Nathan Kallus
Title
Cited by
Cited by
Year
Data-driven robust optimization
D Bertsimas, V Gupta, N Kallus
Mathematical Programming 167 (2), 235-292, 2018
3662018
From predictive to prescriptive analytics
D Bertsimas, N Kallus
Management Science 66 (3), 1025-1044, 2020
190*2020
Robust sample average approximation
D Bertsimas, V Gupta, N Kallus
Mathematical Programming 171 (1), 217-282, 2018
120*2018
Predicting crowd behavior with big public data
N Kallus
Proceedings of the 23rd International Conference on World Wide Web, 625-630, 2014
902014
Balanced policy evaluation and learning
N Kallus
Advances in neural information processing systems 31, 2018
812018
Personalized diabetes management using electronic medical records
D Bertsimas, N Kallus, AM Weinstein, YD Zhuo
Diabetes care 40 (2), 210-217, 2017
742017
Generalized optimal matching methods for causal inference.
N Kallus
Journal of Machine Learning Research 21 (62), 1-54, 2020
66*2020
Recursive partitioning for personalization using observational data
N Kallus
International Conference on Machine Learning, 1789-1798, 2017
64*2017
The power of optimization over randomization in designing experiments involving small samples
D Bertsimas, M Johnson, N Kallus
Operations Research 63 (4), 868-876, 2015
602015
Confounding-robust policy improvement
N Kallus, A Zhou
arXiv preprint arXiv:1805.08593, 2018
592018
Policy evaluation and optimization with continuous treatments
N Kallus, A Zhou
International Conference on Artificial Intelligence and Statistics, 1243-1251, 2018
542018
Fairness under unawareness: Assessing disparity when protected class is unobserved
J Chen, N Kallus, X Mao, G Svacha, M Udell
Proceedings of the conference on fairness, accountability, and transparency …, 2019
522019
Residual unfairness in fair machine learning from prejudiced data
N Kallus, A Zhou
International Conference on Machine Learning, 2439-2448, 2018
512018
Optimal a priori balance in the design of controlled experiments
N Kallus
arXiv preprint arXiv:1312.0531, 2013
472013
Double reinforcement learning for efficient off-policy evaluation in markov decision processes
N Kallus, M Uehara
Journal of Machine Learning Research 21 (167), 1-63, 2020
452020
Efficiently breaking the curse of horizon: Double reinforcement learning in infinite-horizon processes
N Kallus, M Uehara
arXiv preprint arXiv:1909.05850, 2019
35*2019
Dynamic assortment personalization in high dimensions
N Kallus, M Udell
arXiv preprint arXiv:1610.05604, 2016
312016
Inventory management in the era of big data
D Bertsimas, N Kallus, A Hussain
Production and Operations Management 25 (12), 2006-2009, 2016
302016
Deep generalized method of moments for instrumental variable analysis
A Bennett, N Kallus, T Schnabel
arXiv preprint arXiv:1905.12495, 2019
282019
Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning
N Kallus, M Uehara
arXiv preprint arXiv:1906.03735, 2019
272019
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