| 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 | 2720 | 2020 |
| Finite-time analysis of kernelised contextual bandits M Valko, N Korda, R Munos, I Flaounas, N Cristianini Uncertainty in Artificial Intelligence, 2013 | 190 | 2013 |
| Outlier detection for patient monitoring and alerting M Hauskrecht, I Batal, M Valko, S Visweswaran, GF Cooper, G Clermont Journal of Biomedical Informatics, 2013 | 153 | 2013 |
| Stochastic simultaneous optimistic optimization M Valko, A Carpentier, R Munos International Conference on Machine Learning, 2013 | 117 | 2013 |
| 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 | 114* | 2022 |
| Online influence maximization under independent cascade model with semi-bandit feedback Z Wen, B Kveton, M Valko, S Vaswani Neural Information Processing Systems, 2017 | 113* | 2017 |
| Efficient learning by implicit exploration in bandit problems with side observations T Kocák, G Neu, M Valko, R Munos Neural Information Processing Systems, 2014 | 108 | 2014 |
| Spectral bandits for smooth graph functions M Valko, R Munos, B Kveton, T Kocák International Conference on Machine Learning, 2014 | 107 | 2014 |
| Black-box optimization of noisy functions with unknown smoothness JB Grill, M Valko, R Munos Neural Information Processing Systems, 2015 | 95 | 2015 |
| Simple regret for infinitely many armed bandits A Carpentier, M Valko International Conference on Machine Learning, 2015 | 80 | 2015 |
| 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 | 71 | 2021 |
| Episodic reinforcement learning in finite MDPs: Minimax lower bounds revisited O Darwiche Domingues, P Ménard, E Kaufmann, M Valko Algorithmic Learning Theory, 2021 | 63 | 2021 |
| 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 | 61 | 2019 |
| Conditional outlier detection for clinical alerting M Hauskrecht, M Valko, I Batal, G Clermont, S Visweswaran, GF Cooper AMIA annual symposium proceedings 2010, 286, 2010 | 60 | 2010 |
| Evidence-based anomaly detection in clinical domains M Hauskrecht, M Valko, B Kveton, S Visweswaran, GF Cooper AMIA Annual Symposium Proceedings 2007, 319, 2007 | 56 | 2007 |
| 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 | 50 | 2020 |
| Bayesian policy gradient and actor-critic algorithms M Ghavamzadeh, Y Engel, M Valko Journal of Machine Learning Research 17 (66), 1-53, 2016 | 50* | 2016 |
| Identification of microbial and proteomic biomarkers in early childhood caries TC Hart, PM Corby, M Hauskrecht, OK Hee Ryu, R Pelikan, M Valko, ... International journal of dentistry 2011, 2011 | 48 | 2011 |
| Online semi-supervised learning on quantized graphs M Valko, B Kveton, L Huang, D Ting Uncertainty in Artificial Intelligence, 2010 | 47 | 2010 |
| DPPy: DPP Sampling with Python. G Gautier, G Polito, R Bardenet, M Valko J. Mach. Learn. Res. 20, 180:1-180:7, 2019 | 46* | 2019 |
Rémi MunosDeepMindVerified email at inria.fr
