Ryan Feng — accepted AI-conference papers

2025

Test-Time Canonicalization by Foundation Models for Robust Perception

Utkarsh Singhal, Ryan Feng, Stella X. Yu, Atul Prakash

ICML 2025poster

Real-world visual perception requires invariance to diverse transformations, yet current methods rely heavily on specialized architectures or training on predefined augmentations, limiting generalization. We propose FoCal, a test-time, data-driven framework that achieves robust perception by leverag…

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2023

Concept-based Explanations for Out-of-Distribution Detectors

Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha, Atul Prakash

ICML 2023poster

Out-of-distribution (OOD) detection plays a crucial role in ensuring the safe deployment of deep neural network (DNN) classifiers. While a myriad of methods have focused on improving the performance of OOD detectors, a critical gap remains in interpreting their decisions. We help bridge this gap by…

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