11 citations · 18 across the 5 of their papers we have counts for
5 papers
Data Heterogeneity Modeling for Trustworthy Machine Learning
Jiashuo Liu, Peng Cui
Data heterogeneity plays a pivotal role in determining the performance of machine learning (ML) systems. Traditional algorithms, which are typically designed to optimize average pe…
Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
Weihuang Zheng, Jiashuo Liu, Jiaxing Li +3
Graph Neural Networks (GNNs) are widely used for node classification tasks but often fail to generalize when training and test nodes come from different distributions, limiting the…
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift
Jiayun Wu, Jiashuo Liu, Peng Cui +1
We establish a new model-agnostic optimization framework for out-of-distribution generalization via multicalibration, a criterion that ensures a predictor is calibrated across a fa…
Stability Evaluation via Distributional Perturbation Analysis
Jose Blanchet, Peng Cui, Jiajin Li +1
The performance of learning models often deteriorates when deployed in out-of-sample environments. To ensure reliable deployment, we propose a stability evaluation criterion based…
Debiased Collaborative Filtering with Kernel-Based Causal Balancing
Haoxuan Li, Chunyuan Zheng, Yanghao Xiao +4
Debiased collaborative filtering aims to learn an unbiased prediction model by removing different biases in observational datasets. To solve this problem, one of the simple and eff…