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Marcus Lapeyrolerie
Marcus Lapeyrolerie
Postdoctoral Researcher, University of British Columbia
Verified email at mail.ubc.ca
Title
Cited by
Cited by
Year
Deep reinforcement learning for conservation decisions
M Lapeyrolerie, MS Chapman, KEA Norman, C Boettiger
Methods in Ecology and Evolution 13 (11), 2649-2662, 2022
522022
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
352023
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
202023
Teaching machines to anticipate catastrophes
M Lapeyrolerie, C Boettiger
Proceedings of the National Academy of Sciences 118 (40), e2115605118, 2021
112021
Power and accountability in reinforcement learning applications to environmental policy
M Chapman, C Scoville, M Lapeyrolerie, C Boettiger
arXiv preprint arXiv:2205.10911, 2022
92022
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 35 (1), e70004, 2025
32025
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, ...
Authorea Preprints, 2024
22024
Environment, Society, and Machine Learning
C Scoville, H Faxon, M Chapman, SJ Fried, L Xu, C Boettiger, JM Reed, ...
22023
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
22023
Applications of Neural Network Models for Conservation Decision-Making and Ecological Forecasting
MF Lapeyrolerie
University of California, Berkeley, 2024
2024
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Articles 1–10