Lee, Su-In

33 publications

ICLR 2025 An Efficient Framework for Crediting Data Contributors of Diffusion Models MingYu Lu, Chris Lin, Chanwoo Kim, Su-In Lee

NeurIPS 2025 CellCLIP - Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning MingYu Lu, Ethan Weinberger, Chanwoo Kim, Su-In Lee

ICLRW 2025 CellCLIP - Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning MingYu Lu, Ethan Weinberger, Su-In Lee

ICLR 2024 Estimating Conditional Mutual Information for Dynamic Feature Selection Soham Gadgil, Ian Connick Covert, Su-In Lee

NeurIPS 2024 Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution Ian Covert, Chanwoo Kim, Su-In Lee, James Zou, Tatsunori Hashimoto

NeurIPSW 2023 A Deep Generative Model of Single-Cell Methylomic Data Ethan Weinberger, Su-In Lee

ICLR 2023 Contrastive Corpus Attribution for Explaining Representations Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee

ICMLW 2023 Explanation-Guided Dynamic Feature Selection for Medical Risk Prediction Nicasia Beebe-Wang, Wei Qiu, Su-In Lee

NeurIPS 2023 Feature Selection in the Contrastive Analysis Setting Ethan Weinberger, Ian Covert, Su-In Lee

ICLR 2023 Learning to Estimate Shapley Values with Vision Transformers Ian Connick Covert, Chanwoo Kim, Su-In Lee

ICML 2023 Learning to Maximize Mutual Information for Dynamic Feature Selection Ian Connick Covert, Wei Qiu, Mingyu Lu, Na Yoon Kim, Nathan J White, Su-In Lee

NeurIPS 2023 On the Robustness of Removal-Based Feature Attributions Chris Lin, Ian Covert, Su-In Lee

AISTATS 2022 Moment Matching Deep Contrastive Latent Variable Models Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee

ICLR 2022 FastSHAP: Real-Time Shapley Value Estimation Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath

ICLRW 2022 Isolating Salient Variations of Interest in Single-Cell Transcriptomic Data with contrastiveVI Ethan Weinberger, Chris Lin, Su-In Lee

AISTATS 2021 Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression Ian Covert, Su-In Lee

JMLR 2021 Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek, Pascal Sturmfels, Su-In Lee

JMLR 2021 Explaining by Removing: A Unified Framework for Model Explanation Ian Covert, Scott Lundberg, Su-In Lee

NeurIPS 2020 Learning Deep Attribution Priors Based on Prior Knowledge Ethan Weinberger, Joseph Janizek, Su-In Lee

ICLR 2020 Learning Explainable Models Using Attribution Priors Gabriel Erion, Joseph D. Janizek, Pascal Sturmfels, Scott Lundberg, Su-In Lee

NeurIPS 2020 Understanding Global Feature Contributions with Additive Importance Measures Ian Covert, Scott M Lundberg, Su-In Lee

Distill 2020 Visualizing the Impact of Feature Attribution Baselines Pascal Sturmfels, Scott Lundberg, Su-In Lee

NeurIPS 2017 A Unified Approach to Interpreting Model Predictions Scott M Lundberg, Su-In Lee

NeurIPS 2016 Learning Sparse Gaussian Graphical Models with Overlapping Blocks Mohammad Javad Hosseini, Su-In Lee

AAAI 2015 Pathway Graphical Lasso Maxim Grechkin, Maryam Fazel, Daniela M. Witten, Su-In Lee

ICML 2014 Efficient Dimensionality Reduction for High-Dimensional Network Estimation Safiye Celik, Benjamin Logsdon, Su-In Lee

JMLR 2014 Learning Graphical Models with Hubs Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, Daniela Witten

JMLR 2014 Node-Based Learning of Multiple Gaussian Graphical Models Karthik Mohan, Palma London, Maryam Fazel, Daniela Witten, Su-In Lee

NeurIPS 2012 Structured Learning of Gaussian Graphical Models Karthik Mohan, Mike Chung, Seungyeop Han, Daniela Witten, Su-in Lee, Maryam Fazel

ICML 2007 Learning a Meta-Level Prior for Feature Relevance from Multiple Related Tasks Su-In Lee, Vassil Chatalbashev, David Vickrey, Daphne Koller

AAAI 2006 Efficient L1 Regularized Logistic Regression Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. Ng

NeurIPS 2006 Efficient Structure Learning of Markov Networks Using $l_1$-Regularization Su-in Lee, Varun Ganapathi, Daphne Koller

NeurIPS 2003 ICA-Based Clustering of Genes from Microarray Expression Data Su-in Lee, Serafim Batzoglou