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Priya L. Donti

8 accepted papers

2026

Benchmarking Multi-Agent Reinforcement Learning in Power Grid Operations

Enrico Marchesini, Eva Boguslawski, Alessandro Leite, Christopher Amato, Matthieu DUSSARTRE, Marc Schoenauer +2
ICLR 2026poster

Improving power grid operations is essential for enhancing flexibility and accelerating grid decarbonization. Reinforcement learning (RL) has shown promise in this domain, most notably through the Learning to Run a Power Network competitions, but prior work has primarily focused on single-agent sett…

Cited by 0Source ↗Scholar ↗
2025

FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees

Hoang T. Nguyen, Priya L. Donti
NeurIPS 2025poster

Efficiently solving constrained optimization problems is crucial for numerous real-world applications, yet traditional solvers are often computationally prohibitive for real-time use. Machine learning-based approaches have emerged as a promising alternative to provide approximate solutions at faster…

Cited by 0Source ↗Scholar ↗
2025

PF∆: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations

Ana K. Rivera, Anvita Bhagavathula, Alvaro Carbonero, Priya L. Donti
NeurIPS 2025poster

Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces). Running…

Cited by 0Source ↗code ↗Scholar ↗
2024

Position: Application-Driven Innovation in Machine Learning

David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi +5
ICML 2024poster

In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offe…

Cited by 4Source ↗PDF ↗Scholar ↗
2021

Adversarially robust learning for security-constrained optimal power flow

Priya L. Donti, Aayushya Agarwal, Neeraj Vijay Bedmutha, Larry Pileggi, J Zico Kolter
NeurIPS 2021poster

In recent years, the ML community has seen surges of interest in both adversarially robust learning and implicit layers, but connections between these two areas have seldom been explored. In this work, we combine innovations from these areas to tackle the problem of N-k security-constrained optimal…

Cited by 23Source ↗PDF ↗Scholar ↗
2021

DC3: A learning method for optimization with hard constraints

Priya L. Donti, David Rolnick, J Zico Kolter
ICLR 2021poster

Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap "approximate solvers." Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such proble…

Cited by 251Source ↗PDF ↗code ↗Scholar ↗
2021

Enforcing robust control guarantees within neural network policies

Priya L. Donti, Melrose Roderick, Mahyar Fazlyab, J Zico Kolter
ICLR 2021poster

When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and performance. While robust control methods provide rigorous guarantees on system stability under certain worst-case disturbances, they often yield simple controllers that per…

Cited by 84Source ↗PDF ↗code ↗Scholar ↗