4 papers
From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual Matching
Ruqi Bai, Yao Ji, Zeyu Zhou +1
Models that learn spurious correlations from training data often fail when deployed in new environments. While many methods aim to learn invariant representations to address this,…
Counterfactual Fairness by Combining Factual and Counterfactual Predictions
Zeyu Zhou, Tianci Liu, Ruqi Bai +3
In high-stake domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns. This work focuses on Counterfactual…
Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models
Zeyu Zhou, Ruqi Bai, Sean Kulinski +2
Answering counterfactual queries has important applications such as explainability, robustness, and fairness but is challenging when the causal variables are unobserved and the obs…
Benchmarking Algorithms for Federated Domain Generalization
Ruqi Bai, Saurabh Bagchi, David I. Inouye
While prior domain generalization (DG) benchmarks consider train-test dataset heterogeneity, we evaluate Federated DG which introduces federated learning (FL) specific challenges.…