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2020 – today
- 2024
[c11]Soham Gadgil, Ian Connick Covert, Su-In Lee:
Estimating Conditional Mutual Information for Dynamic Feature Selection. ICLR 2024- [c10]Ian Connick Covert, Wenlong Ji, Tatsunori Hashimoto, James Zou:
Scaling Laws for the Value of Individual Data Points in Machine Learning. ICML 2024 - [i15]Ian Covert, Chanwoo Kim, Su-In Lee, James Zou, Tatsunori Hashimoto:
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution. CoRR abs/2401.15866 (2024) - [i14]Ian Covert, Wenlong Ji, Tatsunori Hashimoto, James Zou:
Scaling Laws for the Value of Individual Data Points in Machine Learning. CoRR abs/2405.20456 (2024) - 2023
- [j3]Hugh Chen
, Ian C. Covert, Scott M. Lundberg, Su-In Lee:
Algorithms to estimate Shapley value feature attributions. Nat. Mac. Intell. 5(6): 590-601 (2023) - [c9]Sarah M. Pratt, Ian Covert, Rosanne Liu, Ali Farhadi:
What does a platypus look like? Generating customized prompts for zero-shot image classification. ICCV 2023: 15645-15655 - [c8]Ian Connick Covert, Chanwoo Kim, Su-In Lee:
Learning to Estimate Shapley Values with Vision Transformers. ICLR 2023 - [c7]Ian Connick Covert, Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan J. White, Su-In Lee:
Learning to Maximize Mutual Information for Dynamic Feature Selection. ICML 2023: 6424-6447 - [c6]Chris Lin, Ian Covert, Su-In Lee:
On the Robustness of Removal-Based Feature Attributions. NeurIPS 2023 - [c5]Ethan Weinberger, Ian Covert, Su-In Lee:
Feature Selection in the Contrastive Analysis Setting. NeurIPS 2023 - [i13]Ian Covert, Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan J. White, Su-In Lee:
Learning to Maximize Mutual Information for Dynamic Feature Selection. CoRR abs/2301.00557 (2023) - [i12]Soham Gadgil, Ian Covert, Su-In Lee:
Estimating Conditional Mutual Information for Dynamic Feature Selection. CoRR abs/2306.03301 (2023) - [i11]Chris Lin, Ian Covert, Su-In Lee:
On the Robustness of Removal-Based Feature Attributions. CoRR abs/2306.07462 (2023) - [i10]Ethan Weinberger, Ian Covert, Su-In Lee:
Feature Selection in the Contrastive Analysis Setting. CoRR abs/2310.18531 (2023) - 2022
- [j2]Alex Tank, Ian Covert, Nicholas J. Foti, Ali Shojaie, Emily B. Fox:
Neural Granger Causality. IEEE Trans. Pattern Anal. Mach. Intell. 44(8): 4267-4279 (2022) - [c4]Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath:
FastSHAP: Real-Time Shapley Value Estimation. ICLR 2022 - [i9]Ian Covert, Chanwoo Kim, Su-In Lee:
Learning to Estimate Shapley Values with Vision Transformers. CoRR abs/2206.05282 (2022) - [i8]Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee:
Algorithms to estimate Shapley value feature attributions. CoRR abs/2207.07605 (2022) - 2021
- [j1]Ian Covert, Scott M. Lundberg, Su-In Lee:
Explaining by Removing: A Unified Framework for Model Explanation. J. Mach. Learn. Res. 22: 209:1-209:90 (2021) - [c3]Ian Covert, Su-In Lee:
Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression. AISTATS 2021: 3457-3465 - [i7]Ivan Evtimov, Ian Covert, Aditya Kusupati, Tadayoshi Kohno:
Disrupting Model Training with Adversarial Shortcuts. CoRR abs/2106.06654 (2021) - [i6]Neil Jethani, Mukund Sudarshan, Ian Covert, Su-In Lee, Rajesh Ranganath:
FastSHAP: Real-Time Shapley Value Estimation. CoRR abs/2107.07436 (2021) - 2020
- [c2]Ian Covert, Scott M. Lundberg, Su-In Lee:
Understanding Global Feature Contributions With Additive Importance Measures. NeurIPS 2020 - [i5]Ian Covert, Scott M. Lundberg, Su-In Lee:
Understanding Global Feature Contributions Through Additive Importance Measures. CoRR abs/2004.00668 (2020) - [i4]Ian Covert, Scott M. Lundberg, Su-In Lee:
Feature Removal Is a Unifying Principle for Model Explanation Methods. CoRR abs/2011.03623 (2020) - [i3]Ian Covert, Scott M. Lundberg, Su-In Lee:
Explaining by Removing: A Unified Framework for Model Explanation. CoRR abs/2011.14878 (2020) - [i2]Ian Covert, Su-In Lee:
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression. CoRR abs/2012.01536 (2020)
2010 – 2019
- 2019
- [c1]Ian C. Covert, Balu Krishnan, Imad Najm, Jiening Zhan, Matthew Shore, John Hixson, Ming Jack Po:
Temporal Graph Convolutional Networks for Automatic Seizure Detection. MLHC 2019: 160-180 - [i1]Ian C. Covert, Balu Krishnan, Imad Najm, Jiening Zhan, Matthew Shore, John Hixson, Ming Jack Po:
Temporal Graph Convolutional Networks for Automatic Seizure Detection. CoRR abs/1905.01375 (2019)
Coauthor Index
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last updated on 2024-09-13 01:36 CEST by the dblp team
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