236 citations · 284 across the 18 of their papers we have counts for
6 papers · 1 filter
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Jiashuo Liu, Jiayun Wu, Tianyu Wang +3
Machine learning algorithms minimizing average risk are susceptible to distributional shifts. Distributionally Robust Optimization (DRO) addresses this issue by optimizing the wors…
Rethinking Distribution Shifts: Empirical Analysis and Modeling for Tabular Data
Tianyu Wang, Jiashuo Liu, Peng Cui +1
Different distribution shifts require different interventions, and algorithms must be grounded in the specific shifts they address. However, methodological development for robust a…
Meta Adaptive Task Sampling for Few-Domain Generalization
Zheyan Shen, Han Yu, Peng Cui +4
To ensure the out-of-distribution (OOD) generalization performance, traditional domain generalization (DG) methods resort to training on data from multiple sources with different u…
Exploring and Exploiting Data Heterogeneity in Recommendation
Zimu Wang, Jiashuo Liu, Hao Zou +4
Massive amounts of data are the foundation of data-driven recommendation models. As an inherent nature of big data, data heterogeneity widely exists in real-world recommendation sy…
Rethinking the Evaluation Protocol of Domain Generalization
Han Yu, Xingxuan Zhang, Renzhe Xu +3
Domain generalization aims to solve the challenge of Out-of-Distribution (OOD) generalization by leveraging common knowledge learned from multiple training domains to generalize to…
Predictive Heterogeneity: Measures and Applications
Jiashuo Liu, Jiayun Wu, Bo Li +1
As an intrinsic and fundamental property of big data, data heterogeneity exists in a variety of real-world applications, such as precision medicine, autonomous driving, financial a…