51 citations · 143 across the 8 of their papers we have counts for
8 papers
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding
Junda Wu, Tong Yu, Rui Wang +6
Soft prompt tuning achieves superior performances across a wide range of few-shot tasks. However, the performances of prompt tuning can be highly sensitive to the initialization of…
Action-Sufficient State Representation Learning for Control with Structural Constraints
Biwei Huang, Chaochao Lu, Liu Leqi +4
Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information requ…
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
Biwei Huang, Fan Feng, Chaochao Lu +2
One practical challenge in reinforcement learning (RL) is how to make quick adaptations when faced with new environments. In this paper, we propose a principled framework for adapt…
Nonlinear Invariant Risk Minimization: A Causal Approach
Chaochao Lu, Yuhuai Wu, Jośe Miguel Hernández-Lobato +1
Due to spurious correlations, machine learning systems often fail to generalize to environments whose distributions differ from the ones used at training time. Prior work addressin…
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
Chaochao Lu, Biwei Huang, Ke Wang +3
Reinforcement learning (RL) algorithms usually require a substantial amount of interaction data and perform well only for specific tasks in a fixed environment. In some scenarios s…
Interpreting Spatially Infinite Generative Models
Chaochao Lu, Richard E. Turner, Yingzhen Li +1
Traditional deep generative models of images and other spatial modalities can only generate fixed sized outputs. The generated images have exactly the same resolution as the traini…