Piot, Bilal

Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 3,846

Rainbow: Combining Improvements in Deep Reinforcement Learning

Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., & Silver, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 1,846

Gemma 3 Technical Report

Gemma Team, Kamath, A., Ferret, J., Pathak, S., Vieillard, N., Merhej, R., Perrin, S., Matejovicova, T., Ramé, A., Rivière, M., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 1,156

Deep Q-learning From Demonstrations

Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Horgan, D., Quan, J., Sendonaris, A., Osband, I., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 889

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., & Riedmiller, M. (2017) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 478

Never Give Up: Learning Directed Exploration Strategies

Badia, A. P., Sprechmann, P., Vitvitskyi, A., Guo, D., Piot, B., Kapturowski, S., Tieleman, O., Arjovsky, M., Pritzel, A., Bolt, A., et al. (2020) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 170

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team, Riviere, M., Pathak, S., Sessa, P. G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ramé, A., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 149

Gemma 4 Technical Report

Gemma Team, Abd, S. E., Aggarwal, V., Algayres, R., Andreev, A., Bachem, O., Ballantyne, I., Brick, C., Cărbune, V., Casbon, M., et al. (2026) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 112

BYOL works even without batch statistics

Richemond, P. H., Grill, J.-B., Altché, F., Tallec, C., Strub, F., Brock, A., Smith, S., De, S., Pascanu, R., Piot, B., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 51

Observational Learning by Reinforcement Learning

Borsa, D., Piot, B., Munos, R., & Pietquin, O. (2017) · arXiv

Reinforcement Learning in RoboticsChild and Animal Learning Development

Cited by 47

Observational Learning by Reinforcement Learning

Borsa, D., Heess, N., Piot, B., Liu, S., Hasenclever, L., Munos, R., & Pietquin, O. (2019) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsChild and Animal Learning Development

Cited by 47

A General Theoretical Paradigm to Understand Learning from Human Preferences

Azar, M. G., Rowland, M., Piot, B., Guo, D., Calandriello, D., Valko, M., & Munos, R. (2023) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 21

BYOL-Explore: Exploration by Bootstrapped Prediction

Guo, Z. D., Thakoor, S., Pîslar, M., Pires, B. A., Altché, F., Tallec, C., Saade, A., Calandriello, D., Grill, J.-B., Tang, Y., et al. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 12