| Deep reinforcement learning for conservation decisions M Lapeyrolerie, MS Chapman, KEA Norman, C Boettiger Methods in Ecology and Evolution 13 (11), 2649-2662, 2022 | 20 | 2022 |
| Bridging adaptive management and reinforcement learning for more robust decisions M Chapman, L Xu, M Lapeyrolerie, C Boettiger Philosophical Transactions of the Royal Society B 378 (1881), 20220195, 2023 | 8 | 2023 |
| Limits to ecological forecasting: Estimating uncertainty for critical transitions with deep learning M Lapeyrolerie, C Boettiger Methods in Ecology and Evolution 14 (3), 785-798, 2023 | 6 | 2023 |
| Teaching machines to anticipate catastrophes M Lapeyrolerie, C Boettiger Proceedings of the National Academy of Sciences 118 (40), e2115605118, 2021 | 6 | 2021 |
| Power and accountability in reinforcement learning applications to environmental policy M Chapman, C Scoville, M Lapeyrolerie, C Boettiger arXiv preprint arXiv:2205.10911, 2022 | 4 | 2022 |
| Environment, Society, and Machine Learning C Scoville, H Faxon, M Chapman, SJ Fried, L Xu, C Boettiger, JM Reed, ... | 1 | 2023 |
| What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge F Olsson, CC Carey, C Boettiger, G Harrison, R Ladwig, MF Lapeyrolerie, ... Ecological Applications 1, 2, 2024 | | 2024 |
| Pretty darn good control: when are approximate solutions better than approximate models F Montealegre-Mora, M Lapeyrolerie, M Chapman, AG Keller, C Boettiger Bulletin of Mathematical Biology 85 (10), 95, 2023 | | 2023 |
