2026 Alessandro Lazaric: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	34	12264	11907	4969	4745	188	4263

Overview

Alessandro Lazaric is affiliated with Facebook in the United States. Their research spans multiple areas within computer science and decision sciences, with a particular focus on artificial intelligence and operations research.

The scientist's main fields of study include:

Computer Science
Decision Sciences

Within these areas, their subfields of research comprise:

Artificial Intelligence
Management Science and Operations Research
Computer Networks and Communications
Electrical and Electronic Engineering
Computational Theory and Mathematics

Lazaric's work covers a range of topics prominently featuring:

Advanced Bandit Algorithms Research
Reinforcement Learning in Robotics
Machine Learning and Algorithms
Data Stream Mining Techniques
Optimization and Search Problems
Age of Information Optimization
Stochastic Gradient Optimization Techniques

The scientist has an extensive publication record with frequent appearances in notable venues such as:

arXiv (Cornell University)
Proceedings of the AAAI Conference on Artificial Intelligence
SIAM Journal on Optimization
HAL (Le Centre pour la Communication Scientifique Directe)

Among their recent papers are:

"Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning" (2021), published in arXiv (Cornell University)
"Reinforcement Learning with Prototypical Representations" (2021), published in arXiv (Cornell University)
"Learning Near Optimal Policies with Low Inherent Bellman Error" (2020), published in arXiv (Cornell University)
"Sketched Newton--Raphson" (2022), published in SIAM Journal on Optimization
"Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning" (2022), published in arXiv (Cornell University)

Lazaric frequently collaborates with several researchers, including:

Matteo Pirotta
Andrea Tirinzoni
Jean Tarbouriech
Michal Vaľko
Evrard Garcelon

Best Publications

Transfer in Reinforcement Learning: a Framework and a Survey

Alessandro Lazaric

306
Citations
Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence

Victor Gabillon;Mohammad Ghavamzadeh;Alessandro Lazaric

260
Citations
Transfer of samples in batch reinforcement learning

Alessandro Lazaric;Marcello Restelli;Andrea Bonarini

190
Citations
Linear Thompson Sampling Revisited

Marc Abeille;Alessandro Lazaric

171
Citations
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods

Alessandro Lazaric;Marcello Restelli;Andrea Bonarini

162
Citations
Bayesian Multi-Task Reinforcement Learning

Alessandro Lazaric;Mohammad Ghavamzadeh

127
Citations
Best-Arm Identification in Linear Bandits

Marta Soare;Alessandro Lazaric;Remi Munos

123
Citations
Upper-confidence-bound algorithms for active learning in multi-armed bandits

Alexandra Carpentier;Alessandro Lazaric;Mohammad Ghavamzadeh;Rémi Munos

116
Citations
Finite-sample analysis of least-squares policy iteration

Alessandro Lazaric;Mohammad Ghavamzadeh;Rémi Munos

114
Citations
Multi-Bandit Best Arm Identification

Victor Gabillon;Mohammad Ghavamzadeh;Alessandro Lazaric;Sébastien Bubeck

112
Citations
Risk-Aversion in Multi-armed Bandits

Amir Sani;Alessandro Lazaric;Rémi Munos

111
Citations
Analysis of a Classification-based Policy Iteration Algorithm

Alessandro Lazaric;Mohammad Ghavamzadeh;R mi Munos

89
Citations
Finite-Sample Analysis of LSTD

Alessandro Lazaric;Mohammad Ghavamzadeh;R mi Munos

84
Citations
Sequential Transfer in Multi-armed Bandit with Finite Set of Models

Mohammad Gheshlaghi azar;Alessandro Lazaric;Emma Brunskill

78
Citations
Reinforcement Learning of POMDPs using Spectral Methods

Kamyar Azizzadenesheli;Alessandro Lazaric;Animashree Anandkumar

76
Citations
Learning Near Optimal Policies with Low Inherent Bellman Error

Andrea Zanette;Alessandro Lazaric;Mykel Kochenderfer;Emma Brunskill

74
Citations
Reinforcement distribution in fuzzy Q-learning

Andrea Bonarini;Alessandro Lazaric;Francesco Montrone;Marcello Restelli

72
Citations
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning

Ronan Fruit;Matteo Pirotta;Alessandro Lazaric;Ronald Ortner

70
Citations
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

Denis Yarats;Rob Fergus;Alessandro Lazaric;Lerrel Pinto

69
Citations
LSTD with Random Projections

Mohammad Ghavamzadeh;Alessandro Lazaric;Odalric Maillard;Rémi Munos

68
Citations
Sparse multi-task reinforcement learning

Daniele Calandriello;Alessandro Lazaric;Marcello Restelli

65
Citations
Finite-Sample Analysis of Lasso-TD

Mohammad Ghavamzadeh;Alessandro Lazaric;Matthew Hoffman;R mi Munos

60
Citations
Improved Learning Complexity in Combinatorial Pure Exploration Bandits

Victor Gabillon;Alessandro Lazaric;Mohammad Ghavamzadeh;Ronald Ortner

29
Citations
Analysis of classification-based policy iteration algorithms

Alessandro Lazaric;Mohammad Ghavamzadeh;Rémi Munos

19
Citations

Frequent Co-Authors

Mohammad Ghavamzadeh Amazon (United States)

Emma Brunskill Stanford University

Rémi Munos French Institute for Research in Computer Science and Automation - INRIA

Anima Anandkumar Nvidia (United Kingdom)

Mykel J. Kochenderfer Stanford University

Ludovic Denoyer Sorbonne University

Lorenzo Rosasco MIT

Nicolas Usunier Facebook (United States)

Peter Auer University of Leoben

Simon S. Du University of Washington

External Links

Personal website of Alessandro Lazaric

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

As demand for tech expertise grows, students considering Computer Science in the USA often explore related fields and flexible learning options. Many universities now offer accredited online programs in STEM areas, increasing accessibility for students worldwide.

For those interested in sustainable tech solutions, an environmental engineer degree online can open doors to careers in eco-friendly innovation. If you're curious about core engineering principles, finding the cheapest mechanical engineering degree online can make this branch of engineering more affordable.

Students passionate about scientific discovery may ask, can you get a physics degree online? Yes—flexible programs now exist for remote learners. For those drawn to data, it’s also worth exploring what is the cheapest data science course in the us? to launch a data-driven career.

Choosing an online degree in these areas offers flexibility, cost savings, and direct routes into thriving tech fields closely related to Computer Science. Explore your options to tailor your academic and career journey.