| Orthogonal Random Forest for Causal Inference M Oprescu, V Syrgkanis, ZS Wu arXiv preprint arXiv:1806.03467, 2018 | 146* | 2018 |
| EconML: a Python package for ML-based heterogeneous treatment effects estimation K Battocchi, E Dillon, M Hei, G Lewis, P Oka, M Oprescu, V Syrgkanis Version 0. x, 2019 | 132 | 2019 |
| Machine learning estimation of heterogeneous treatment effects with instruments V Syrgkanis, V Lei, M Oprescu, M Hei, K Battocchi, G Lewis Advances in Neural Information Processing Systems 32, 2019 | 77 | 2019 |
| Online learning with optimism and delay GE Flaspohler, F Orabona, J Cohen, S Mouatadid, M Oprescu, ... International Conference on Machine Learning, 3363-3373, 2021 | 38 | 2021 |
| Causal inference and machine learning in practice with econml and causalml: Industrial use cases at microsoft, tripadvisor, uber V Syrgkanis, G Lewis, M Oprescu, M Hei, K Battocchi, E Dillon, J Pan, ... Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 32 | 2021 |
| Adaptive bias correction for improved subseasonal forecasting S Mouatadid, P Orenstein, G Flaspohler, J Cohen, M Oprescu, E Fraenkel, ... Nature Communications 14 (1), 3482, 2023 | 27 | 2023 |
| B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding M Oprescu, J Dorn, M Ghoummaid, A Jesson, N Kallus, U Shalit International Conference on Machine Learning (ICML) 202, 26599-26618, 2023 | 22 | 2023 |
| The REgolith X-Ray Imaging Spectrometer (REXIS) for OSIRIS-REx: identifying regional elemental enrichment on asteroids B Allen, J Grindlay, J Hong, RP Binzel, R Masterson, NK Inamdar, ... Optical Modeling and Performance Predictions VI 8840, 142-158, 2013 | 20 | 2013 |
| Flexible and scalable deep learning with MMLSpark M Hamilton, S Raghunathan, A Annavajhala, D Kirsanov, E Leon, ... International Conference on Predictive Applications and APIs, 11-22, 2018 | 17 | 2018 |
| Estimating the long-term effects of novel treatments K Battocchi, E Dillon, M Hei, G Lewis, M Oprescu, V Syrgkanis Advances in Neural Information Processing Systems 34, 2925-2935, 2021 | 14 | 2021 |
| Robust and Agnostic Learning of Conditional Distributional Treatment Effects N Kallus, M Oprescu International Conference on Artificial Intelligence and Statistics, 6037-6060, 2023 | 10 | 2023 |
| EconML: A machine learning library for estimating heterogeneous treatment effects M Oprescu, V Syrgkanis, K Battocchi, M Hei, G Lewis 33rd Conference on Neural Information Processing Systems, 6, 2019 | 8 | 2019 |
| SubseasonalClimateUSA: a dataset for subseasonal forecasting and benchmarking S Mouatadid, P Orenstein, G Flaspohler, M Oprescu, J Cohen, F Wang, ... Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| Learned benchmarks for subseasonal forecasting S Mouatadid, P Orenstein, G Flaspohler, M Oprescu, J Cohen, F Wang, ... arXiv preprint arXiv:2109.10399, 2021 | 7 | 2021 |
| RDataTracker and DDG Explorer-Capture, Visualization and Querying of Provenance from R Scripts. BS Lerner, ER Boose IPAW, 288-290, 2014 | 6 | 2014 |
| Spectroscopic Classification of the SN in NGC 4790 A Soderberg, J Dittmann, B Claus, T Esty, S Harrison, T Kurcz, Y Michaels, ... The Astronomer's Telegram 3968, 1, 2012 | 5 | 2012 |
| Low-rank MDPs with Continuous Action Spaces M Oprescu, A Bennett, N Kallus International Conference on Artificial Intelligence and Statistics, 4069-4077, 2024 | 1* | 2024 |
| Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes A Bennett, N Kallus, M Oprescu, W Sun, K Wang arXiv preprint arXiv:2404.00099, 2024 | 1 | 2024 |
| Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data M Oprescu, N Kallus arXiv preprint arXiv:2406.06452, 2024 | | 2024 |
| MA11. 01 Multifactorial Model to Predict Response to PD-(L) 1 Blockade in Patients with High PD-L1 Metastatic Non-Small Cell Lung Cancer K Arbour, M Oprescu, J Hakim, H Rizvi, M Leiserson, M Ginsburg, ... Journal of Thoracic Oncology 14 (10), S290, 2019 | | 2019 |
Vasilis SyrgkanisAssistant Professor, Stanford UniversityZweryfikowany adres z stanford.edu
