51 citations · 143 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
Deconfounding Reinforcement Learning in Observational Settings
Chaochao Lu, Bernhard Schölkopf, José Miguel Hernández-Lobato
We propose a general formulation for addressing reinforcement learning (RL) problems in settings with observational data. That is, we consider the problem of learning good policies…