236 citations · 284 across the 18 of their papers we have counts for
5 papers · 1 filter
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…
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…
A Survey on Evaluation of Out-of-Distribution Generalization
Han Yu, Jiashuo Liu, Xingxuan Zhang +2
Machine learning models, while progressively advanced, rely heavily on the IID assumption, which is often unfulfilled in practice due to inevitable distribution shifts. This render…