| Taking the human out of the loop: A review of Bayesian optimization B Shahriari, K Swersky, Z Wang, RP Adams, N De Freitas Proceedings of the IEEE 104 (1), 148-175, 2015 | 6232 | 2015 |
| Gemma: Open models based on gemini research and technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 1149 | 2024 |
| Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 573 | 2024 |
| Critic regularized regression Z Wang, A Novikov, K Zolna, JS Merel, JT Springenberg, SE Reed, ... Advances in Neural Information Processing Systems 33, 7768-7778, 2020 | 364 | 2020 |
| Acme: A research framework for distributed reinforcement learning MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ... arXiv preprint arXiv:2006.00979, 2020 | 276 | 2020 |
| On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning M Hoffman, B Shahriari, N Freitas Artificial Intelligence and Statistics, 365-374, 2014 | 181 | 2014 |
| Making efficient use of demonstrations to solve hard exploration problems TL Paine, C Gulcehre, B Shahriari, M Denil, M Hoffman, H Soyer, ... arXiv preprint arXiv:1909.01387, 2019 | 102 | 2019 |
| Unbounded Bayesian optimization via regularization B Shahriari, A Bouchard-Côté, N Freitas Artificial intelligence and statistics, 1168-1176, 2016 | 90 | 2016 |
| An entropy search portfolio for Bayesian optimization B Shahriari, Z Wang, MW Hoffman, A Bouchard-Côté, N de Freitas arXiv preprint arXiv:1406.4625, 2014 | 86 | 2014 |
| Charline Le Lan, Christopher A G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... Choquette-Choo, Clément Crepy, Daniel Cer, Daphne Ippolito, David Reid, 2024 | 52 | 2024 |
| Modular mechanisms for Bayesian optimization MW Hoffman, B Shahriari NIPS workshop on Bayesian optimization, 1-5, 2014 | 37 | 2014 |
| Do we need “harmless” Bayesian optimization and “first-order” Bayesian optimization MO Ahmed, B Shahriari, M Schmidt NIPS BayesOpt 6, 22, 2016 | 36 | 2016 |
| Heteroscedastic treed bayesian optimisation JAM Assael, Z Wang, B Shahriari, N de Freitas arXiv preprint arXiv:1410.7172, 2014 | 30 | 2014 |
| On multi-objective policy optimization as a tool for reinforcement learning: case studies in offline RL and finetuning A Abdolmaleki, SH Huang, G Vezzani, B Shahriari, JT Springenberg, ... arXiv preprint arXiv:2106.08199, 2021 | 24 | 2021 |
| Bond: Aligning llms with best-of-n distillation PG Sessa, R Dadashi, L Hussenot, J Ferret, N Vieillard, A Ramé, ... arXiv preprint arXiv:2407.14622, 2024 | 20 | 2024 |
| Which learning algorithms can generalize identity-based rules to novel inputs? P Tupper, B Shahriari arXiv preprint arXiv:1605.04002, 2016 | 10 | 2016 |
| Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach B Shahriari, A Abdolmaleki, A Byravan, A Friesen, S Liu, JT Springenberg, ... arXiv preprint arXiv:2204.10256, 2022 | 9 | 2022 |
| A combined finite element and Bayesian optimization framework for shape optimization in spectral geometry S Dominguez, N Nigam, B Shahriari Computers & Mathematics with Applications 74 (11), 2874-2896, 2017 | 9 | 2017 |
| Practical Bayesian optimization with application to tuning machine learning algorithms B Shahriari Ph. D. Dissertation. University of British Columbia. DOI: http://dx. doi …, 2016 | 6 | 2016 |
| The modified Cahn-Hilliard equation on general surfaces B Shahriari Simon Fraser University, 2010 | 6 | 2010 |
Nando de FreitasCIFAR & DeepMindVerified email at google.com
