Berk Ustun

According to our database, Berk Ustun authored at least 33 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning.

Sejoon Oh, Berk Ustun, Julian J. McAuley, Srijan Kumar

ACM Trans. Knowl. Discov. Data, November, 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, Jinny G. Park, Jessica K. Paulus, Joyce Sackey, Richard Sharp, Ewout W. Steyerberg, Berk Ustun, David van Klaveren, Saul N. Weingart, David M. Kent

npj Digit. Medicine, 2024

Concept Bottleneck Large Language Models.

Chung-En Sun, Tuomas P. Oikarinen, Berk Ustun, Tsui-Wei Weng

CoRR, 2024

Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse.

Seung Hyun Cheon, Anneke Wernerfelt, Sorelle A. Friedler, 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, Rahul Thapa, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason A. Fries, Parisa Rashidi, Brett K. Beaulieu-Jones, Xuhai Orson Xu, Matthew B. A. McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gürsoy, Marzyeh Ghassemi, Emma Pierson, George H. Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo

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

Learning from Time Series under Temporal Label Noise.

Sujay Nagaraj, Walter Gerych, Sana Tonekaboni, Anna Goldenberg, Berk Ustun, Thomas Hartvigsen

CoRR, 2024

Prediction without Preclusion: Recourse Verification with Reachable Sets.

Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng, Berk Ustun

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Classification with Conceptual Safeguards.

Hailey Joren, Charles T. Marx, Berk Ustun

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Operationalizing the Search for Less Discriminatory Alternatives in Fair Lending.

Talia B. Gillis, Vitaly Meursault, Berk Ustun

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

Providing Fair Recourse over Plausible Groups.

Jayanth Yetukuri, Ian Hardy, Yevgeniy Vorobeychik, Berk Ustun, Yang Liu

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Participatory Systems for Personalized Prediction.

Hailey James, Chirag Nagpal, Katherine A. Heller, Berk Ustun

CoRR, 2023

Participatory Personalization in Classification.

Hailey Joren, Chirag Nagpal, Katherine A. Heller, Berk Ustun

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction.

Vinith Menon Suriyakumar, Marzyeh Ghassemi, Berk Ustun

Proceedings of the International Conference on Machine Learning, 2023

Algorithmic Censoring in Dynamic Learning Systems.

Jennifer Chien, Margaret E. Roberts, Berk Ustun

Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023

Predictive Multiplicity in Probabilistic Classification.

Jamelle Watson-Daniels, David C. Parkes, Berk Ustun

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction.

Vinith M. Suriyakumar, Marzyeh Ghassemi, Berk Ustun

CoRR, 2022

On the Epistemic Limits of Personalized Prediction.

Lucas Monteiro Paes, Carol Xuan Long, Berk Ustun, Flávio P. Calmon

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Rank List Sensitivity of Recommender Systems to Interaction Perturbations.

Sejoon Oh, Berk Ustun, Julian J. McAuley, Srijan Kumar

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

Learning Optimal Predictive Checklists.

Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Predictive Multiplicity in Classification.

Charles T. Marx, Flávio P. Calmon, Berk Ustun

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Learning Optimized Risk Scores.

Berk Ustun, Cynthia Rudin

J. Mach. Learn. Res., 2019

Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions.

Hao Wang, Berk Ustun, Flávio P. Calmon

Proceedings of the 36th International Conference on Machine Learning, 2019

Fairness without Harm: Decoupled Classifiers with Preference Guarantees.

Berk Ustun, Yang Liu, David C. Parkes

Proceedings of the 36th International Conference on Machine Learning, 2019

Actionable Recourse in Linear Classification.

Berk Ustun, Alexander Spangher, Yang Liu

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

2018

Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice.

Cynthia Rudin, Berk Ustun

Interfaces, 2018

On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning.

Hao Wang, Berk Ustun, Flávio P. Calmon

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

2017

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

Berk Ustun

PhD thesis, 2017

Optimized Risk Scores.

Berk Ustun, Cynthia Rudin

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

2016

Supersparse linear integer models for optimized medical scoring systems.

Berk Ustun, Cynthia Rudin

Mach. Learn., 2016

2015

Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach.

Panos Parpas, Berk Ustun, Mort Webster, Quang Kha Tran

INFORMS J. Comput., 2015

2014

Methods and Models for Interpretable Linear Classification.

Berk Ustun, Cynthia Rudin

CoRR, 2014

2013

Supersparse Linear Integer Models for Predictive Scoring Systems.

Berk Ustun, Stefano Tracà, Cynthia Rudin

Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013