Temporal Graph Convolutional Networks for Automatic Seizure Detection. - Ian C. Covert; Balu Krishnan; Imad Najm; Jiening Zhan; Matthew Shore; John Hixson; Ming Jack Po (MLHC, 2019)

Understanding Global Feature Contributions With Additive Importance Measures. - Ian Covert; Scott M. Lundberg; Su-In Lee (NeurIPS, 2020)

Understanding Global Feature Contributions Through Additive Importance Measures. - Ian Covert; Scott M. Lundberg; Su-In Lee (CoRR, 2020)

Feature Removal Is a Unifying Principle for Model Explanation Methods. - Ian Covert; Scott M. Lundberg; Su-In Lee (CoRR, 2020)

Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression. - Ian Covert; Su-In Lee (CoRR, 2020)

Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression. - Ian Covert; Su-In Lee (AISTATS, 2021)

Disrupting Model Training with Adversarial Shortcuts. - Ivan Evtimov; Ian Covert; Aditya Kusupati; Tadayoshi Kohno (CoRR, 2021)

Explaining by Removing: A Unified Framework for Model Explanation. - Ian Covert; Scott M. Lundberg; Su-In Lee (J. Mach. Learn. Res., 2021)

FastSHAP: Real-Time Shapley Value Estimation. - Neil Jethani; Mukund Sudarshan; Ian Connick Covert; Su-In Lee; Rajesh Ranganath (ICLR, 2022)

Neural Granger Causality. - Alex Tank; Ian Covert; Nicholas J. Foti; Ali Shojaie; Emily B. Fox (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

What does a platypus look like? Generating customized prompts for zero-shot image classification. - Sarah M. Pratt; Ian Covert; Rosanne Liu; Ali Farhadi (ICCV, 2023)

Learning to Estimate Shapley Values with Vision Transformers. - Ian Connick Covert; Chanwoo Kim 002; Su-In Lee (ICLR, 2023)

Learning to Maximize Mutual Information for Dynamic Feature Selection. - Ian Connick Covert; Wei Qiu; Mingyu Lu; Nayoon Kim; Nathan J. White; Su-In Lee (ICML, 2023)

On the Robustness of Removal-Based Feature Attributions. - Chris Lin; Ian Covert; Su-In Lee (NeurIPS, 2023)

Feature Selection in the Contrastive Analysis Setting. - Ethan Weinberger; Ian Covert; Su-In Lee (NeurIPS, 2023)

Transparent Machine Learning: Theory and Computation - Ian Connick Covert (2023)

Algorithms to estimate Shapley value feature attributions. - Hugh Chen; Ian C. Covert; Scott M. Lundberg; Su-In Lee (Nat. Mac. Intell., 2023)

Estimating Conditional Mutual Information for Dynamic Feature Selection. - Soham Gadgil; Ian Connick Covert; Su-In Lee (ICLR, 2024)

Scaling Laws for the Value of Individual Data Points in Machine Learning. - Ian Connick Covert; Wenlong Ji; Tatsunori Hashimoto; James Zou 001 (ICML, 2024)

Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution. - Ian Covert; Chanwoo Kim 002; Su-In Lee; James Y. Zou; Tatsunori B. Hashimoto (NeurIPS, 2024)

Locality Alignment Improves Vision-Language Models. - Ian Connick Covert; Tony Sun; James Zou 001; Tatsunori Hashimoto (ICLR, 2025)