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2020 – today
- 2023
[i10]Hailey James, Chirag Nagpal, Katherine A. Heller, Berk Ustun:
Participatory Systems for Personalized Prediction. CoRR abs/2302.03874 (2023)- 2022
- [c9]Sejoon Oh, Berk Ustun, Julian J. McAuley, Srijan Kumar:
Rank List Sensitivity of Recommender Systems to Interaction Perturbations. CIKM 2022: 1584-1594 - [i9]Jamelle Watson-Daniels, David C. Parkes, Berk Ustun:
Predictive Multiplicity in Probabilistic Classification. CoRR abs/2206.01131 (2022) - [i8]Vinith M. Suriyakumar, Marzyeh Ghassemi, Berk Ustun:
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction. CoRR abs/2206.02058 (2022) - 2021
- [c8]Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi:
Learning Optimal Predictive Checklists. NeurIPS 2021: 1215-1229 - [i7]Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi:
Learning Optimal Predictive Checklists. CoRR abs/2112.01020 (2021) - 2020
- [c7]Charles T. Marx, Flávio P. Calmon, Berk Ustun:
Predictive Multiplicity in Classification. ICML 2020: 6765-6774
2010 – 2019
- 2019
- [j4]Berk Ustun, Cynthia Rudin:
Learning Optimized Risk Scores. J. Mach. Learn. Res. 20: 150:1-150:75 (2019) - [c6]Berk Ustun
, Alexander Spangher, Yang Liu:
Actionable Recourse in Linear Classification. FAT 2019: 10-19 - [c5]Berk Ustun, Yang Liu, David C. Parkes:
Fairness without Harm: Decoupled Classifiers with Preference Guarantees. ICML 2019: 6373-6382 - [c4]Hao Wang, Berk Ustun, Flávio P. Calmon:
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions. ICML 2019: 6618-6627 - [i6]Hao Wang, Berk Ustun, Flávio P. Calmon:
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions. CoRR abs/1901.10501 (2019) - [i5]Charles T. Marx, Flávio du Pin Calmon, Berk Ustun:
Predictive Multiplicity in Classification. CoRR abs/1909.06677 (2019) - 2018
- [j3]Cynthia Rudin, Berk Ustun:
Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice. Interfaces 48(5): 449-466 (2018) - [c3]Hao Wang, Berk Ustun, Flávio P. Calmon:
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning. ISIT 2018: 126-130 - [i4]Hao Wang, Berk Ustun, Flávio P. Calmon:
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning. CoRR abs/1801.05398 (2018) - [i3]Berk Ustun, Alexander Spangher, Yang Liu:
Actionable Recourse in Linear Classification. CoRR abs/1809.06514 (2018) - 2017
- [b1]Berk Ustun:
Simple linear classifiers via discrete optimization: learning certifiably optimal scoring systems for decision-making and risk assessment. Massachusetts Institute of Technology, Cambridge, USA, 2017 - [c2]Berk Ustun, Cynthia Rudin:
Optimized Risk Scores. KDD 2017: 1125-1134 - 2016
- [j2]Berk Ustun, Cynthia Rudin:
Supersparse linear integer models for optimized medical scoring systems. Mach. Learn. 102(3): 349-391 (2016) - 2015
- [j1]Panos Parpas, Berk Ustun, Mort Webster, Quang Kha Tran:
Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach. INFORMS J. Comput. 27(2): 358-377 (2015) - [i2]Berk Ustun, Cynthia Rudin:
Supersparse Linear Integer Models for Optimized Medical Scoring Systems. CoRR abs/1502.04269 (2015) - 2014
- [i1]Berk Ustun, Cynthia Rudin:
Methods and Models for Interpretable Linear Classification. CoRR abs/1405.4047 (2014) - 2013
- [c1]Berk Ustun, Stefano Tracà, Cynthia Rudin:
Supersparse Linear Integer Models for Predictive Scoring Systems. AAAI (Late-Breaking Developments) 2013
Coauthor Index
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