| Effective Diversity in Population Based Reinforcement Learning J Parker-Holder*, A Pacchiano*, K Choromanski, S Roberts NeurIPS 2020 (Spotlight), 2020 | 96 | 2020 |
| Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits J Parker-Holder, V Nguyen, S Roberts NeurIPS 2020, 2020 | 45 | 2020 |
| MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research M Samvelyan, R Kirk, V Kurin, J Parker-Holder, M Jiang, E Hambro, ... NeurIPS 2021 (Datasets and Benchmarks), 2021 | 41 | 2021 |
| From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization KM Choromanski*, A Pacchiano*, J Parker-Holder*, Y Tang*, ... Advances in Neural Information Processing Systems, 10299-10309, 2019 | 41 | 2019 |
| Ready Policy One: World Building Through Active Learning P Ball*, J Parker-Holder*, A Pacchiano, K Choromanski, S Roberts ICML 2020, 2020 | 36 | 2020 |
| 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, 2019 | 32* | 2019 |
| Evolving Curricula with Regret-Based Environment Design J Parker-Holder*, M Jiang*, M Dennis, M Samvelyan, J Foerster, ... ICML 2022, 2022 | 31 | 2022 |
| Automated Reinforcement Learning (AutoRL): A Survey and Open Problems J Parker-Holder*, R Rajan*, X Song*, A Biedenkapp, Y Miao, T Eimer, ... JAIR, 2022 | 27 | 2022 |
| Replay-Guided Adversarial Environment Design M Jiang*, M Dennis*, J Parker-Holder, J Foerster, E Grefenstette, ... NeurIPS 2021, 2021 | 27 | 2021 |
| Towards Tractable Optimism in Model-Based Reinforcement Learning A Pacchiano*, P Ball*, J Parker-Holder*, K Choromanski, S Roberts UAI 2021, 2020 | 27* | 2020 |
| Learning to Score Behaviors for Guided Policy Optimization A Pacchiano*, J Parker-Holder*, Y Tang*, K Choromanski, ... International Conference on Machine Learning, 7445-7454, 2020 | 24 | 2020 |
| Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment PJ Ball*, C Lu*, J Parker-Holder, S Roberts ICML 2021, 2021 | 23 | 2021 |
| Tactical Optimism and Pessimism for Deep Reinforcement Learning T Moskovitz, J Parker-Holder, A Pacchiano, M Arbel, MI Jordan NeurIPS 2021, 2021 | 21* | 2021 |
| Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian J Parker-Holder*, L Metz, C Resnick, H Hu, A Lerer, A Letcher, ... Advances in Neural Information Processing Systems 33, 2020 | 21 | 2020 |
| Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations C Lu, PJ Ball, TGJ Rudner, J Parker-Holder, MA Osborne, YW Teh RSS L-DOD Workshop (Best Paper Award), 2022 | 10 | 2022 |
| Revisiting Design Choices in Model-Based Offline Reinforcement Learning C Lu*, PJ Ball*, J Parker-Holder, MA Osborne, SJ Roberts ICLR 2022 (Spotlight), 2022 | 10* | 2022 |
| Same State, Different Task: Continual Reinforcement Learning without Interference S Kessler, J Parker-Holder, P Ball, S Zohren, SJ Roberts AAAI 2022 (Oral), 2022 | 10 | 2022 |
| ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution X Song, KM Choromanski, J Parker-Holder, Y Tang, D Peng, D Jain, ... | 8* | 2021 |
| Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL J Parker-Holder, V Nguyen, S Desai, S Roberts NeurIPS 2021, 2021 | 8 | 2021 |
| Towards an Understanding of Default Policies in Multitask Policy Optimization T Moskovitz, M Arbel, J Parker-Holder, A Pacchiano AISTATS 2022 (Nominated for Best Paper Award), 2022 | 7 | 2022 |
Aldo PacchianoMicrosoft ResearchVerified email at microsoft.com
