630 citations · 2.3k across the 48 of their papers we have counts for
22 papers · 1 filter
Minimax Active Learning
Sayna Ebrahimi, William Gan, Dian Chen +5
Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator. Current active learning techniques either…
Temporal Action Detection with Multi-level Supervision
Baifeng Shi, Qi Dai, Judy Hoffman +3
Training temporal action detection in videos requires large amounts of labeled data, yet such annotation is expensive to collect. Incorporating unlabeled or weakly-labeled data to…
Fighting Copycat Agents in Behavioral Cloning from Observation Histories
Chuan Wen, Jierui Lin, Trevor Darrell +2
Imitation learning trains policies to map from input observations to the actions that an expert would choose. In this setting, distribution shift frequently exacerbates the effect…
Auxiliary Task Reweighting for Minimum-data Learning
Baifeng Shi, Judy Hoffman, Kate Saenko +2
Supervised learning requires a large amount of training data, limiting its application where labeled data is scarce. To compensate for data scarcity, one possible method is to util…
Modular Networks for Compositional Instruction Following
Rodolfo Corona, Daniel Fried, Coline Devin +2
Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training.…
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
Bo Li, Yezhen Wang, Shanghang Zhang +4
The success of supervised learning hinges on the assumption that the training and test data come from the same underlying distribution, which is often not valid in practice due to…