492 citations · 1.5k across the 62 of their papers we have counts for
10 papers · 2 filters
The Effect of Model Size on Worst-Group Generalization
Alan Pham, Eunice Chan, Vikranth Srivatsa +6
Overparameterization is shown to result in poor test accuracy on rare subgroups under a variety of settings where subgroup information is known. To gain a more complete picture, we…
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks
Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov +2
Goal-conditioned reinforcement learning (RL) can solve tasks in a wide range of domains, including navigation and manipulation, but learning to reach distant goals remains a centra…
Accelerating Quadratic Optimization with Reinforcement Learning
Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato +6
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapid…
Taxonomizing local versus global structure in neural network loss landscapes
Yaoqing Yang, Liam Hodgkinson, Ryan Theisen +4
Viewing neural network models in terms of their loss landscapes has a long history in the statistical mechanics approach to learning, and in recent years it has received attention…
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks
Albert Wilcox, Ashwin Balakrishna, Brijen Thananjeyan +2
Reinforcement learning (RL) has shown impressive success in exploring high-dimensional environments to learn complex tasks, but can often exhibit unsafe behaviors and require exten…
MADE: Exploration via Maximizing Deviation from Explored Regions
Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao +3
In online reinforcement learning (RL), efficient exploration remains particularly challenging in high-dimensional environments with sparse rewards. In low-dimensional environments,…