Ian Covert

According to our database, Ian Covert authored at least 18 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution.

Ian Covert, Chanwoo Kim, Su-In Lee, James Zou, Tatsunori Hashimoto

CoRR, 2024

2023

Algorithms to estimate Shapley value feature attributions.

Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee

Nat. Mac. Intell., June, 2023

Estimating Conditional Mutual Information for Dynamic Feature Selection.

Soham Gadgil, Ian Covert, Su-In Lee

CoRR, 2023

Feature Selection in the Contrastive Analysis Setting.

Ethan Weinberger, Ian Covert, Su-In Lee

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Robustness of Removal-Based Feature Attributions.

Chris Lin, Ian Covert, Su-In Lee

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the International Conference on Machine Learning, 2023

Learning to Estimate Shapley Values with Vision Transformers.

Ian Connick Covert, Chanwoo Kim, Su-In Lee

Proceedings of the Eleventh International Conference on Learning Representations, 2023

What does a platypus look like? Generating customized prompts for zero-shot image classification.

Sarah M. Pratt, Ian Covert, Rosanne Liu, Ali Farhadi

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Neural Granger Causality.

Alex Tank, Ian Covert, Nicholas J. Foti, Ali Shojaie, Emily B. Fox

IEEE Trans. Pattern Anal. Mach. Intell., 2022

FastSHAP: Real-Time Shapley Value Estimation.

Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Explaining by Removing: A Unified Framework for Model Explanation.

Ian Covert, Scott M. Lundberg, Su-In Lee

J. Mach. Learn. Res., 2021

Disrupting Model Training with Adversarial Shortcuts.

Ivan Evtimov, Ian Covert, Aditya Kusupati, Tadayoshi Kohno

CoRR, 2021

Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression.

Ian Covert, Su-In Lee

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression.

Ian Covert, 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

Understanding Global Feature Contributions Through Additive Importance Measures.

Ian Covert, Scott M. Lundberg, Su-In Lee

CoRR, 2020

Understanding Global Feature Contributions With Additive Importance Measures.

Ian Covert, Scott M. Lundberg, Su-In Lee

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Temporal Graph Convolutional Networks for Automatic Seizure Detection.

Ian C. Covert, Balu Krishnan, Imad Najm, Jiening Zhan, Matthew Shore, John Hixson, Ming Jack Po

Proceedings of the Machine Learning for Healthcare Conference, 2019