2.4k citations · 2.5k across the 23 of their papers we have counts for
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Label Budget Allocation in Multi-Task Learning
Ximeng Sun, Kihyuk Sohn, Kate Saenko +2
The cost of labeling data often limits the performance of machine learning systems. In multi-task learning, related tasks provide information to each other and improve overall perf…
Explaining Reinforcement Learning Policies through Counterfactual Trajectories
Julius Frost, Olivia Watkins, Eric Weiner +4
In order for humans to confidently decide where to employ RL agents for real-world tasks, a human developer must validate that the agent will perform well at test-time. Some policy…
Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee +17
Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of…