6 citations · 7 across the 8 of their papers we have counts for
8 papers
Differentiable Predictive Control for Robotics: A Data-Driven Predictive Safety Filter Approach
John Viljoen, Wenceslao Shaw Cortez, Jan Drgona +3
Model Predictive Control (MPC) is effective at generating safe control strategies in constrained scenarios, at the cost of computational complexity. This is especially the case in…
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming
Ethan King, James Kotary, Ferdinando Fioretto +1
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems. Increasing demand for real-time decis…
Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach
Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana +5
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Rec…
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory
Karthik Somayaji NS, Yu Wang, Malachi Schram +4
Risk-sensitive reinforcement learning (RL) has garnered significant attention in recent years due to the growing interest in deploying RL agents in real-world scenarios. A critical…
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning
Shimiao Li, Jan Drgona, Shrirang Abhyankar +1
Recent years have seen a rich literature of data-driven approaches designed for power grid applications. However, insufficient consideration of domain knowledge can impose a high r…
Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges
Feras A. Batarseh, Priya L. Donti, Ján Drgoňa +14
Climate change is one of the most pressing challenges of our time, requiring rapid action across society. As artificial intelligence tools (AI) are rapidly deployed, it is therefor…