Cikkek nyilvánosan hozzáférhető megbízással - Nathan KallusTovábbi információ
Valahol hozzáférhető: 59
From predictive to prescriptive analytics
D Bertsimas, N Kallus
Management Science 66 (3), 1025-1044, 2020
Megbízások: US National Science Foundation
Data-driven robust optimization
D Bertsimas, V Gupta, N Kallus
Mathematical Programming 167, 235-292, 2018
Megbízások: US National Science Foundation
Robust sample average approximation
D Bertsimas, V Gupta, N Kallus
Mathematical Programming 171 (1), 217-282, 2018
Megbízások: US National Science Foundation
Balanced policy evaluation and learning
N Kallus
Advances in neural information processing systems 31, 2018
Megbízások: US National Science Foundation
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
Megbízások: US National Science Foundation
Assessing algorithmic fairness with unobserved protected class using data combination
N Kallus, X Mao, A Zhou
Management Science 68 (3), 1959-1981, 2022
Megbízások: US National Science Foundation
Confounding-robust policy improvement
N Kallus, A Zhou
Advances in neural information processing systems 31, 2018
Megbízások: US National Science Foundation, US Department of Defense
Residual unfairness in fair machine learning from prejudiced data
N Kallus, A Zhou
International Conference on Machine Learning, 2439-2448, 2018
Megbízások: US National Science Foundation, US Department of Defense
Personalized diabetes management using electronic medical records
D Bertsimas, N Kallus, AM Weinstein, YD Zhuo
Diabetes care 40 (2), 210-217, 2017
Megbízások: US National Science Foundation
Deep generalized method of moments for instrumental variable analysis
A Bennett, N Kallus, T Schnabel
Advances in neural information processing systems 32, 2019
Megbízások: US National Science Foundation
Policy evaluation and optimization with continuous treatments
N Kallus, A Zhou
International conference on artificial intelligence and statistics, 1243-1251, 2018
Megbízások: US National Science Foundation, US Department of Defense
Removing hidden confounding by experimental grounding
N Kallus, AM Puli, U Shalit
Advances in neural information processing systems 31, 2018
Megbízások: US National Science Foundation
Generalization bounds and representation learning for estimation of potential outcomes and causal effects
FD Johansson, U Shalit, N Kallus, D Sontag
Journal of Machine Learning Research 23 (166), 1-50, 2022
Megbízások: US National Science Foundation, US Department of Defense, Knut and Alice …
Dynamic assortment personalization in high dimensions
N Kallus, M Udell
Operations Research 68 (4), 1020-1037, 2020
Megbízások: US National Science Foundation, US Department of Defense
Efficiently breaking the curse of horizon: Double reinforcement learning in infinite-horizon processes
N Kallus, M Uehara
arXiv preprint arXiv:1909.05850 2061, 2019
Megbízások: US National Science Foundation
Deepmatch: Balancing deep covariate representations for causal inference using adversarial training
N Kallus
International Conference on Machine Learning, 5067-5077, 2020
Megbízások: US National Science Foundation
The fairness of risk scores beyond classification: Bipartite ranking and the xauc metric
N Kallus, A Zhou
Advances in neural information processing systems 32, 2019
Megbízások: US National Science Foundation
Causal inference with noisy and missing covariates via matrix factorization
N Kallus, X Mao, M Udell
Advances in neural information processing systems 31, 2018
Megbízások: US National Science Foundation, US Department of Defense
Stochastic optimization forests
N Kallus, X Mao
Management Science 69 (4), 1975-1994, 2023
Megbízások: US National Science Foundation
Minimax-optimal policy learning under unobserved confounding
N Kallus, A Zhou
Management Science 67 (5), 2870-2890, 2021
Megbízások: US National Science Foundation, US Department of Defense
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