Berk Ustun

Research Interests

Areas
Machine Learning, Optimization, Human-Centered Design
Topics
Algorithmic Fairness, Interpretability, Personalization, Uncertainty Quantification
Applications
Medicine, Consumer Finance, Criminal Justice, Governance

Recent Papers

Participatory Systems for Classification

Hailey Joren, Chirag Nagpal, Katherine Heller, Berk Ustun

NeurIPS

Neural Information Processing Systems, 2023

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

Vinith M. Suriyakumar, Marzyeh Ghassemi, Berk Ustun

ICML

International Conference on Machine Learning, 2023

Predictive Multiplicity in Probabilistic Classification

Jamelle Watson-Daniels, David Parkes, Berk Ustun

AAAI

Association for Advancement in Artificial Intelligence, 2023

On the Epistemic Limits of Personalized Prediction

Lucas Monteiro Paes, Carol Long, Berk Ustun, Flavio Calmon

NeurIPS

Neural Information Processing Systems, 2022

Rank List Sensitivity of Recommender Systems to Interaction Perturbations

Sejoon Oh, Berk Ustun, Julian McAuley, Srijan Kumar

CIKM

Conference on Information and Knowledge Management, 2022

Optimized Short-Forms of the Cognitive Distortions Questionnaire

Amanda Morrison, Berk Ustun, Arielle Horenstein, Simona Kaplan, Irismar Reis de Oliveira, Sedat Batmaz, James Gross, Ekaterina Sadikova, Curt Hemanny, Pedro Pires, Philippe Goldin, Ronald Kessler, Richard G. Heimberg

Journal of Anxiety Disorders, 2022

Machine Learning Methods

Learning Optimal Predictive Checklists

Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi

NeurIPS

Neural Information Processing Systems, 2020

Predictive Multiplicity in Classification

Charles Marx, Flavio du Pin Calmon, Berk Ustun

ICML

International Conference on Machine Learning, 2020

Actionable Recourse in Linear Classification

Berk Ustun, Alexander Spangher, Yang Liu

FAT*

ACM Conference on Fairness, Accountability and Transparency, 2019

Learning Optimized Risk Scores

Berk Ustun, Cynthia Rudin

JMLR

Journal of Machine Learning Research, 2019

Fairness without Harm: Decoupled Classifiers with Preference Guarantees

Berk Ustun, Yang Liu, David Parkes

ICML

International Conference on Machine Learning, 2019

Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions

Hao Wang, Berk Ustun, Flavio du Pin Calmon

ICML

International Conference on Machine Learning, 2019

The Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in ML

Hao Wang, Berk Ustun, Flavio du Pin Calmon

ISIT

IEEE International Symposium on Information Theory, 2018

Optimized Risk Scores

Berk Ustun, Cynthia Rudin

KDD

Knowledge Discovery and Data Mining, 2017

Supersparse Linear Integer Models for Optimized Medical Scoring Systems

Berk Ustun, Cynthia Rudin

MLJ

Machine Learning, 2015

Supersparse Linear Integer Models for Predictive Scoring Systems

Berk Ustun, Stefano Tracà, Cynthia Rudin

AAAI Late Breaking Track, 2013

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

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

INFORMS Journal of Computing, 2015

Machine Learning Applications

Developing an Optimal Short-Form of the PTSD Checklist for DSM-5

Kelly Zuromski, Berk Ustun, Irving Hwang, Terence Keane, Brian Marx, Murray Stein, Robert Ursano, Ronald Kessler

Depression and Anxiety, 2019

Optimized Scoring Systems: Towards Trust in ML for Healthcare and Criminal Justice

Cynthia Rudin, Berk Ustun

Interfaces, 2018

The World Health Organization Adult ADHD Self-Report Scale Screening Scale for DSM-5

Berk Ustun, Lenard Adler, Cynthia Rudin, Stephen Faraone, Thomas Spencer, Patricia Berglund, Michael Gruber, Ronald Kessler

JAMA Psychiatry, 2017

Association of an EEG-Based Risk Score With Seizure Probability in Hospitalized Patients

Aaron Struck, Berk Ustun, Andres Rodriguez Ruiz, Jong Woo Lee, Suzette LaRoche, Lawrence J. Hirsch, Emily J. Gilmore, Jan Vlachy,
Hiba Arif Haider, Cynthia Rudin, M. Brandon Westover

JAMA Neurology, 2017