Su-In Lee

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 62

Top Topics

Top Venues

J. Mach. Learn. Res.

Publications

Ian Covert, Chanwoo Kim, Su-In Lee, James Zou, Tatsunori Hashimoto
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution. CoRR (2024)
Wei Jin, Haohan Wang, Daochen Zha, Qiaoyu Tan, Yao Ma, Sharon Li, Su-In Lee
DCAI: Data-centric Artificial Intelligence. WWW (Companion Volume) (2024)
Chris Lin, Mingyu Lu, Chanwoo Kim, Su-In Lee
Efficient Shapley Values for Attributing Global Properties of Diffusion Models to Data Group. CoRR (2024)
Soham Gadgil, Ian Connick Covert, Su-In Lee
Estimating Conditional Mutual Information for Dynamic Feature Selection. ICLR (2024)
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)
Chris Lin, Ian Covert, Su-In Lee
On the Robustness of Removal-Based Feature Attributions. NeurIPS (2023)
Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee
Algorithms to estimate Shapley value feature attributions. Nat. Mac. Intell. 5 (6) (2023)
Chris Lin, Ian Covert, Su-In Lee
On the Robustness of Removal-Based Feature Attributions. CoRR (2023)
Soham Gadgil, Ian Covert, Su-In Lee
Estimating Conditional Mutual Information for Dynamic Feature Selection. CoRR (2023)
Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee
Contrastive Corpus Attribution for Explaining Representations. ICLR (2023)
Ethan Weinberger, Ian Covert, Su-In Lee
Feature Selection in the Contrastive Analysis Setting. NeurIPS (2023)
Ian Connick Covert, Chanwoo Kim, Su-In Lee
Learning to Estimate Shapley Values with Vision Transformers. ICLR (2023)
Ian Covert, Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan White, Su-In Lee
Learning to Maximize Mutual Information for Dynamic Feature Selection. CoRR (2023)
Ethan Weinberger, Ian Covert, Su-In Lee
Feature Selection in the Contrastive Analysis Setting. CoRR (2023)
Mingyu Lu, Yifang Chen, Su-In Lee
A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior. CoRR (2022)
Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee
Moment Matching Deep Contrastive Latent Variable Models. AISTATS (2022)
Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath
FastSHAP: Real-Time Shapley Value Estimation. ICLR (2022)
Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee
Moment Matching Deep Contrastive Latent Variable Models. CoRR (2022)
Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee
Algorithms to estimate Shapley value feature attributions. CoRR (2022)
Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee
Contrastive Corpus Attribution for Explaining Representations. CoRR (2022)
Ian Covert, Chanwoo Kim, Su-In Lee
Learning to Estimate Shapley Values with Vision Transformers. CoRR (2022)
Gabriel G. Erion, Joseph D. Janizek, Pascal Sturmfels, Scott M. Lundberg, Su-In Lee
Improving performance of deep learning models with axiomatic attribution priors and expected gradients. Nat. Mach. Intell. 3 (7) (2021)
Sahil Verma, Aditya Lahiri, John P. Dickerson, Su-In Lee
Pitfalls of Explainable ML: An Industry Perspective. CoRR (2021)
Hugh Chen, Scott M. Lundberg, Su-In Lee
Explaining a Series of Models by Propagating Local Feature Attributions. CoRR (2021)
Ian Covert, Su-In Lee
Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression. AISTATS (2021)
Alex J. DeGrave, Joseph D. Janizek, Su-In Lee
AI for radiographic COVID-19 detection selects shortcuts over signal. Nat. Mach. Intell. 3 (7) (2021)
Ian Covert, Scott M. Lundberg, Su-In Lee
Explaining by Removing: A Unified Framework for Model Explanation. J. Mach. Learn. Res. 22 (2021)
Joseph D. Janizek, Pascal Sturmfels, Su-In Lee
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks. J. Mach. Learn. Res. 22 (2021)
Hugh Chen, Scott M. Lundberg, Gabriel G. Erion, Jerry H. Kim, Su-In Lee
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals. npj Digit. Medicine 4 (2021)
Nicasia Beebe-Wang, Alex Okeson, Tim Althoff, Su-In Lee
Efficient and Explainable Risk Assessments for Imminent Dementia in an Aging Cohort Study. IEEE J. Biomed. Health Informatics 25 (7) (2021)
Neil Jethani, Mukund Sudarshan, Ian Covert, Su-In Lee, Rajesh Ranganath
FastSHAP: Real-Time Shapley Value Estimation. CoRR (2021)
Joseph D. Janizek, Gabriel G. Erion, Alex J. DeGrave, Su-In Lee
An adversarial approach for the robust classification of pneumonia from chest radiographs. CHIL (2020)
Ian Covert, Scott M. Lundberg, Su-In Lee
Understanding Global Feature Contributions Through Additive Importance Measures. CoRR (2020)
Ian Covert, Su-In Lee
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression. CoRR (2020)
Joseph D. Janizek, Pascal Sturmfels, Su-In Lee
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks. CoRR (2020)
Ian Covert, Scott M. Lundberg, Su-In Lee
Feature Removal Is a Unifying Principle for Model Explanation Methods. CoRR (2020)
Ethan Weinberger, Joseph D. Janizek, Su-In Lee
Learning Deep Attribution Priors Based On Prior Knowledge. NeurIPS (2020)
Hugh Chen, Joseph D. Janizek, Scott M. Lundberg, Su-In Lee
True to the Model or True to the Data? CoRR (2020)
Ian Covert, Scott M. Lundberg, Su-In Lee
Understanding Global Feature Contributions With Additive Importance Measures. NeurIPS (2020)
Joseph D. Janizek, Gabriel G. Erion, Alex J. DeGrave, Su-In Lee
An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs. CoRR (2020)
Scott M. Lundberg, Gabriel G. Erion, Hugh Chen, Alex J. DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, Su-In Lee
From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2 (1) (2020)
Youngseok Lee, Su-In Lee, Chan-Gun Lee, Ikjun Yeom, Honguk Woo
Continual Prediction of Bug-Fix Time Using Deep Learning-Based Activity Stream Embedding. IEEE Access 8 (2020)
Hugh Chen, Scott M. Lundberg, Gabriel G. Erion, Jerry H. Kim, Su-In Lee
Deep Transfer Learning for Physiological Signals. CoRR (2020)
Ian Covert, Scott M. Lundberg, Su-In Lee
Explaining by Removing: A Unified Framework for Model Explanation. CoRR (2020)
Gabriel G. Erion, Joseph D. Janizek, Pascal Sturmfels, Scott M. Lundberg, Su-In Lee
Learning Explainable Models Using Attribution Priors. CoRR (2019)
Ethan Weinberger, Joseph D. Janizek, Su-In Lee
Learned Feature Attribution Priors. CoRR (2019)
Scott M. Lundberg, Gabriel G. Erion, Hugh Chen, Alex J. DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, Su-In Lee
Explainable AI for Trees: From Local Explanations to Global Understanding. CoRR (2019)
Hugh Chen, Scott M. Lundberg, Su-In Lee
Explaining Models by Propagating Shapley Values of Local Components. CoRR (2019)
Sumit Mukherjee, Alberto Carignano, Georg Seelig, Su-In Lee
Identifying progressive gene network perturbation from single-cell RNA-seq data. EMBC (2018)
Scott M. Lundberg, Gabriel G. Erion, Su-In Lee
Consistent Individualized Feature Attribution for Tree Ensembles. CoRR (2018)
Hugh Chen, Scott M. Lundberg, Su-In Lee
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data. CoRR (2018)
Scott M. Lundberg, Su-In Lee
A Unified Approach to Interpreting Model Predictions. NIPS (2017)
Gabriel G. Erion, Hugh Chen, Scott M. Lundberg, Su-In Lee
Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning. CoRR (2017)
Hugh Chen, Scott M. Lundberg, Su-In Lee
Checkpoint Ensembles: Ensemble Methods from a Single Training Process. CoRR (2017)
Scott M. Lundberg, Su-In Lee
Consistent feature attribution for tree ensembles. CoRR (2017)
Scott M. Lundberg, Su-In Lee
A unified approach to interpreting model predictions. CoRR (2017)
Scott M. Lundberg, Su-In Lee
An unexpected unity among methods for interpreting model predictions. CoRR (2016)
Mohammad Javad Hosseini, Su-In Lee
Learning Sparse Gaussian Graphical Models with Overlapping Blocks. NIPS (2016)
Mohammad Javad Hosseini, Noah A. Smith, Su-In Lee
UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields. SemEval@NAACL-HLT (2016)
Maxim Grechkin, Benjamin A. Logsdon, Andrew J. Gentles, Su-In Lee
Identifying Network Perturbation in Cancer. PLoS Comput. Biol. 12 (5) (2016)
Naozumi Hiranuma, Scott M. Lundberg, Su-In Lee
CloudControl: Leveraging many public ChIP-seq control experiments to better remove background noise. BCB (2016)
Jang-Woon Baek, Byung-Gil Han, Hyunwoo Kang, Yoonsu Chung, Su-In Lee
Fast and reliable tracking algorithm for on-road vehicle detection systems. ICUFN (2016)
Maxim Grechkin, Maryam Fazel, Daniela M. Witten, Su-In Lee
Pathway Graphical Lasso. AAAI (2015)
Safiye Celik, Benjamin A. Logsdon, Su-In Lee
Efficient Dimensionality Reduction for High-Dimensional Network Estimation. ICML (2014)
Karthik Mohan, Palma London, Maryam Fazel, Daniela M. Witten, Su-In Lee
Node-based learning of multiple Gaussian graphical models. J. Mach. Learn. Res. 15 (1) (2014)
Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, Daniela M. Witten
Learning graphical models with hubs. J. Mach. Learn. Res. 15 (1) (2014)
Jang-Woon Baek, Kee-Koo Kwon, Su-In Lee, Dae-Wha Seo
Track Topology Based Reliable In-Network Aggregation Scheduling in Wireless Sensor Networks. IEICE Trans. Commun. (11) (2014)
Ezgi Mercan, Linda G. Shapiro, Seth M. Weinberg, Su-In Lee
The use of pseudo-landmarks for craniofacial analysis: A comparative study with L1-regularized logistic regression. EMBC (2013)
Karthik Mohan, Palma London, Maryam Fazel, Daniela M. Witten, Su-In Lee
Node-Based Learning of Multiple Gaussian Graphical Models CoRR (2013)
Karthik Mohan, Michael Jae-Yoon Chung, Seungyeop Han, Daniela M. Witten, Su-In Lee, Maryam Fazel
Structured Learning of Gaussian Graphical Models. NIPS (2012)
Bilge Soran, Zhiyong Xie, Rosalia F. Tungaraza, Su-In Lee, Linda G. Shapiro, Thomas J. Grabowski
Parcellation of human inferior parietal lobule based on diffusion MRI. EMBC (2012)
Shulin Yang, Linda G. Shapiro, Michael L. Cunningham, Matthew L. Speltz, Craig Birgfeld, Indriyati Atmosukarto, Su-In Lee
Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models. MCBR-CDS (2012)
Bilge Soran, Jenq-Neng Hwang, Su-In Lee, Linda G. Shapiro
Tremor detection using motion filtering and SVM. ICPR (2012)
Shulin Yang, Linda G. Shapiro, Michael L. Cunningham, Matthew L. Speltz, Su-In Lee
Classification and feature selection for craniosynostosis. BCB (2011)
Su-In Lee, Vassil Chatalbashev, David Vickrey, Daphne Koller
Learning a meta-level prior for feature relevance from multiple related tasks. ICML (2007)
Su-In Lee, Varun Ganapathi, Daphne Koller
-Regularization. NIPS (2006)
Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. Ng
Efficient L1 Regularized Logistic Regression. AAAI (2006)
Su-In Lee, Serafim Batzoglou
ICA-based Clustering of Genes from Microarray Expression Data. NIPS (2003)
Su-In Lee, Soo-Young Lee
Top-Down Attention Control at Feature Space for Robust Pattern Recognition. Biologically Motivated Computer Vision (2000)