Ian Covert

22 papers A* 5A 1Journal 16
YearRankTypeTitle / Venue / Authors
2024 J jnl
Locality Alignment Improves Vision-Language Models.
CoRR
Ian Covert, Tony Sun, James Zou, Tatsunori Hashimoto
2024 J jnl
Scaling Laws for the Value of Individual Data Points in Machine Learning.
CoRR
Ian Covert, Wenlong Ji, Tatsunori Hashimoto, James Zou
2024 A* conf
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution.
NeurIPS
Ian Covert, Chanwoo Kim, Su-In Lee, James Y. Zou, Tatsunori B. Hashimoto
2024 J jnl
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution.
CoRR
Ian Covert, Chanwoo Kim, Su-In Lee, James Zou, Tatsunori Hashimoto
2023 J jnl
Estimating Conditional Mutual Information for Dynamic Feature Selection.
CoRR
Soham Gadgil, Ian Covert, Su-In Lee
2023 A* conf
Feature Selection in the Contrastive Analysis Setting.
NeurIPS
Ethan Weinberger, Ian Covert, Su-In Lee
2023 J jnl
Feature Selection in the Contrastive Analysis Setting.
CoRR
Ethan Weinberger, Ian Covert, Su-In Lee
2023 J jnl
Learning to Maximize Mutual Information for Dynamic Feature Selection.
CoRR
Ian Covert, Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan J. White, Su-In Lee
2023 A* conf
On the Robustness of Removal-Based Feature Attributions.
NeurIPS
Chris Lin, Ian Covert, Su-In Lee
2023 J jnl
On the Robustness of Removal-Based Feature Attributions.
CoRR
Chris Lin, Ian Covert, Su-In Lee
2023 A* conf
What does a platypus look like? Generating customized prompts for zero-shot image classification.
ICCV
Sarah M. Pratt, Ian Covert, Rosanne Liu, Ali Farhadi
2022 J jnl
Learning to Estimate Shapley Values with Vision Transformers.
CoRR
Ian Covert, Chanwoo Kim, Su-In Lee
2022 J jnl
Neural Granger Causality.
IEEE Trans. Pattern Anal. Mach. Intell.
Alex Tank, Ian Covert, Nicholas J. Foti, Ali Shojaie, Emily B. Fox
2021 J jnl
Disrupting Model Training with Adversarial Shortcuts.
CoRR
Ivan Evtimov, Ian Covert, Aditya Kusupati, Tadayoshi Kohno
2021 J jnl
Explaining by Removing: A Unified Framework for Model Explanation.
J. Mach. Learn. Res.
Ian Covert, Scott M. Lundberg, Su-In Lee
2021 J jnl
FastSHAP: Real-Time Shapley Value Estimation.
CoRR
Neil Jethani, Mukund Sudarshan, Ian Covert, Su-In Lee, Rajesh Ranganath
2021 A conf
Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression.
AISTATS
Ian Covert, Su-In Lee
2020 J jnl
Explaining by Removing: A Unified Framework for Model Explanation.
CoRR
Ian Covert, Scott M. Lundberg, Su-In Lee
2020 J jnl
Feature Removal Is a Unifying Principle for Model Explanation Methods.
CoRR
Ian Covert, Scott M. Lundberg, Su-In Lee
2020 J jnl
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression.
CoRR
Ian Covert, Su-In Lee
2020 J jnl
Understanding Global Feature Contributions Through Additive Importance Measures.
CoRR
Ian Covert, Scott M. Lundberg, Su-In Lee
2020 A* conf
Understanding Global Feature Contributions With Additive Importance Measures.
NeurIPS
Ian Covert, Scott M. Lundberg, Su-In Lee