Su-in Lee
23 papers · 2006–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (11) π Conference Polyglot (6)
π
Academic Marathon
(19)
π
Renaissance Researcher
(5)
π§
Keyword Pioneer
π±
Topic Pioneer
π
Keyword Champion
(2)
π¬
Deep Specialist
(11)
π
Triple Crown
ποΈ
Keyword Collector
(83)
β‘
Prolific Year
(5)
π
Conference Pioneer
π
Trend Setter
π
Century Club
(23)
π₯
Unstoppable
(6)
Conferences
NIPS (9)
ICLR (5)
JMLR (4)
AISTATS (2)
ICML (2)
SEMEVAL (1)
Top co-authors
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