warlop; Alessandro Lazaric; Jérémie Mary (AAAI Workshops, 2017)

Linear Thompson Sampling Revisited. - Marc Abeille; Alessandro Lazaric (AISTATS, 2017)

Thompson Sampling for Linear-Quadratic Control Problems. - Marc Abeille; Alessandro Lazaric (AISTATS, 2017)

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)

Exploration-Exploitation in MDPs with Options. - Ronan Fruit; Alessandro Lazaric (AISTATS, 2017)

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

Active Learning for Accurate Estimation of Linear Models. - Carlos Riquelme; Mohammad Ghavamzadeh; Alessandro Lazaric (ICML, 2017)

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

Regret Minimization in MDPs with Options without Prior Knowledge. - Ronan Fruit; Matteo Pirotta; Alessandro Lazaric; Emma Brunskill (NIPS, 2017)

Experimental results : Reinforcement Learning of POMDPs using Spectral Methods. - Kamyar Azizzadenesheli; Alessandro Lazaric; Animashree Anandkumar (CoRR, 2017)

Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems. - Marc Abeille; Alessandro Lazaric (ICML, 2018)

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

Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. - Ronan Fruit; Matteo Pirotta; Alessandro Lazaric; Ronald Ortner (ICML, 2018)

Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes. - Ronan Fruit; Matteo Pirotta; Alessandro Lazaric (NeurIPS, 2018)

Fighting Boredom in Recommender Systems with Linear Reinforcement Learning. - Romain Warlop; Alessandro Lazaric; Jérémie Mary (NeurIPS, 2018)

Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes. - Jian Qian; Ronan Fruit; Matteo Pirotta; Alessandro Lazaric (CoRR, 2018)

Word-order Biases in Deep-agent Emergent Communication. - Rahma Chaabouni 001; Eugene Kharitonov; Alessandro Lazaric; Emmanuel Dupoux; Marco Baroni (ACL (1), 2019)

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

Active Exploration in Markov Decision Processes. - Jean Tarbouriech; Alessandro Lazaric (AISTATS, 2019)

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

A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning. - Nicolas Carion; Nicolas Usunier; Gabriel Synnaeve; Alessandro Lazaric (NeurIPS, 2019)

Regret Bounds for Learning State Representations in Reinforcement Learning. - Ronald Ortner; Matteo Pirotta; Alessandro Lazaric; Ronan Fruit; Odalric-Ambrym Maillard (NeurIPS, 2019)

Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs. - Jian Qian; Ronan Fruit; Matteo Pirotta; Alessandro Lazaric (NeurIPS, 2019)

Limiting Extrapolation in Linear Approximate Value Iteration. - Andrea Zanette; Alessandro Lazaric; Mykel J. Kochenderfer; Emma Brunskill (NeurIPS, 2019)

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. - Andrea Zanette; David Brandfonbrener; Matteo Pirotta; Alessandro Lazaric (CoRR, 2019)

Improved Algorithms for Conservative Exploration in Bandits. - Evrard Garcelon; Mohammad Ghavamzadeh; Alessandro Lazaric; Matteo Pirotta (AAAI, 2020)

Conservative Exploration in Reinforcement Learning. - Evrard Garcelon; Mohammad Ghavamzadeh; Alessandro Lazaric; Matteo Pirotta (AISTATS, 2020)

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

A Novel Confidence-Based Algorithm for Structured Bandits. - Andrea Tirinzoni; Alessandro Lazaric; Marcello Restelli (AISTATS, 2020)

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. - Andrea Zanette; David Brandfonbrener; Emma Brunskill; Matteo Pirotta; Alessandro Lazaric (AISTATS, 2020)

Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation. - Marc Abeille; Alessandro Lazaric (ICML, 2020)

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

Meta-learning with Stochastic Linear Bandits. - Leonardo Cella; Alessandro Lazaric; Massimiliano Pontil (ICML, 2020)

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

Learning Near Optimal Policies with Low Inherent Bellman Error. - Andrea Zanette; Alessandro Lazaric; Mykel J. Kochenderfer; Emma Brunskill (ICML, 2020)

Adversarial Attacks on Linear Contextual Bandits. - Evrard Garcelon; Baptiste Rozière; Laurent Meunier; Jean Tarbouriech; Olivier Teytaud; Alessandro Lazaric; Matteo Pirotta (NeurIPS, 2020)

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

An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits. - Andrea Tirinzoni; Matteo Pirotta; Marcello Restelli; Alessandro Lazaric (NeurIPS, 2020)

Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. - Andrea Zanette; Alessandro Lazaric; Mykel J. Kochenderfer; Emma Brunskill (NeurIPS, 2020)

Active Model Estimation in Markov Decision Processes. - Jean Tarbouriech; Shubhanshu Shekhar; Matteo Pirotta; Mohammad Ghavamzadeh; Alessandro Lazaric (UAI, 2020)

Concentration Inequalities for Multinoulli Random Variables. - Jian Qian; Ronan Fruit; Matteo Pirotta; Alessandro Lazaric (CoRR, 2020)

Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization. - Pierre-Alexandre Kamienny; Matteo Pirotta; Alessandro Lazaric; Thibault Lavril; Nicolas Usunier; Ludovic Denoyer (CoRR, 2020)

Improved Analysis of UCRL2 with Empirical Bernstein Inequality. - Ronan Fruit; Matteo Pirotta; Alessandro Lazaric (CoRR, 2020)

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

Leveraging Good Representations in Linear Contextual Bandits. - Matteo Papini; Andrea Tirinzoni; Marcello Restelli; Alessandro Lazaric; Matteo Pirotta (ICML, 2021)

Reinforcement Learning with Prototypical Representations. - Denis Yarats; Rob Fergus; Alessandro Lazaric; Lerrel Pinto (ICML, 2021)

Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. - Matteo Papini; Andrea Tirinzoni; Aldo Pacchiano; Marcello Restelli; Alessandro Lazaric; Matteo Pirotta (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)

A Unified Framework for Conservative Exploration. - Yunchang Yang; Tianhao Wu 002; Han Zhong 001; Evrard Garcelon; Matteo Pirotta; Alessandro Lazaric; Liwei Wang 001; Simon S. Du (CoRR, 2021)

A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs. - Andrea Tirinzoni; Matteo Pirotta; Alessandro Lazaric (CoRR, 2021)

Differentially Private Exploration in Reinforcement Learning with Linear Representation. - Paul Luyo; Evrard Garcelon; Alessandro Lazaric; Matteo Pirotta (CoRR, 2021)

Top K Ranking for Multi-Armed Bandit with Noisy Evaluations. - Evrard Garcelon; Vashist Avadhanula; Alessandro Lazaric; Matteo Pirotta (AISTATS, 2022)

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

A general sample complexity analysis of vanilla policy gradient. - Rui Yuan; Robert M. Gower; Alessandro Lazaric (AISTATS, 2022)

Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping. - Lina Mezghani; Sainbayar Sukhbaatar; Piotr Bojanowski; Alessandro Lazaric; Karteek Alahari (CoRL, 2022)

Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching. - Pierre-Alexandre Kamienny; Jean Tarbouriech; Sylvain Lamprier; Alessandro Lazaric; Ludovic Denoyer (ICLR, 2022)

A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning. - Yunchang Yang; Tianhao Wu 002; Han Zhong 001; Evrard Garcelon; Matteo Pirotta; Alessandro Lazaric; Liwei Wang 001; Simon Shaolei Du (ICLR, 2022)

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning. - Denis Yarats; Rob Fergus; Alessandro Lazaric; Lerrel Pinto (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)

Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. - Andrea Tirinzoni; Matteo Papini; Ahmed Touati; Alessandro Lazaric; Matteo Pirotta (NeurIPS, 2022)

Temporal abstractions-augmented temporally contrastive learning: An alternative to the Laplacian in RL. - Akram Erraqabi; Marlos C. Machado; Mingde Zhao 001; Sainbayar Sukhbaatar; Alessandro Lazaric; Ludovic Denoyer; Yoshua Bengio (UAI, 2022)

Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning. - Denis Yarats; David Brandfonbrener; Hao Liu 055; Michael Laskin; Pieter Abbeel; Alessandro Lazaric; Lerrel Pinto (CoRR, 2022)

Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler. - Yifang Chen; Karthik Abinav Sankararaman; Alessandro Lazaric; Matteo Pirotta; Dmytro Karamshuk; Qifan Wang 001; Karishma Mandyam; Sinong Wang; Han Fang (CoRR, 2022)

Sketched Newton-Raphson. - Rui Yuan; Alessandro Lazaric; Robert M. Gower (SIAM J. Optim., 2022)

On the Complexity of Representation Learning in Contextual Linear Bandits. - Andrea Tirinzoni; Matteo Pirotta; Alessandro Lazaric (AISTATS, 2023)

Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path. - Liyu Chen; Andrea Tirinzoni; Matteo Pirotta; Alessandro Lazaric (ALT, 2023)

Contextual bandits with concave rewards, and an application to fair ranking. - Virginie Do; Elvis Dohmatob; Matteo Pirotta; Alessandro Lazaric; Nicolas Usunier (ICLR, 2023)

Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. - Rui Yuan; Simon Shaolei Du; Robert M. Gower; Alessandro Lazaric; Lin Xiao (ICLR, 2023)

Layered State Discovery for Incremental Autonomous Exploration. - Liyu Chen; Andrea Tirinzoni; Alessandro Lazaric; Matteo Pirotta (ICML, 2023)

Group Fairness in Reinforcement Learning. - Harsh Satija; Alessandro Lazaric; Matteo Pirotta; Joelle Pineau (Trans. Mach. Learn. Res., 2023)

System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes. - Arpit Agarwal; Nicolas Usunier; Alessandro Lazaric; Maximilian Nickel (FAccT, 2024)

Fast Imitation via Behavior Foundation Models. - Matteo Pirotta; Andrea Tirinzoni; Ahmed Touati; Alessandro Lazaric; Yann Ollivier (ICLR, 2024)

Simple Ingredients for Offline Reinforcement Learning. - Edoardo Cetin; Andrea Tirinzoni; Matteo Pirotta; Alessandro Lazaric; Yann Ollivier; Ahmed Touati (ICML, 2024)

Reinforcement Learning with Options and State Representation. - Ayoub Ghriss; Masashi Sugiyama; Alessandro Lazaric (CoRR, 2024)

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 (ICLR, 2025)

Temporal Difference Flows. - Jesse Farebrother; Matteo Pirotta; Andrea Tirinzoni; Rémi Munos; Alessandro Lazaric; Ahmed Touati (CoRR, 2025)

Fast Adaptation with Behavioral Foundation Models. - Harshit Sikchi; Andrea Tirinzoni; Ahmed Touati; Yingchen Xu; Anssi Kanervisto; Scott Niekum; Amy Zhang 001; Alessandro Lazaric; Matteo Pirotta (CoRR, 2025)

Embodied AI Agents: Modeling the World. - Pascale Fung; Yoram Bachrach; Asli Celikyilmaz; Kamalika Chaudhuri; Delong Chen; Willy Chung; Emmanuel Dupoux; Hervé Jégou; Alessandro Lazaric; Arjun Majumdar; Andrea Madotto; Franziska Meier; et al. (CoRR, 2025)