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Michal Valko
Michal Valko
Llama @ Meta Paris & Inria & MVA - Ex: Gemini and BYOL @ Google DeepMind
Verified email at meta.com - Homepage
Title
Cited by
Cited by
Year
Bootstrap your own latent: A new approach to self-supervised learning
JB Grill, F Strub, F Altché, C Tallec, PH Richemond, E Buchatskaya, ...
Neural Information Processing Systems, 2020
61482020
Large-scale representation learning on graphs via bootstrapping
S Thakoor, C Tallec, MG Azar, R Munos, P Veličković, M Valko
International Conference on Learning Representations, 2022
360*2022
Finite-time analysis of kernelised contextual bandits
M Valko, N Korda, R Munos, I Flaounas, N Cristianini
Uncertainty in Artificial Intelligence, 2013
2652013
Outlier detection for patient monitoring and alerting
M Hauskrecht, I Batal, M Valko, S Visweswaran, GF Cooper, G Clermont
Journal of Biomedical Informatics, 2013
1732013
Online influence maximization under independent cascade model with semi-bandit feedback
Z Wen, B Kveton, M Valko, S Vaswani
Neural Information Processing Systems, 2017
147*2017
Stochastic simultaneous optimistic optimization
M Valko, A Carpentier, R Munos
International Conference on Machine Learning, 2013
1412013
Spectral bandits for smooth graph functions
M Valko, R Munos, B Kveton, T Kocák
International Conference on Machine Learning, 2014
1322014
Efficient learning by implicit exploration in bandit problems with side observations
T Kocák, G Neu, M Valko, R Munos
Neural Information Processing Systems, 2014
1302014
Broaden your views for self-supervised video learning
A Recasens, P Luc, JB Alayrac, L Wang, F Strub, C Tallec, M Malinowski, ...
International Conference on Computer Vision, 2021
1262021
Episodic reinforcement learning in finite MDPs: Minimax lower bounds revisited
O Darwiche Domingues, P Ménard, E Kaufmann, M Valko
Algorithmic Learning Theory, 2021
1102021
Black-box optimization of noisy functions with unknown smoothness
JB Grill, M Valko, R Munos
Neural Information Processing Systems, 2015
1102015
Simple regret for infinitely many armed bandits
A Carpentier, M Valko
International Conference on Machine Learning, 2015
1022015
A general theoretical paradigm to understand learning from human preferences
MG Azar, M Rowland, B Piot, D Guo, D Calandriello, M Valko, R Munos
International Conference on Artificial Intelligence and Statistics, 2024
932024
Game Plan: What AI can do for Football, and What Football can do for AI
K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ...
Journal of Artificial Intelligence Research 71, 41-88, 2021
882021
Adaptive reward-free exploration
E Kaufmann, P Ménard, OD Domingues, A Jonsson, E Leurent, M Valko
Algorithmic Learning Theory, 2021
882021
BYOL works even without batch statistics
PH Richemond, JB Grill, F Altché, C Tallec, F Strub, A Brock, S Smith, ...
NeurIPS 2020 Workshop: Self-Supervised Learning - Theory and Practice, 2020
852020
Gaussian process optimization with adaptive sketching: Scalable and no regret
D Calandriello, L Carratino, A Lazaric, M Valko, L Rosasco
Conference on Learning Theory, 2019
822019
Gamification of pure exploration for linear bandits
R Degenne, P Ménard, X Shang, M Valko
International Conference on Machine Learning, 2020
812020
Fast active learning for pure exploration in reinforcement learning
P Ménard, OD Domingues, A Jonsson, E Kaufmann, E Leurent, M Valko
International Conference on Machine Learning, 2021
742021
Monte-Carlo tree search as regularized policy optimization
JB Grill, F Altché, Y Tang, T Hubert, M Valko, I Antonoglou, R Munos
International Conference on Machine Learning, 2020
692020
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