3 citations · 4 across the 5 of their papers we have counts for
5 papers
GRIT: Out-of-Distribution Robustness from Finite and Noisy Invariant Pairs
Ruqi Bai, Yao Ji, Easton Currie +3
Predictors often exploit spurious correlations that fail under distribution shift, undermining out-of-distribution (OOD) robustness. Invariant pairs, such as the same object photog…
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…
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.…
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…
Exploring Adversarial Examples via Invertible Neural Networks
Ruqi Bai, Saurabh Bagchi, David I. Inouye
Adversarial examples (AEs) are images that can mislead deep neural network (DNN) classifiers via introducing slight perturbations into original images. This security vulnerability…