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Su-In Lee
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2020 – today
- 2024
- [c32]Soham Gadgil, Ian Connick Covert, Su-In Lee:
Estimating Conditional Mutual Information for Dynamic Feature Selection. ICLR 2024 - [c31]Wei Jin
, Haohan Wang
DCAI: Data-centric Artificial Intelligence. WWW (Companion Volume) 2024: 1482-1485 - [i34]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) - [i33]Chris Lin, Mingyu Lu, Chanwoo Kim, Su-In Lee:
Efficient Shapley Values for Attributing Global Properties of Diffusion Models to Data Group. CoRR abs/2407.03153 (2024) - 2023
- [j13]Hugh Chen
Algorithms to estimate Shapley value feature attributions. Nat. Mac. Intell. 5(6): 590-601 (2023) - [c30]Ian Connick Covert, Chanwoo Kim, Su-In Lee:
Learning to Estimate Shapley Values with Vision Transformers. ICLR 2023 - [c29]Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee:
Contrastive Corpus Attribution for Explaining Representations. ICLR 2023 - [c28]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 - [c27]Chris Lin, Ian Covert, Su-In Lee:
On the Robustness of Removal-Based Feature Attributions. NeurIPS 2023 - [c26]Ethan Weinberger, Ian Covert, Su-In Lee:
Feature Selection in the Contrastive Analysis Setting. NeurIPS 2023 - [i32]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) - [i31]Soham Gadgil, Ian Covert, Su-In Lee:
Estimating Conditional Mutual Information for Dynamic Feature Selection. CoRR abs/2306.03301 (2023) - [i30]Chris Lin, Ian Covert, Su-In Lee:
On the Robustness of Removal-Based Feature Attributions. CoRR abs/2306.07462 (2023) - [i29]Ethan Weinberger, Ian Covert, Su-In Lee:
Feature Selection in the Contrastive Analysis Setting. CoRR abs/2310.18531 (2023) - 2022
- [c25]Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee:
Moment Matching Deep Contrastive Latent Variable Models. AISTATS 2022: 2354-2371 - [c24]Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath:
FastSHAP: Real-Time Shapley Value Estimation. ICLR 2022 - [e2]David A. Knowles, Sara Mostafavi, Su-In Lee:
Machine Learning in Computational Biology, 21-22 November 2022, Online. Proceedings of Machine Learning Research 200, PMLR 2022 [contents] - [i28]Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee:
Moment Matching Deep Contrastive Latent Variable Models. CoRR abs/2202.10560 (2022) - [i27]Mingyu Lu, Yifang Chen, Su-In Lee:
A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior. CoRR abs/2205.02944 (2022) - [i26]Ian Covert, Chanwoo Kim, Su-In Lee:
Learning to Estimate Shapley Values with Vision Transformers. CoRR abs/2206.05282 (2022) - [i25]Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee:
Algorithms to estimate Shapley value feature attributions. CoRR abs/2207.07605 (2022) - [i24]Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee:
Contrastive Corpus Attribution for Explaining Representations. CoRR abs/2210.00107 (2022) - 2021
- [j12]Joseph D. Janizek, Pascal Sturmfels, Su-In Lee:
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks. J. Mach. Learn. Res. 22: 104:1-104:54 (2021) - [j11]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) - [j10]Alex J. DeGrave
AI for radiographic COVID-19 detection selects shortcuts over signal. Nat. Mach. Intell. 3(7): 610-619 (2021) - [j9]Gabriel G. Erion
Improving performance of deep learning models with axiomatic attribution priors and expected gradients. Nat. Mach. Intell. 3(7): 620-631 (2021) - [j8]Hugh Chen
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals. npj Digit. Medicine 4 (2021) - [j7]Nicasia Beebe-Wang
Efficient and Explainable Risk Assessments for Imminent Dementia in an Aging Cohort Study. IEEE J. Biomed. Health Informatics 25(7): 2409-2420 (2021) - [c23]Ian Covert, Su-In Lee:
Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression. AISTATS 2021: 3457-3465 - [e1]David A. Knowles, Sara Mostafavi, Su-In Lee:
Machine Learning in Computational Biology Meeting, MLCB 2021, online, November 22-23, 2021. Proceedings of Machine Learning Research 165, PMLR 2021 [contents] - [i23]Hugh Chen, Scott M. Lundberg, Su-In Lee:
Explaining a Series of Models by Propagating Local Feature Attributions. CoRR abs/2105.00108 (2021) - [i22]Sahil Verma
Pitfalls of Explainable ML: An Industry Perspective. CoRR abs/2106.07758 (2021) - [i21]Neil Jethani, Mukund Sudarshan, Ian Covert, Su-In Lee, Rajesh Ranganath:
FastSHAP: Real-Time Shapley Value Estimation. CoRR abs/2107.07436 (2021) - 2020
- [j6]Youngseok Lee, Su-In Lee, Chan-Gun Lee
Continual Prediction of Bug-Fix Time Using Deep Learning-Based Activity Stream Embedding. IEEE Access 8: 10503-10515 (2020) - [j5]Scott M. Lundberg, Gabriel G. Erion
From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2(1): 56-67 (2020) - [c22]Joseph D. Janizek
An adversarial approach for the robust classification of pneumonia from chest radiographs. CHIL 2020: 69-79 - [c21]Ian Covert, Scott M. Lundberg, Su-In Lee:
Understanding Global Feature Contributions With Additive Importance Measures. NeurIPS 2020 - [c20]Ethan Weinberger, Joseph D. Janizek, Su-In Lee:
Learning Deep Attribution Priors Based On Prior Knowledge. NeurIPS 2020 - [i20]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 abs/2001.04051 (2020) - [i19]Joseph D. Janizek, Pascal Sturmfels, Su-In Lee:
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks. CoRR abs/2002.04138 (2020) - [i18]Hugh Chen, Scott M. Lundberg, Gabriel G. Erion, Jerry H. Kim, Su-In Lee:
Deep Transfer Learning for Physiological Signals. CoRR abs/2002.04770 (2020) - [i17]Ian Covert, Scott M. Lundberg, Su-In Lee:
Understanding Global Feature Contributions Through Additive Importance Measures. CoRR abs/2004.00668 (2020) - [i16]Hugh Chen, Joseph D. Janizek, Scott M. Lundberg, Su-In Lee:
True to the Model or True to the Data? CoRR abs/2006.16234 (2020) - [i15]Ian Covert, Scott M. Lundberg, Su-In Lee:
Feature Removal Is a Unifying Principle for Model Explanation Methods. CoRR abs/2011.03623 (2020) - [i14]Ian Covert, Scott M. Lundberg, Su-In Lee:
Explaining by Removing: A Unified Framework for Model Explanation. CoRR abs/2011.14878 (2020) - [i13]Ian Covert, Su-In Lee:
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression. CoRR abs/2012.01536 (2020)
2010 – 2019
- 2019
- [i12]Scott M. Lundberg, Gabriel G. Erion
Explainable AI for Trees: From Local Explanations to Global Understanding. CoRR abs/1905.04610 (2019) - [i11]Gabriel G. Erion
Learning Explainable Models Using Attribution Priors. CoRR abs/1906.10670 (2019) - [i10]Hugh Chen, Scott M. Lundberg, Su-In Lee:
Explaining Models by Propagating Shapley Values of Local Components. CoRR abs/1911.11888 (2019) - [i9]Ethan Weinberger, Joseph D. Janizek, Su-In Lee:
Learned Feature Attribution Priors. CoRR abs/1912.10065 (2019) - 2018
- [c19]Sumit Mukherjee, Alberto Carignano, Georg Seelig, Su-In Lee:
Identifying progressive gene network perturbation from single-cell RNA-seq data. EMBC 2018: 5034-5040 - [i8]Hugh Chen, Scott M. Lundberg, Su-In Lee:
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data. CoRR abs/1801.07384 (2018) - [i7]Scott M. Lundberg, Gabriel G. Erion, Su-In Lee:
Consistent Individualized Feature Attribution for Tree Ensembles. CoRR abs/1802.03888 (2018) - 2017
- [c18]Scott M. Lundberg, Su-In Lee:
A Unified Approach to Interpreting Model Predictions. NIPS 2017: 4765-4774 - [i6]Scott M. Lundberg, Su-In Lee:
A unified approach to interpreting model predictions. CoRR abs/1705.07874 (2017) - [i5]Scott M. Lundberg, Su-In Lee:
Consistent feature attribution for tree ensembles. CoRR abs/1706.06060 (2017) - [i4]Hugh Chen, Scott M. Lundberg, Su-In Lee:
Checkpoint Ensembles: Ensemble Methods from a Single Training Process. CoRR abs/1710.03282 (2017) - [i3]Gabriel G. Erion, Hugh Chen, Scott M. Lundberg, Su-In Lee:
Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning. CoRR abs/1712.00563 (2017) - 2016
- [j4]Maxim Grechkin
Identifying Network Perturbation in Cancer. PLoS Comput. Biol. 12(5) (2016) - [c17]Naozumi Hiranuma, Scott M. Lundberg, Su-In Lee:
CloudControl: Leveraging many public ChIP-seq control experiments to better remove background noise. BCB 2016: 191-199 - [c16]Jang-Woon Baek
Fast and reliable tracking algorithm for on-road vehicle detection systems. ICUFN 2016: 70-72 - [c15]Mohammad Javad Hosseini, Su-In Lee:
Learning Sparse Gaussian Graphical Models with Overlapping Blocks. NIPS 2016: 3801-3809 - [c14]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: 931-936 - [i2]Scott M. Lundberg, Su-In Lee:
An unexpected unity among methods for interpreting model predictions. CoRR abs/1611.07478 (2016) - 2015
- [c13]Maxim Grechkin, Maryam Fazel, Daniela M. Witten, Su-In Lee:
Pathway Graphical Lasso. AAAI 2015: 2617-2623 - 2014
- [j3]Jang-Woon Baek
Track Topology Based Reliable In-Network Aggregation Scheduling in Wireless Sensor Networks. IEICE Trans. Commun. 97-B(11): 2386-2394 (2014) - [j2]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): 445-488 (2014) - [j1]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): 3297-3331 (2014) - [c12]Safiye Celik, Benjamin A. Logsdon, Su-In Lee:
Efficient Dimensionality Reduction for High-Dimensional Network Estimation. ICML 2014: 1953-1961 - 2013
- [c11]Ezgi Mercan, Linda G. Shapiro, Seth M. Weinberg
The use of pseudo-landmarks for craniofacial analysis: A comparative study with L1-regularized logistic regression. EMBC 2013: 6083-6086 - [i1]Karthik Mohan, Palma London, Maryam Fazel, Daniela M. Witten, Su-In Lee:
Node-Based Learning of Multiple Gaussian Graphical Models. CoRR abs/1303.5145 (2013) - 2012
- [c10]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: 3219-3222 - [c9]Bilge Soran, Jenq-Neng Hwang, Su-In Lee, Linda G. Shapiro:
Tremor detection using motion filtering and SVM. ICPR 2012: 178-181 - [c8]Shulin Yang, Linda G. Shapiro, Michael L. Cunningham, Matthew L. Speltz, Craig Birgfeld, Indriyati Atmosukarto
Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models. MCBR-CDS 2012: 33-44 - [c7]Karthik Mohan, Michael Jae-Yoon Chung, Seungyeop Han, Daniela M. Witten, Su-In Lee, Maryam Fazel:
Structured Learning of Gaussian Graphical Models. NIPS 2012: 629-637 - 2011
- [c6]Shulin Yang, Linda G. Shapiro, Michael L. Cunningham, Matthew L. Speltz, Su-In Lee:
Classification and feature selection for craniosynostosis. BCB 2011: 340-344
2000 – 2009
- 2007
- [c5]Su-In Lee, Vassil Chatalbashev, David Vickrey, Daphne Koller:
Learning a meta-level prior for feature relevance from multiple related tasks. ICML 2007: 489-496 - 2006
- [c4]Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. Ng:
Efficient L1 Regularized Logistic Regression. AAAI 2006: 401-408 - [c3]Su-In Lee, Varun Ganapathi, Daphne Koller:
Efficient Structure Learning of Markov Networks using L1-Regularization. NIPS 2006: 817-824 - 2003
- [c2]Su-In Lee, Serafim Batzoglou:
ICA-based Clustering of Genes from Microarray Expression Data. NIPS 2003: 675-682 - 2000
- [c1]Su-In Lee, Soo-Young Lee:
Top-Down Attention Control at Feature Space for Robust Pattern Recognition. Biologically Motivated Computer Vision 2000: 129-138
Coauthor Index
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