Berk Ustun

Assistant Professor@University of California, San Diego·美国·OpenReview

研究方向

personalization · model multiplicity · algorithmic recourse · algorithmic fairness · machine learning · interpretability · classification · discrete optimization · healthcare

7.0

合作者 (20)

Berk Ustun

Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse

Regretful Decisions under Label Noise

Learning under Temporal Label Noise

Concept Bottleneck Large Language Models

Understanding Fixed Predictions via Confined Regions

Selective Preference Aggregation

Classification with Conceptual Safeguards

Prediction without Preclusion: Recourse Verification with Reachable Sets

Learning with Temporal Label Noise