| Effective diversity in population based reinforcement learning J Parker-Holder, A Pacchiano, KM Choromanski, SJ Roberts Advances in Neural Information Processing Systems 33, 18050-18062, 2020 | 176 | 2020 |
| Evolving curricula with regret-based environment design J Parker-Holder, M Jiang, M Dennis, M Samvelyan, J Foerster, ... International Conference on Machine Learning, 17473-17498, 2022 | 128 | 2022 |
| Genie: Generative Interactive Environments J Bruce, M Dennis, A Edwards, J Parker-Holder, Y Shi, E Hughes, M Lai, ... ICML 2024 (Best Paper Award), 2024 | 125 | 2024 |
| Human-timescale adaptation in an open-ended task space AA Team, J Bauer, K Baumli, S Baveja, F Behbahani, A Bhoopchand, ... ICML 2023 (Oral), 2023 | 118* | 2023 |
| Automated reinforcement learning (autorl): A survey and open problems J Parker-Holder, R Rajan, X Song, A Biedenkapp, Y Miao, T Eimer, ... Journal of Artificial Intelligence Research 74, 517-568, 2022 | 108 | 2022 |
| Replay-guided adversarial environment design M Jiang, M Dennis, J Parker-Holder, J Foerster, E Grefenstette, ... Advances in Neural Information Processing Systems 34, 1884-1897, 2021 | 102 | 2021 |
| Minihack the planet: A sandbox for open-ended reinforcement learning research M Samvelyan, R Kirk, V Kurin, J Parker-Holder, M Jiang, E Hambro, ... arXiv preprint arXiv:2109.13202, 2021 | 100 | 2021 |
| Provably efficient online hyperparameter optimization with population-based bandits J Parker-Holder, V Nguyen, SJ Roberts Advances in neural information processing systems 33, 17200-17211, 2020 | 89 | 2020 |
| Revisiting Design Choices in Offline Model-Based Reinforcement Learning C Lu, PJ Ball, J Parker-Holder, MA Osborne, SJ Roberts arXiv preprint arXiv:2110.04135, 2021 | 68* | 2021 |
| Tactical optimism and pessimism for deep reinforcement learning T Moskovitz, J Parker-Holder, A Pacchiano, M Arbel, M Jordan Advances in Neural Information Processing Systems 34, 12849-12863, 2021 | 64 | 2021 |
| Synthetic Experience Replay C Lu, PJ Ball, YW Teh, J Parker-Holder NeurIPS 2023, 2023 | 62 | 2023 |
| Ready policy one: World building through active learning P Ball, J Parker-Holder, A Pacchiano, K Choromanski, S Roberts International Conference on Machine Learning, 591-601, 2020 | 57 | 2020 |
| From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization KM Choromanski, A Pacchiano, J Parker-Holder, Y Tang, V Sindhwani Advances in Neural Information Processing Systems 32, 2019 | 56 | 2019 |
| Same state, different task: Continual reinforcement learning without interference S Kessler, J Parker-Holder, P Ball, S Zohren, SJ Roberts Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7143-7151, 2022 | 54* | 2022 |
| Challenges and opportunities in offline reinforcement learning from visual observations C Lu, PJ Ball, TGJ Rudner, J Parker-Holder, MA Osborne, YW Teh arXiv preprint arXiv:2206.04779, 2022 | 46 | 2022 |
| Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts M Samvelyan, SC Raparthy, A Lupu, E Hambro, AH Markosyan, M Bhatt, ... arXiv preprint arXiv:2402.16822, 2024 | 45* | 2024 |
| Learning to score behaviors for guided policy optimization A Pacchiano, J Parker-Holder, Y Tang, K Choromanski, A Choromanska, ... International Conference on Machine Learning, 7445-7454, 2020 | 45 | 2020 |
| Augmented world models facilitate zero-shot dynamics generalization from a single offline environment PJ Ball, C Lu, J Parker-Holder, S Roberts International Conference on Machine Learning, 619-629, 2021 | 44 | 2021 |
| Provably robust blackbox optimization for reinforcement learning K Choromanski, A Pacchiano, J Parker-Holder, Y Tang, D Jain, Y Yang, ... Conference on Robot Learning, 683-696, 2020 | 43 | 2020 |
| Towards tractable optimism in model-based reinforcement learning A Pacchiano, P Ball, J Parker-Holder, K Choromanski, S Roberts Uncertainty in Artificial Intelligence, 1413-1423, 2021 | 41* | 2021 |
Aldo PacchianoBoston University and Broad Institute of MIT and HarvardVerified email at bu.edu