198 citations · 1.1k across the 35 of their papers we have counts for
7 papers · 1 filter
Input Convex Gradient Networks
Jack Richter-Powell, Jonathan Lorraine, Brandon Amos
The gradients of convex functions are expressive models of non-trivial vector fields. For example, Brenier's theorem yields that the optimal transport map between any two measures…
Cross-Domain Imitation Learning via Optimal Transport
Arnaud Fickinger, Samuel Cohen, Stuart Russell +1
Cross-domain imitation learning studies how to leverage expert demonstrations of one agent to train an imitation agent with a different embodiment or morphology. Comparing trajecto…
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Arnaud Fickinger, Hengyuan Hu, Brandon Amos +2
Lookahead search has been a critical component of recent AI successes, such as in the games of chess, go, and poker. However, the search methods used in these games, and in many ot…
Neural Fixed-Point Acceleration for Convex Optimization
Shobha Venkataraman, Brandon Amos
Fixed-point iterations are at the heart of numerical computing and are often a computational bottleneck in real-time applications that typically need a fast solution of moderate ac…
Riemannian Convex Potential Maps
Samuel Cohen, Brandon Amos, Yaron Lipman
Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this…
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints
Anselm Paulus, Michal Rolínek, Vít Musil +2
Bridging logical and algorithmic reasoning with modern machine learning techniques is a fundamental challenge with potentially transformative impact. On the algorithmic side, many…
MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
Luis Pineda, Brandon Amos, Amy Zhang +2
Model-based reinforcement learning is a compelling framework for data-efficient learning of agents that interact with the world. This family of algorithms has many subcomponents th…