19 citations · 30 across the 5 of their papers we have counts for
7 papers
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning
Jongjin Park, Younggyo Seo, Chang Liu +4
Behavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations. However, behavioral cloning often suffers from the causal…
On the Generative Utility of Cyclic Conditionals
Chang Liu, Haoyue Tang, Tao Qin +2
We study whether and how can we model a joint distribution using two conditional models and that form a cycle. This is motivated by the observation that…
Learning Invariant Representations across Domains and Tasks
Jindong Wang, Wenjie Feng, Chang Liu +5
Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledg…
Latent Causal Invariant Model
Xinwei Sun, Botong Wu, Xiangyu Zheng +4
Current supervised learning can learn spurious correlation during the data-fitting process, imposing issues regarding interpretability, out-of-distribution (OOD) generalization, an…
Learning Causal Semantic Representation for Out-of-Distribution Prediction
Chang Liu, Xinwei Sun, Jindong Wang +5
Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes th…
Learning to Match Distributions for Domain Adaptation
Chaohui Yu, Jindong Wang, Chang Liu +5
When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is diff…