24 citations · 69 across the 15 of their papers we have counts for
10 papers
Automatic Gradient Descent: Deep Learning without Hyperparameters
Jeremy Bernstein, Chris Mingard, Kevin Huang +2
The architecture of a deep neural network is defined explicitly in terms of the number of layers, the width of each layer and the general network topology. Existing optimisation fr…
Conformal Generative Modeling on Triangulated Surfaces
Victor Dorobantu, Charlotte Borcherds, Yisong Yue
We propose conformal generative modeling, a framework for generative modeling on 2D surfaces approximated by discrete triangle meshes. Our approach leverages advances in discrete c…
Eventual Discounting Temporal Logic Counterfactual Experience Replay
Cameron Voloshin, Abhinav Verma, Yisong Yue
Linear temporal logic (LTL) offers a simplified way of specifying tasks for policy optimization that may otherwise be difficult to describe with scalar reward functions. However, t…
POLAR: Preference Optimization and Learning Algorithms for Robotics
Maegan Tucker, Kejun Li, Yisong Yue +1
Parameter tuning for robotic systems is a time-consuming and challenging task that often relies on domain expertise of the human operator. Moreover, existing learning methods are n…
Compactly Restrictable Metric Policy Optimization Problems
Victor D. Dorobantu, Kamyar Azizzadenesheli, Yisong Yue
We study policy optimization problems for deterministic Markov decision processes (MDPs) with metric state and action spaces, which we refer to as Metric Policy Optimization Proble…
Safety-Aware Preference-Based Learning for Safety-Critical Control
Ryan K. Cosner, Maegan Tucker, Andrew J. Taylor +7
Bringing dynamic robots into the wild requires a tenuous balance between performance and safety. Yet controllers designed to provide robust safety guarantees often result in conser…