Supersparse Linear Integer Models for Predictive Scoring Systems. - Berk Ustun; Stefano Tracà; Cynthia Rudin (AAAI (Late-Breaking Developments), 2013)

Methods and Models for Interpretable Linear Classification. - Berk Ustun; Cynthia Rudin (CoRR, 2014)

Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach. - Panos Parpas; Berk Ustun; Mort Webster; Quang Kha Tran (INFORMS J. Comput., 2015)

Supersparse linear integer models for optimized medical scoring systems. - Berk Ustun; Cynthia Rudin (Mach. Learn., 2016)

Optimized Risk Scores. - Berk Ustun; Cynthia Rudin (KDD, 2017)

Simple linear classifiers via discrete optimization: learning certifiably optimal scoring systems for decision-making and risk assessment. - Berk Ustun (2017)

On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning. - Hao Wang 063; Berk Ustun; Flávio P. Calmon (ISIT, 2018)

Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice. - Cynthia Rudin; Berk Ustun (Interfaces, 2018)

Actionable Recourse in Linear Classification. - Berk Ustun; Alexander Spangher; Yang Liu 018 (FAT, 2019)

Fairness without Harm: Decoupled Classifiers with Preference Guarantees. - Berk Ustun; Yang Liu 018; David C. Parkes (ICML, 2019)

Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions. - Hao Wang 063; Berk Ustun; Flávio P. Calmon (ICML, 2019)

Learning Optimized Risk Scores. - Berk Ustun; Cynthia Rudin (J. Mach. Learn. Res., 2019)

Predictive Multiplicity in Classification. - Charles T. Marx; Flávio P. Calmon; Berk Ustun (ICML, 2020)

Learning Optimal Predictive Checklists. - Haoran Zhang 003; Quaid Morris; Berk Ustun; Marzyeh Ghassemi (NeurIPS, 2021)

Rank List Sensitivity of Recommender Systems to Interaction Perturbations. - Sejoon Oh; Berk Ustun; Julian J. McAuley; Srijan Kumar (CIKM, 2022)

On the Epistemic Limits of Personalized Prediction. - Lucas Monteiro Paes; Carol Xuan Long; Berk Ustun; Flávio P. Calmon (NeurIPS, 2022)

When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction. - Vinith M. Suriyakumar; Marzyeh Ghassemi; Berk Ustun (CoRR, 2022)

Predictive Multiplicity in Probabilistic Classification. - Jamelle Watson-Daniels; David C. Parkes; Berk Ustun (AAAI, 2023)

Algorithmic Censoring in Dynamic Learning Systems. - Jennifer Chien; Margaret E. Roberts; Berk Ustun (EAAMO, 2023)

When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction. - Vinith Menon Suriyakumar; Marzyeh Ghassemi; Berk Ustun (ICML, 2023)

Participatory Personalization in Classification. - Hailey Joren; Chirag Nagpal; Katherine A. Heller; Berk Ustun (NeurIPS, 2023)

Participatory Systems for Personalized Prediction. - Hailey James; Chirag Nagpal; Katherine A. Heller; Berk Ustun (CoRR, 2023)

Providing Fair Recourse over Plausible Groups. - Jayanth Yetukuri; Ian Hardy; Yevgeniy Vorobeychik; Berk Ustun; Yang Liu 018 (AAAI, 2024)

Operationalizing the Search for Less Discriminatory Alternatives in Fair Lending. - Talia B. Gillis; Vitaly Meursault; Berk Ustun (FAccT, 2024)

Classification with Conceptual Safeguards. - Hailey Joren; Charles T. Marx; Berk Ustun (ICLR, 2024)

Prediction without Preclusion: Recourse Verification with Reachable Sets. - Avni Kothari; Bogdan Kulynych; Tsui-Wei Weng; Berk Ustun (ICLR, 2024)

Learning from Time Series under Temporal Label Noise. - Sujay Nagaraj; Walter Gerych; Sana Tonekaboni; Anna Goldenberg; Berk Ustun; Thomas Hartvigsen (CoRR, 2024)

Predictive Churn with the Set of Good Models. - Jamelle Watson-Daniels; Flávio du Pin Calmon; Alexander D'Amour; Carol Xuan Long; David C. Parkes; Berk Ustun (CoRR, 2024)

Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium. - Hyewon Jeong; Sarah Jabbour; Yuzhe Yang 003; Rahul Thapa; Hussein Mozannar; William Jongwon Han; Nikita Mehandru; Michael Wornow; Vladislav Lialin; Xin Liu 034; Alejandro Lozano; Jiacheng Zhu; et al. (CoRR, 2024)

Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor. - Keren Ladin; John Cuddeback; O. Kenrik Duru; Sharad Goel; William Harvey 003; Jinny G. Park; Jessica K. Paulus; Joyce Sackey; Richard Sharp; Ewout W. Steyerberg; Berk Ustun; David van Klaveren; et al. (npj Digit. Medicine, 2024)

FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning. - Sejoon Oh; Berk Ustun; Julian J. McAuley; Srijan Kumar (ACM Trans. Knowl. Discov. Data, 2024)

Discrimination Exposed? On the Reliability of Explanations for Discrimination Detection. - Julian Skirzynski; David Danks; Berk Ustun (FAccT, 2025)

Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse. - Seung Hyun Cheon; Anneke Wernerfelt; Sorelle A. Friedler; Berk Ustun (ICLR, 2025)

Regretful Decisions under Label Noise. - Sujay Nagaraj; Yang Liu 018; Flávio P. Calmon; Berk Ustun (ICLR, 2025)

Learning under Temporal Label Noise. - Sujay Nagaraj; Walter Gerych; Sana Tonekaboni; Anna Goldenberg; Berk Ustun; Thomas Hartvigsen (ICLR, 2025)

Concept Bottleneck Large Language Models. - Chung-En Sun; Tuomas P. Oikarinen; Berk Ustun; Tsui-Wei Weng (ICLR, 2025)

Understanding Fixed Predictions via Confined Regions. - Connor Lawless; Tsui-Wei Weng; Berk Ustun; Madeleine Udell (CoRR, 2025)

Explanations are a means to an end. - Jessica Hullman; Ziyang Guo; Berk Ustun (CoRR, 2025)

Statistical Inference for Responsiveness Verification. - Seung Hyun Cheon; Meredith Stewart; Bogdan Kulynych; Tsui-Wei Weng; Berk Ustun (CoRR, 2025)

Bridging Prediction and Intervention Problems in Social Systems. - Lydia T. Liu; Inioluwa Deborah Raji; Angela Zhou; Luke Guerdan; Jessica Hullman; Daniel Malinsky; Bryan Wilder; Simone Zhang; Hammaad Adam; Amanda Coston; Benjamin Laufer; Ezinne Nwankwo; et al. (CoRR, 2025)

Observational Multiplicity. - Erin George; Deanna Needell; Berk Ustun (CoRR, 2025)