| Tackling Climate Change with Machine Learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... ACM Computing Surveys (CSUR) 55 (2), 1-96, 2022 | 1073* | 2022 |
| Task-based end-to-end model learning in stochastic optimization P Donti, B Amos, JZ Kolter Advances in Neural Information Processing Systems, 5484-5494, 2017 | 352 | 2017 |
| SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver PW Wang, PL Donti, B Wilder, Z Kolter International Conference on Machine Learning, 6545-6554, 2019 | 273 | 2019 |
| Aligning artificial intelligence with climate change mitigation LH Kaack, PL Donti, E Strubell, G Kamiya, F Creutzig, D Rolnick Nature Climate Change 12 (6), 518-527, 2022 | 205 | 2022 |
| DC3: A learning method for optimization with hard constraints PL Donti, D Rolnick, JZ Kolter International Conference on Learning Representations, 2021 | 157 | 2021 |
| Matrix Completion for Low-Observability Voltage Estimation PL Donti, Y Liu, AJ Schmitt, A Bernstein, R Yang, Y Zhang IEEE Transactions on Smart Grid 11 (3), 2520 - 2530, 2019 | 82 | 2019 |
| Enforcing robust control guarantees within neural network policies PL Donti, M Roderick, M Fazlyab, JZ Kolter International Conference on Learning Representations, 2021 | 75 | 2021 |
| Machine Learning for Sustainable Energy Systems PL Donti, JZ Kolter Annual Review of Environment and Resources 46, 719-747, 2021 | 66 | 2021 |
| Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization B Chen, PL Donti, K Baker, JZ Kolter, M Berges ACM International Conference on Future Energy Systems (ACM e-Energy), 2021 | 51 | 2021 |
| Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals LH Kaack, PL Donti, E Strubell, D Rolnick Heinrich Böll Foundation E-Paper, 2020 | 20 | 2020 |
| How Much Are We Saving after All? Characterizing the Effects of Commonly Varying Assumptions on Emissions and Damage Estimates in PJM PL Donti, JZ Kolter, IL Azevedo Environmental Science & Technology 53 (16), 9905-9914, 2019 | 20 | 2019 |
| Climate Change and AI. Recommendations for Government Action P Clutton-Brock, D Rolnick, PL Donti, L Kaack GPAI, Climate Change AI, Centre for AI & Climate, 2021 | 16 | 2021 |
| Adversarially robust learning for security-constrained optimal power flow PL Donti, A Agarwal, NV Bedmutha, L Pileggi, JZ Kolter Advances in Neural Information Processing Systems 34, 2021 | 14 | 2021 |
| Digitizing a sustainable future LA Reisch, L Joppa, P Howson, A Gil, P Alevizou, N Michaelidou, ... One Earth 4 (6), 768-771, 2021 | 9 | 2021 |
| A Call for Universities to Develop Requirements for Community Engagement in AI Research E Black, J Williams, MA Madaio, PL Donti Fair & Responsible AI Workshop @ CHI2020, 2020 | 9 | 2020 |
| Inverse Optimal Power Flow: Assessing the Vulnerability of Power Grid Data PL Donti, IL Azevedo, JZ Kolter AI for Social Good Workshop at NeurIPS 2018, 2018 | 6 | 2018 |
| How machine learning can help tackle climate change P Donti XRDS: Crossroads, The ACM Magazine for Students 27 (2), 58-61, 2020 | 5 | 2020 |
| Employing adversarial robustness techniques for large-scale stochastic optimal power flow A Agarwal, PL Donti, JZ Kolter, L Pileggi Electric Power Systems Research 212, 108497, 2022 | 4 | 2022 |
| Predicting the Quality of User Experiences to Improve Productivity and Wellness. PL Donti, J Rosenbloom, A Gruver, JC Boerkoel Jr AAAI, 4154-4155, 2015 | 3 | 2015 |
| Forecasting Marginal Emissions Factors in PJM A Wang, PL Donti Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020, 2020 | 2 | 2020 |
David RolnickMcGill University, Mila Quebec AI Institute在 cs.mcgill.ca 的电子邮件经过验证
