| A general framework for counterfactual learning-to-rank A Agarwal, K Takatsu, I Zaitsev, T Joachims Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 124 | 2019 |
| Causal inference using Gaussian processes with structured latent confounders S Witty, K Takatsu, D Jensen, V Mansinghka International Conference on Machine Learning, 10313-10323, 2020 | 25 | 2020 |
| Debiased inference for a covariate-adjusted regression function K Takatsu, T Westling arXiv preprint arXiv:2210.06448, 2022 | 8 | 2022 |
| Resilience of Supervised Learning Algorithms to Discriminatory Data Perturbations PA Grabowicz, N Perello, K Takatsu arXiv e-prints, arXiv: 1912.08189, 2019 | 2 | 2019 |
| Doubly robust machine learning for an instrumental variable study of surgical care for cholecystitis K Takatsu, AW Levis, E Kennedy, R Kelz, L Keele arXiv preprint arXiv:2307.06269, 2023 | 1 | 2023 |
| U-statistics for importance-weighted variational inference J Burroni, K Takatsu, J Domke, D Sheldon arXiv preprint arXiv:2302.13918, 2023 | 1 | 2023 |
| Learning from Discriminatory Training Data P Grabowicz, N Perello, K Takatsu Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 752-763, 2023 | | 2023 |
| Semiparametric shape-restricted estimators for nonparametric regression K Takatsu, T Zhang, AK Kuchibhotla arXiv preprint arXiv:2307.05732, 2023 | | 2023 |
| Nonparametric causal inference with a continuous exposure T Westling, K Takatsu 2022 Fall Eastern Sectional Meeting, 0 | | |
Ted WestlingAssistant Professor, University of Massachusetts AmherstVerified email at math.umass.edu
