78 citations · 121 across the 12 of their papers we have counts for
8 papers · 1 filter
Hidden symmetries of ReLU networks
J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick
The parameter space for any fixed architecture of feedforward ReLU neural networks serves as a proxy during training for the associated class of functions - but how faithful is thi…
PGrad: Learning Principal Gradients For Domain Generalization
Zhe Wang, Jake Grigsby, Yanjun Qi
Machine learning models fail to perform when facing out-of-distribution (OOD) domains, a challenging task known as domain generalization (DG). In this work, we develop a novel DG t…
Launchpad: Learning to Schedule Using Offline and Online RL Methods
Vanamala Venkataswamy, Jake Grigsby, Andrew Grimshaw +1
Deep reinforcement learning algorithms have succeeded in several challenging domains. Classic Online RL job schedulers can learn efficient scheduling strategies but often takes tho…
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets
Jake Grigsby, Yanjun Qi
Recent Offline Reinforcement Learning methods have succeeded in learning high-performance policies from fixed datasets of experience. A particularly effective approach learns to fi…
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning
Zhe Wang, Jake Grigsby, Arshdeep Sekhon +1
Optimization-based meta-learning typically assumes tasks are sampled from a single distribution - an assumption oversimplifies and limits the diversity of tasks that meta-learning…
Long-Range Transformers for Dynamic Spatiotemporal Forecasting
Jake Grigsby, Zhe Wang, Nam Nguyen +1
Multivariate time series forecasting focuses on predicting future values based on historical context. State-of-the-art sequence-to-sequence models rely on neural attention between…
Towards Automatic Actor-Critic Solutions to Continuous Control
Jake Grigsby, Jin Yong Yoo, Yanjun Qi
Model-free off-policy actor-critic methods are an efficient solution to complex continuous control tasks. However, these algorithms rely on a number of design tricks and hyperparam…
Measuring Visual Generalization in Continuous Control from Pixels
Jake Grigsby, Yanjun Qi
Self-supervised learning and data augmentation have significantly reduced the performance gap between state and image-based reinforcement learning agents in continuous control task…