Spremljaj
Priya L. Donti
Priya L. Donti
Assistant Professor, MIT EECS & LIDS; Co-founder and Chair, Climate Change AI
Preverjeni e-poštni naslov na mit.edu - Domača stran
Naslov
Navedeno
Navedeno
Leto
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
1346*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
4242017
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
3462022
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
3222019
DC3: A learning method for optimization with hard constraints
PL Donti, D Rolnick, JZ Kolter
International Conference on Learning Representations, 2021
2282021
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
962019
Machine Learning for Sustainable Energy Systems
PL Donti, JZ Kolter
Annual Review of Environment and Resources 46, 719-747, 2021
882021
Enforcing robust control guarantees within neural network policies
PL Donti, M Roderick, M Fazlyab, JZ Kolter
International Conference on Learning Representations, 2021
822021
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
652021
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
272020
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
232021
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
212019
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
192021
Digitizing a sustainable future
LA Reisch, L Joppa, P Howson, A Gil, P Alevizou, N Michaelidou, ...
One Earth 4 (6), 768-771, 2021
112021
The Climate and Sustainability Implications of Generative AI
N Bashir, P Donti, J Cuff, S Sroka, M Ilic, V Sze, C Delimitrou, E Olivetti
MIT, 2024
102024
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
102020
Position: Application-driven innovation in machine learning
D Rolnick, A Aspuru-Guzik, S Beery, B Dilkina, PL Donti, M Ghassemi, ...
Forty-first International Conference on Machine Learning, 2024
9*2024
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
72022
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
72018
How machine learning can help tackle climate change
P Donti
XRDS: Crossroads, The ACM Magazine for Students 27 (2), 58-61, 2020
42020
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Članki 1–20