174 citations · 444 across the 27 of their papers we have counts for
5 papers · 1 filter
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…
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…
Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG
Arshdeep Sekhon, Zhe Wang, Yanjun Qi
Understanding relationships between feature variables is one important way humans use to make decisions. However, state-of-the-art deep learning studies either focus on task-agnost…
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…
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning
Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi +1
In this paper we revisit the idea of pseudo-labeling in the context of semi-supervised learning where a learning algorithm has access to a small set of labeled samples and a large…