← Authors

Alessandro Lazaric

81 papers · 2007–2025 · 13 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (13) 🌈 Renaissance Researcher (5) πŸƒ Academic Marathon (18) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (13) πŸ—ΊοΈ Taxonomy Completionist (22) 🏠 Conference Loyalist (26) 🌟 Keyword Trendsetter Combo (6) 🀝 Dynamic Duo (30) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (11) πŸ† Keyword Champion (2) πŸ† Grand Slam πŸ’Ž Century Club (81) πŸ—ƒοΈ Keyword Collector (85) πŸ”₯ Unstoppable (16) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer ⚑ Prolific Year (6)

Conferences

NIPS (26) ICML (17) AISTATS (16) ICLR (7) COLT (3) ALT (2) IJCAI (2) JMLR (2) UAI (2) AAAI (1) ACL (1) ACML (1) CORL (1)

Top co-authors

Matteo Pirotta (30) Michal Valko (17) Andrea Tirinzoni (12) Mohammad Ghavamzadeh (12) Jean Tarbouriech (10) Emma Brunskill (8) Daniele Calandriello (8) RΓ©mi Munos (7) Marcello Restelli (7) Ronan Fruit (6)

Keywords

regret bound (30) reinforcement learning (17) markov decision process (14) multi-armed bandit (13) sample complexity (13) regret minimization (7) online learning (6) representation learning (5) function approximation (5) exploration exploitation (5) value iteration (4) policy learning (4) contextual bandit (4) gaussian process (3) thompson sampling (3) stochastic shortest path (3) bellman error (3) stochastic optimization (3) sequential decision making (3) bayesian optimization (3)

Papers

Temporal Difference Flows ICML 2025
Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models ICLR 2025
Fast Imitation via Behavior Foundation Models ICLR 2024
Simple Ingredients for Offline Reinforcement Learning ICML 2024
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies ICLR 2023
Layered State Discovery for Incremental Autonomous Exploration ICML 2023
Contextual bandits with concave rewards, and an application to fair ranking ICLR 2023
On the Complexity of Representation Learning in Contextual Linear Bandits AISTATS 2023
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path ALT 2023
Temporal abstractions-augmented temporally contrastive learning: An alternative to the Laplacian in RL UAI 2022
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning ICLR 2022
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations AISTATS 2022
Adaptive Multi-Goal Exploration AISTATS 2022
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees NIPS 2022
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning ICLR 2022
A general sample complexity analysis of vanilla policy gradient AISTATS 2022
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching ICLR 2022
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times ICML 2022
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping CORL 2022
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection NIPS 2021
Reinforcement Learning with Prototypical Representations ICML 2021
Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model ALT 2021
Leveraging Good Representations in Linear Contextual Bandits ICML 2021
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret NIPS 2021
A Provably Efficient Sample Collection Strategy for Reinforcement Learning NIPS 2021
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration AISTATS 2020
A single algorithm for both restless and rested rotting bandits AISTATS 2020
Learning Near Optimal Policies with Low Inherent Bellman Error ICML 2020
No-Regret Exploration in Goal-Oriented Reinforcement Learning ICML 2020
Meta-learning with Stochastic Linear Bandits ICML 2020
Near-linear time Gaussian process optimization with adaptive batching and resparsification ICML 2020
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation ICML 2020
Active Model Estimation in Markov Decision Processes UAI 2020
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits NIPS 2020
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs NIPS 2020
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration NIPS 2020
Adversarial Attacks on Linear Contextual Bandits NIPS 2020
Improved Algorithms for Conservative Exploration in Bandits AAAI 2020
Conservative Exploration in Reinforcement Learning AISTATS 2020
A Novel Confidence-Based Algorithm for Structured Bandits AISTATS 2020
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret COLT 2019
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning NIPS 2019
Limiting Extrapolation in Linear Approximate Value Iteration NIPS 2019
Regret Bounds for Learning State Representations in Reinforcement Learning NIPS 2019
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs NIPS 2019
Word-order Biases in Deep-agent Emergent Communication ACL 2019
Active Exploration in Markov Decision Processes AISTATS 2019
Rotting bandits are no harder than stochastic ones AISTATS 2019
Improved large-scale graph learning through ridge spectral sparsification ICML 2018
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning ICML 2018
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes NIPS 2018
Fighting Boredom in Recommender Systems with Linear Reinforcement Learning NIPS 2018
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems ICML 2018
Trading off Rewards and Errors in Multi-Armed Bandits AISTATS 2017
Exploration-Exploitation in MDPs with Options AISTATS 2017
Linear Thompson Sampling Revisited AISTATS 2017
Efficient Second-Order Online Kernel Learning with Adaptive Embedding NIPS 2017
Regret Minimization in MDPs with Options without Prior Knowledge NIPS 2017
Active Learning for Accurate Estimation of Linear Models ICML 2017
Second-Order Kernel Online Convex Optimization with Adaptive Sketching ICML 2017
Distributed Adaptive Sampling for Kernel Matrix Approximation AISTATS 2017
Thompson Sampling for Linear-Quadratic Control Problems AISTATS 2017
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies COLT 2016
Reinforcement Learning of POMDPs using Spectral Methods COLT 2016
Improved Learning Complexity in Combinatorial Pure Exploration Bandits AISTATS 2016
Analysis of Classification-based Policy Iteration Algorithms JMLR 2016
Direct Policy Iteration with Demonstrations IJCAI 2015
Maximum Entropy Semi-Supervised Inverse Reinforcement Learning IJCAI 2015
Exploiting easy data in online optimization NIPS 2014
Sparse Multi-Task Reinforcement Learning NIPS 2014
Best-Arm Identification in Linear Bandits NIPS 2014
Online Stochastic Optimization under Correlated Bandit Feedback ICML 2014
Sequential Transfer in Multi-armed Bandit with Finite Set of Models NIPS 2013
Risk-Aversion in Multi-armed Bandits NIPS 2012
Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence NIPS 2012
Finite-Sample Analysis of Least-Squares Policy Iteration JMLR 2012
Multi-Bandit Best Arm Identification NIPS 2011
Transfer from Multiple MDPs NIPS 2011
Finite-sample Analysis of Bellman Residual Minimization ACML 2010
LSTD with Random Projections NIPS 2010
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods NIPS 2007