Su-In Lee - RankMe

87 papers A* 15A 2B 1Journal 51Unranked 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models. CoRR Mingyu Lu, Soham Gadgil, Chris Lin, Chanwoo Kim, Su-In Lee
2025	A*	`conf`	An Efficient Framework for Crediting Data Contributors of Diffusion Models. ICLR Mingyu Lu, Chris Lin, Chanwoo Kim, Su-In Lee
2025	J	`jnl`	CellCLIP - Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning. CoRR Mingyu Lu, Ethan Weinberger, Chanwoo Kim, Su-In Lee
2025	J	`jnl`	Ensembling Sparse Autoencoders. CoRR Soham Gadgil, Chris Lin, Su-In Lee
2024	—	`conf`	DCAI: Data-centric Artificial Intelligence. WWW (Companion Volume) Wei Jin, Haohan Wang, Daochen Zha, Qiaoyu Tan, Yao Ma, Sharon Li, Su-In Lee
2024	J	`jnl`	Efficient Shapley Values for Attributing Global Properties of Diffusion Models to Data Group. CoRR Chris Lin, Mingyu Lu, Chanwoo Kim, Su-In Lee
2024	A*	`conf`	Estimating Conditional Mutual Information for Dynamic Feature Selection. ICLR Soham Gadgil, Ian Connick Covert, Su-In Lee
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`	Algorithms to estimate Shapley value feature attributions. Nat. Mac. Intell. Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee
2023	A*	`conf`	Contrastive Corpus Attribution for Explaining Representations. ICLR Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee
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	A*	`conf`	Learning to Estimate Shapley Values with Vision Transformers. ICLR Ian Connick Covert, Chanwoo Kim, Su-In Lee
2023	A*	`conf`	Learning to Maximize Mutual Information for Dynamic Feature Selection. ICML Ian Connick Covert, Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan J. White, 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
2022	J	`jnl`	A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior. CoRR Mingyu Lu, Yifang Chen, Su-In Lee
2022	J	`jnl`	Algorithms to estimate Shapley value feature attributions. CoRR Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee
2022	J	`jnl`	Contrastive Corpus Attribution for Explaining Representations. CoRR Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee
2022	A*	`conf`	FastSHAP: Real-Time Shapley Value Estimation. ICLR Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath
2022	J	`jnl`	Learning to Estimate Shapley Values with Vision Transformers. CoRR Ian Covert, Chanwoo Kim, Su-In Lee
2022	—	`ed.`	Machine Learning in Computational Biology, 21-22 November 2022, Online MLCB David A. Knowles, Sara Mostafavi, Su-In Lee
2022	A	`conf`	Moment Matching Deep Contrastive Latent Variable Models. AISTATS Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee
2022	J	`jnl`	Moment Matching Deep Contrastive Latent Variable Models. CoRR Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee
2021	J	`jnl`	AI for radiographic COVID-19 detection selects shortcuts over signal. Nat. Mach. Intell. Alex J. DeGrave, Joseph D. Janizek, Su-In Lee
2021	J	`jnl`	Efficient and Explainable Risk Assessments for Imminent Dementia in an Aging Cohort Study. IEEE J. Biomed. Health Informatics Nicasia Beebe-Wang, Alex Okeson, Tim Althoff, Su-In Lee
2021	J	`jnl`	Explaining Explanations: Axiomatic Feature Interactions for Deep Networks. J. Mach. Learn. Res. Joseph D. Janizek, Pascal Sturmfels, Su-In Lee
2021	J	`jnl`	Explaining a Series of Models by Propagating Local Feature Attributions. CoRR Hugh Chen, Scott M. Lundberg, Su-In Lee
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	J	`jnl`	Forecasting adverse surgical events using self-supervised transfer learning for physiological signals. npj Digit. Medicine Hugh Chen, Scott M. Lundberg, Gabriel G. Erion, Jerry H. Kim, Su-In Lee
2021	A	`conf`	Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression. AISTATS Ian Covert, Su-In Lee
2021	J	`jnl`	Improving performance of deep learning models with axiomatic attribution priors and expected gradients. Nat. Mach. Intell. Gabriel G. Erion, Joseph D. Janizek, Pascal Sturmfels, Scott M. Lundberg, Su-In Lee
2021	—	`ed.`	Machine Learning in Computational Biology Meeting, MLCB 2021, online, November 22-23, 2021. MLCB David A. Knowles, Sara Mostafavi, Su-In Lee
2021	J	`jnl`	Pitfalls of Explainable ML: An Industry Perspective. CoRR Sahil Verma, Aditya Lahiri, John P. Dickerson, Su-In Lee
2020	J	`jnl`	Adversarial deconfounding autoencoder for learning robust gene expression embeddings. Bioinform. Ayse Berceste Dincer, Joseph D. Janizek, Su-In Lee
2020	J	`jnl`	An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs. CoRR Joseph D. Janizek, Gabriel G. Erion, Alex J. DeGrave, Su-In Lee
2020	—	`conf`	An adversarial approach for the robust classification of pneumonia from chest radiographs. CHIL Joseph D. Janizek, Gabriel G. Erion, Alex J. DeGrave, Su-In Lee
2020	J	`jnl`	Continual Prediction of Bug-Fix Time Using Deep Learning-Based Activity Stream Embedding. IEEE Access Youngseok Lee, Su-In Lee, Chan-Gun Lee, Ikjun Yeom, Honguk Woo
2020	J	`jnl`	Deep Transfer Learning for Physiological Signals. CoRR Hugh Chen, Scott M. Lundberg, Gabriel G. Erion, Jerry H. Kim, Su-In Lee
2020	J	`jnl`	Explaining Explanations: Axiomatic Feature Interactions for Deep Networks. CoRR Joseph D. Janizek, Pascal Sturmfels, 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`	From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 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
2020	J	`jnl`	Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression. CoRR Ian Covert, Su-In Lee
2020	A*	`conf`	Learning Deep Attribution Priors Based On Prior Knowledge. NeurIPS Ethan Weinberger, Joseph D. Janizek, Su-In Lee
2020	J	`jnl`	True to the Model or True to the Data? CoRR Hugh Chen, Joseph D. Janizek, Scott M. Lundberg, 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
2019	J	`jnl`	Explainable AI for Trees: From Local Explanations to Global Understanding. CoRR 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
2019	J	`jnl`	Explaining Models by Propagating Shapley Values of Local Components. CoRR Hugh Chen, Scott M. Lundberg, Su-In Lee
2019	J	`jnl`	Learned Feature Attribution Priors. CoRR Ethan Weinberger, Joseph D. Janizek, Su-In Lee
2019	J	`jnl`	Learning Explainable Models Using Attribution Priors. CoRR Gabriel G. Erion, Joseph D. Janizek, Pascal Sturmfels, Scott M. Lundberg, Su-In Lee
2018	J	`jnl`	Consistent Individualized Feature Attribution for Tree Ensembles. CoRR Scott M. Lundberg, Gabriel G. Erion, Su-In Lee
2018	J	`jnl`	Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data. CoRR Hugh Chen, Scott M. Lundberg, Su-In Lee
2018	—	`conf`	Identifying progressive gene network perturbation from single-cell RNA-seq data. EMBC Sumit Mukherjee, Alberto Carignano, Georg Seelig, Su-In Lee
2017	—	`conf`	A Unified Approach to Interpreting Model Predictions. NIPS Scott M. Lundberg, Su-In Lee
2017	J	`jnl`	A unified approach to interpreting model predictions. CoRR Scott M. Lundberg, Su-In Lee
2017	J	`jnl`	Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning. CoRR Gabriel G. Erion, Hugh Chen, Scott M. Lundberg, Su-In Lee
2017	J	`jnl`	Checkpoint Ensembles: Ensemble Methods from a Single Training Process. CoRR Hugh Chen, Scott M. Lundberg, Su-In Lee
2017	J	`jnl`	Consistent feature attribution for tree ensembles. CoRR Scott M. Lundberg, Su-In Lee
2016	J	`jnl`	An unexpected unity among methods for interpreting model predictions. CoRR Scott M. Lundberg, Su-In Lee
2016	—	`conf`	CloudControl: Leveraging many public ChIP-seq control experiments to better remove background noise. BCB Naozumi Hiranuma, Scott M. Lundberg, Su-In Lee
2016	—	`conf`	Fast and reliable tracking algorithm for on-road vehicle detection systems. ICUFN Jang-Woon Baek, Byung-Gil Han, Hyunwoo Kang, Yoonsu Chung, Su-In Lee
2016	J	`jnl`	Identifying Network Perturbation in Cancer. PLoS Comput. Biol. Maxim Grechkin, Benjamin A. Logsdon, Andrew J. Gentles, Su-In Lee
2016	—	`conf`	Learning Sparse Gaussian Graphical Models with Overlapping Blocks. NIPS Mohammad Javad Hosseini, Su-In Lee
2016	—	`conf`	UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields. SemEval@NAACL-HLT Mohammad Javad Hosseini, Noah A. Smith, Su-In Lee
2015	A*	`conf`	Pathway Graphical Lasso. AAAI Maxim Grechkin, Maryam Fazel, Daniela M. Witten, Su-In Lee
2014	A*	`conf`	Efficient Dimensionality Reduction for High-Dimensional Network Estimation. ICML Safiye Celik, Benjamin A. Logsdon, Su-In Lee
2014	J	`jnl`	Learning graphical models with hubs. J. Mach. Learn. Res. Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, Daniela M. Witten
2014	J	`jnl`	Node-based learning of multiple Gaussian graphical models. J. Mach. Learn. Res. Karthik Mohan, Palma London, Maryam Fazel, Daniela M. Witten, Su-In Lee
2014	J	`jnl`	Track Topology Based Reliable In-Network Aggregation Scheduling in Wireless Sensor Networks. IEICE Trans. Commun. Jang-Woon Baek, Kee-Koo Kwon, Su-In Lee, Dae-Wha Seo
2013	J	`jnl`	Node-Based Learning of Multiple Gaussian Graphical Models CoRR Karthik Mohan, Palma London, Maryam Fazel, Daniela M. Witten, Su-In Lee
2013	—	`conf`	The use of pseudo-landmarks for craniofacial analysis: A comparative study with L1-regularized logistic regression. EMBC Ezgi Mercan, Linda G. Shapiro, Seth M. Weinberg, Su-In Lee
2012	—	`conf`	Parcellation of human inferior parietal lobule based on diffusion MRI. EMBC Bilge Soran, Zhiyong Xie, Rosalia F. Tungaraza, Su-In Lee, Linda G. Shapiro, Thomas J. Grabowski
2012	—	`conf`	Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models. MCBR-CDS Shulin Yang, Linda G. Shapiro, Michael L. Cunningham, Matthew L. Speltz, Craig Birgfeld, Indriyati Atmosukarto, Su-In Lee
2012	—	`conf`	Structured Learning of Gaussian Graphical Models. NIPS Karthik Mohan, Michael Jae-Yoon Chung, Seungyeop Han, Daniela M. Witten, Su-In Lee, Maryam Fazel
2012	B	`conf`	Tremor detection using motion filtering and SVM. ICPR Bilge Soran, Jenq-Neng Hwang, Su-In Lee, Linda G. Shapiro
2011	—	`conf`	Classification and feature selection for craniosynostosis. BCB Shulin Yang, Linda G. Shapiro, Michael L. Cunningham, Matthew L. Speltz, Su-In Lee
2007	A*	`conf`	Learning a meta-level prior for feature relevance from multiple related tasks. ICML Su-In Lee, Vassil Chatalbashev, David Vickrey, Daphne Koller
2006	A*	`conf`	Efficient L1 Regularized Logistic Regression. AAAI Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. Ng
2006	—	`conf`	Efficient Structure Learning of Markov Networks using L NIPS Su-In Lee, Varun Ganapathi, Daphne Koller
2003	—	`conf`	ICA-based Clustering of Genes from Microarray Expression Data. NIPS Su-In Lee, Serafim Batzoglou
2000	—	`conf`	Top-Down Attention Control at Feature Space for Robust Pattern Recognition. Biologically Motivated Computer Vision Su-In Lee, Soo-Young Lee