497 citations · 2.1k across the 297 of their papers we have counts for
347 papers
Can You Trust Your Preference Models? Stackelberg Games for Alignment
Riccardo Formenti, Aditya Agrawal, Vikas Garg
Language models are frequently aligned to human preferences via imperfect evaluators whose errors can be exploited during optimization. We train policies to search for comparisons…
Can Play That Game: Online Fitted -Iteration for Continuous-Action Zero-Sum Markov Games with Convex-Concave Function Approximation
Kushagra Gupta, Jingqi Li, Cade Armstrong +4
Zero-sum Markov games arise in a wide variety of sequential decision-making problems such as adversarial learning and planning against modeled uncertainties. However, prior work on…
Multi-Fidelity Policy Gradients Stabilize Data-Scarce Reinforcement Learning
Xinjie Liu, Ruihan Zhao, Anirban Chaudhuri +3
Policy gradient methods for on-policy reinforcement learning (RL) can become unstable when expensive, scarce target-domain data yield noisy gradient estimates. We address this chal…
Learning Higher Order DC-DC Converter Control from Inversion
Kamakshi Tatkare, Ufuk Topcu, Brian Johnson
Control design for high-order converters with nonlinear dynamics is generally difficult. Nonlinear controls introduce formidable model complexity whereas linear controls suffer fro…
Flatness-based Neural Network Control of DC-DC Converters
Kamakshi Tatkare, Ufuk Topcu, Brian Johnson
In this work, we present a simple method to engineer a small neural network to control power converters. Learning-based methods have emerged as a promising approach for power conve…
FORM: Robot Manipulation through Direct Material Law Identification
Stepan Tretiakov, Ruihan Zhao, Cheng-Hsi Hsiao +6
When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied forces and motion. Rapid online…