6 papers
TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval
Yi-Chung Chen, Philip Jacobson, Tom Lampo +6
Efficiently retrieving relevant clips from large-scale driving logs is essential for data curation, model development, and safety analysis. Structured and rule-based retrieval syst…
Your VAR Model is Secretly an Efficient and Explainable Generative Classifier
Yi-Chung Chen, David I. Inouye, Jing Gao
Generative classifiers, which leverage conditional generative models for classification, have recently demonstrated desirable properties such as robustness to distribution shifts.…
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…
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions
Mai Elkady, Thu Bui, Bruno Ribeiro +1
There has been a growing excitement that implicit graph generative models could be used to design or discover new molecules for medicine or material design. Because these molecules…
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…
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data
Avi Amalanshu, Yash Sirvi, David I. Inouye
Vertical Federated Learning (VFL) is an emergent distributed machine learning paradigm for collaborative learning between clients who have disjoint features of common entities. How…