8 citations · 11 across the 5 of their papers we have counts for
5 papers
USE: Dynamic User Modeling with Stateful Sequence Models
Zhihan Zhou, Qixiang Fang, Leonardo Neves +5
User embeddings play a crucial role in user engagement forecasting and personalized services. Recent advances in sequence modeling have sparked interest in learning user embeddings…
Combating Representation Learning Disparity with Geometric Harmonization
Zhihan Zhou, Jiangchao Yao, Feng Hong +3
Self-supervised learning (SSL) as an effective paradigm of representation learning has achieved tremendous success on various curated datasets in diverse scenarios. Nevertheless, w…
Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty
Guanlin Liu, Zhihan Zhou, Han Liu +1
Robust reinforcement learning (RL) aims to find a policy that optimizes the worst-case performance in the face of uncertainties. In this paper, we focus on action robust RL with th…
Latent Class-Conditional Noise Model
Jiangchao Yao, Bo Han, Zhihan Zhou +2
Learning with noisy labels has become imperative in the Big Data era, which saves expensive human labors on accurate annotations. Previous noise-transition-based methods have achie…
Long-Tailed Partial Label Learning via Dynamic Rebalancing
Feng Hong, Jiangchao Yao, Zhihan Zhou +2
Real-world data usually couples the label ambiguity and heavy imbalance, challenging the algorithmic robustness of partial label learning (PLL) and long-tailed learning (LT). The s…