1 citations · 1 across the 1 of their papers we have counts for
5 papers
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…
Error Slice Discovery via Manifold Compactness
Han Yu, Hao Zou, Jiashuo Liu +4
Despite the great performance of deep learning models in many areas, they still make mistakes and underperform on certain subsets of data, i.e. error slices. Given a trained model,…
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…
DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
Jiashuo Liu, Tianyu Wang, Henry Lam +2
We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulation…
LLM Embeddings Improve Test-time Adaptation to Tabular -Shifts
Yibo Zeng, Jiashuo Liu, Henry Lam +1
For tabular datasets, the change in the relationship between the label and covariates (-shifts) is common due to missing variables (a.k.a. confounders). Since it is impossible…