← Authors

Su-in Lee

23 papers · 2006–2025 · 6 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (11) 🐝 Cross-Pollinator (14) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (19) 🌈 Renaissance Researcher (5) 🧭 Keyword Pioneer 🌱 Topic Pioneer πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (11) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (83) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (23) πŸ”₯ Unstoppable (6)

Conferences

NIPS (9) ICLR (5) JMLR (4) AISTATS (2) ICML (2) SEMEVAL (1)

Top co-authors

Ian Covert (6) Chanwoo Kim (4) Ian Connick Covert (4) Ethan Weinberger (3) Karthik Mohan (3) Maryam Fazel (3) Chris Lin (3) Daniela Witten (3) Scott M Lundberg (2) Mohammad Javad Hosseini (2)

Keywords

feature attribution (5) gaussian graphical model (4) convex optimization (4) model explanation (3) network estimation (3) high-dimensional estimation (2) model interpretability (2) graphical model (2) information theory (2) shapley value (2) gene expression (2) graphical lasso (2) contrastive learning (2) latent variable (2) l1 regularization (1) game theory (1) data valuation (1) representation learning (1) precision matrix (1) feature importance (1)

Papers

An Efficient Framework for Crediting Data Contributors of Diffusion Models ICLR 2025
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution NIPS 2024
Estimating Conditional Mutual Information for Dynamic Feature Selection ICLR 2024
Feature Selection in the Contrastive Analysis Setting NIPS 2023
Learning to Maximize Mutual Information for Dynamic Feature Selection ICML 2023
Learning to Estimate Shapley Values with Vision Transformers ICLR 2023
Contrastive Corpus Attribution for Explaining Representations ICLR 2023
On the Robustness of Removal-Based Feature Attributions NIPS 2023
FastSHAP: Real-Time Shapley Value Estimation ICLR 2022
Moment Matching Deep Contrastive Latent Variable Models AISTATS 2022
Explaining by Removing: A Unified Framework for Model Explanation JMLR 2021
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks JMLR 2021
Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression AISTATS 2021
Understanding Global Feature Contributions With Additive Importance Measures NIPS 2020
Learning Deep Attribution Priors Based On Prior Knowledge NIPS 2020
A Unified Approach to Interpreting Model Predictions NIPS 2017
UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields SEMEVAL 2016
Learning Sparse Gaussian Graphical Models with Overlapping Blocks NIPS 2016
Node-Based Learning of Multiple Gaussian Graphical Models JMLR 2014
Learning Graphical Models With Hubs JMLR 2014
Efficient Dimensionality Reduction for High-Dimensional Network Estimation ICML 2014
Structured Learning of Gaussian Graphical Models NIPS 2012
Efficient Structure Learning of Markov Networks using $L_1$-Regularization NIPS 2006