212 citations · 290 across the 5 of their papers we have counts for
7 papers
Domain Adaptation with Factorizable Joint Shift
Hao He, Yuzhe Yang, Hao Wang
Existing domain adaptation (DA) usually assumes the domain shift comes from either the covariates or the labels. However, in real-world applications, samples selected from differen…
Delving into Deep Imbalanced Regression
Yuzhe Yang, Kaiwen Zha, Ying-Cong Chen +2
Real-world data often exhibit imbalanced distributions, where certain target values have significantly fewer observations. Existing techniques for dealing with imbalanced data focu…
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation
Devavrat Shah, Dogyoon Song, Zhi Xu +1
We consider the question of learning -function in a sample efficient manner for reinforcement learning with continuous state and action spaces under a generative model. If -f…
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
Yuzhe Yang, Zhi Xu
Real-world data often exhibits long-tailed distributions with heavy class imbalance, posing great challenges for deep recognition models. We identify a persisting dilemma on the va…
Harnessing Structures for Value-Based Planning and Reinforcement Learning
Yuzhe Yang, Guo Zhang, Zhi Xu +1
Value-based methods constitute a fundamental methodology in planning and deep reinforcement learning (RL). In this paper, we propose to exploit the underlying structures of the sta…
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Yuzhe Yang, Guo Zhang, Dina Katabi +1
Deep neural networks are vulnerable to adversarial attacks. The literature is rich with algorithms that can easily craft successful adversarial examples. In contrast, the performan…