Harsha Nori
Harsha Nori
Microsoft Research
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Zitiert von
Zitiert von
Interpretml: A unified framework for machine learning interpretability
H Nori, S Jenkins, P Koch, R Caruana
arXiv preprint arXiv:1909.09223, 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
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
Capabilities of gpt-4 on medical challenge problems
H Nori, N King, SM McKinney, D Carignan, E Horvitz
arXiv preprint arXiv:2303.13375, 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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