| 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 | 265 | 2020 |
| Estimating the progress of MapReduce pipelines K Morton, AL Friesen, M Balazinska, D Grossman Data Engineering (ICDE), 2010 IEEE 26th International Conference on, 681-684, 2010 | 190 | 2010 |
| Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv e-prints, arXiv: 2408.00118, 2024 | 178 | 2024 |
| On the role of planning in model-based deep reinforcement learning JB Hamrick, AL Friesen, F Behbahani, A Guez, F Viola, S Witherspoon, ... arXiv preprint arXiv:2011.04021, 2020 | 86 | 2020 |
| Retrieval-augmented reinforcement learning A Goyal, A Friesen, A Banino, T Weber, NR Ke, AP Badia, A Guez, ... International Conference on Machine Learning, 7740-7765, 2022 | 51 | 2022 |
| Modular meta-learning with shrinkage Y Chen, AL Friesen, F Behbahani, A Doucet, D Budden, M Hoffman, ... Advances in Neural Information Processing Systems 33, 2858-2869, 2020 | 47 | 2020 |
| The sum-product theorem: A foundation for learning tractable models AL Friesen, P Domingos International Conference on Machine Learning, 1909-1918, 2016 | 40 | 2016 |
| Recursive decomposition for nonconvex optimization AL Friesen, P Domingos arXiv preprint arXiv:1611.02755, 2016 | 38 | 2016 |
| How prior probability influences decision making: A unifying probabilistic model Y Huang, AL Friesen, T Hanks, M Shadlen, RP Rao Advances in neural information processing systems, 1268-1276, 2012 | 34 | 2012 |
| Introducing symmetries to black box meta reinforcement learning L Kirsch, S Flennerhag, H Van Hasselt, A Friesen, J Oh, Y Chen Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7202-7210, 2022 | 33 | 2022 |
| Deep learning as a mixed convex-combinatorial optimization problem AL Friesen, P Domingos arXiv preprint arXiv:1710.11573, 2017 | 27 | 2017 |
| A Bayesian developmental approach to robotic goal-based imitation learning MJY Chung, AL Friesen, D Fox, AN Meltzoff, RPN Rao PloS one 10 (11), e0141965, 2015 | 22 | 2015 |
| Gaze following as goal inference: A Bayesian model AL Friesen, RPN Rao Proceedings of the Annual Meeting of the Cognitive Science Society 33 (33), 2011 | 18 | 2011 |
| Imitation learning with hierarchical actions AL Friesen, RPN Rao 2010 IEEE 9th International conference on development and learning, 263-268, 2010 | 17 | 2010 |
| 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 | 12 | 2024 |
| Gemma 2: Improving open language models at a practical size, 2024 G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... URL https://arxiv. org/abs/2408.00118 1 (2), 3, 0 | 11 | |
| Model-value inconsistency as a signal for epistemic uncertainty A Filos, E Vértes, Z Marinho, G Farquhar, D Borsa, A Friesen, ... arXiv preprint arXiv:2112.04153, 2021 | 10 | 2021 |
| 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 | 7 | 2022 |
| Charline Le Lan G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... Sammy Jerome, Anton Tsitsulin, Nino Vieillard, Piotr Stanczyk, Sertan Girgin …, 2024 | 6 | 2024 |
| Submodular field grammars: representation, inference, and application to image parsing AL Friesen, PM Domingos Advances in Neural Information Processing Systems 31, 2018 | 5 | 2018 |
Pedro DomingosProfessor of Computer Science and Engineering, University of WashingtonEmail verificata su cs.washington.edu