| Interpretml: A unified framework for machine learning interpretability H Nori, S Jenkins, P Koch, R Caruana arXiv preprint arXiv:1909.09223, 2019 | 344 | 2019 |
| Interpreting interpretability: understanding data scientists' use of interpretability tools for machine learning H Kaur, H Nori, S Jenkins, R Caruana, H Wallach, J Wortman Vaughan Proceedings of the 2020 CHI conference on human factors in computing systems …, 2020 | 340 | 2020 |
| Sparks of artificial general intelligence: Early experiments with gpt-4 S Bubeck, V Chandrasekaran, R Eldan, J Gehrke, E Horvitz, E Kamar, ... arXiv preprint arXiv:2303.12712, 2023 | 241 | 2023 |
| Capabilities of gpt-4 on medical challenge problems H Nori, N King, SM McKinney, D Carignan, E Horvitz arXiv preprint arXiv:2303.13375, 2023 | 45 | 2023 |
| An algorithmic framework for differentially private data analysis on trusted processors J Allen, B Ding, J Kulkarni, H Nori, O Ohrimenko, S Yekhanin Advances in Neural Information Processing Systems 32, 2019 | 38 | 2019 |
| Comparing population means under local differential privacy: with significance and power B Ding, H Nori, P Li, J Allen Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 31 | 2018 |
| Intelligible and explainable machine learning: Best practices and practical challenges R Caruana, S Lundberg, MT Ribeiro, H Nori, S Jenkins Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 29 | 2020 |
| Gam changer: Editing generalized additive models with interactive visualization ZJ Wang, A Kale, H Nori, P Stella, M Nunnally, DH Chau, M Vorvoreanu, ... arXiv preprint arXiv:2112.03245, 2021 | 16 | 2021 |
| Accuracy, Interpretability, and Differential Privacy via Explainable Boosting H Nori, R Caruana, Z Bu, JH Shen, J Kulkarni Proceedings of the 38th International Conference on Machine Learning 139 …, 2021 | 15 | 2021 |
| Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems H Kaur, H Nori, S Jenkins, R Caruana, H Wallach, J Wortman Vaughan | 10 | 2020 |
| Interpretability, then what? editing machine learning models to reflect human knowledge and values ZJ Wang, A Kale, H Nori, P Stella, ME Nunnally, DH Chau, M Vorvoreanu, ... Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 7 | 2022 |
| Method and System of Correcting Data Imbalance in a Dataset Used in Machine-Learning CL Weider, R Kikin-Gil, HP Nori US Patent App. 16/424,371, 2020 | 6 | 2020 |
| Method and system of performing data imbalance detection and correction in training a machine-learning model CL Weider, R Kikin-Gil, HP Nori US Patent 11,526,701, 2022 | 5 | 2022 |
| Method and system of detecting data imbalance in a dataset used in machine-learning CL Weider, R Kikin-Gil, HP Nori US Patent 11,521,115, 2022 | 5 | 2022 |
| Remote testing analysis for software optimization based on client-side local differential privacy-based data B Ding, HP Nori, PL Li, JS Allen US Patent 10,902,149, 2021 | 5 | 2021 |
| Differentially private estimation of heterogeneous causal effects F Niu, H Nori, B Quistorff, R Caruana, D Ngwe, A Kannan Conference on Causal Learning and Reasoning, 618-633, 2022 | 3 | 2022 |
| Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data Z Chen, S Tan, H Nori, K Inkpen, Y Lou, R Caruana Machine Learning and Principles and Practice of Knowledge Discovery in …, 2022 | 3 | 2022 |
| Summarize with Caution: Comparing Global Feature Attributions. A Okeson, R Caruana, N Craswell, K Inkpen, SM Lundberg, H Nori, ... IEEE Data Eng. Bull. 44 (4), 14-27, 2021 | 2 | 2021 |
| Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes TM Bosschieter, Z Xu, H Lan, BJ Lengerich, H Nori, K Sitcov, V Souter, ... arXiv preprint arXiv:2207.05322, 2022 | 1 | 2022 |
| Primo: Practical {Learning-Augmented} Systems with Interpretable Models Q Hu, H Nori, P Sun, Y Wen, T Zhang 2022 USENIX Annual Technical Conference (USENIX ATC 22), 519-538, 2022 | 1 | 2022 |
Rich CaruanaMicrosoft ReesearchBestätigte E-Mail-Adresse bei microsoft.com
