DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values

ICML 2025 Workshop on Actionable Interpretability (ICMLW-AIW) DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values Changhun Kim, Yechan Mun, Sangchul Hahn, Eunho Yang  This study proposes DeltaSHAP, a novel explainable artificial ...

PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency

ICML 2024 PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency  Yeonsung Jung, Heecheol Yun, Joonhyung Park, Jin-Hwa Kim, Eunho Yang  Neural Radiance Fields (NeRF) have shown remarkable performance in learning 3D scenes. However, NeRF exhibits vulnerability wh...

Traversing Between Modes in Function Space for Fast Ensembling

ICML 2023  (TML4H workshop)  Traversing Between Modes in Function Space for Fast Ensembling  Eunggu Yun, Hyungi Lee, Giung Nam, Juho Lee    Deep ensemble is a simple yet powerful way to improve the performance of deep neural networks. Under this moti...

RGE: A Repulsive Graph Rectification for Node Classification via Influence

ICML 2023 RGE: A Repulsive Graph Rectification for Node Classification via Influence  Jaeyun Song*, Sung-Yub Kim* and Eunho Yang (*: equal contribution)   In real-world graphs, noisy connections are inevitable, which makes it difficult to obtain unbiased node repre...

Probabilistic Imputation for Time-series Classification with Missing Data

ICML 2023 Probabilistic Imputation for Time-series Classification with Missing Data  SeungHyun Kim, Hyunsu Kim, Eunggu Yun, Hwangrae Lee, Jaehun Lee, Juho Lee    Multivariate time series data for real-world applications typically contain a significant amount o...

Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation

ICML 2023 Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation  Yeonsung Jung, Hajin Shim, June Yong Yang and Eunho Yang   Deep neural networks (DNNs), despite their impressive ability to generalize over-capacity ...

TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification

ICML 2022 TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification Jaeyun Song, Joonhyung Park, Eunho Yang  Learning unbiased node representations under class-imbalanced graph data is challenging due to interactions between adjacent nodes. Existing studies have in...

Set Based Stochastic Subsampling

ICML 2022 Set Based Stochastic Subsampling Bruno Andreis, Seanie Lee, A. Tuan Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang  Deep models are designed to operate on huge volumes of high dimensional data such as images. In order to reduce the volume of data these models must process,...

Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

ICML 2022 Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations Jaehyeong Jo, Seul Lee, Sung Ju Hwang  Generating graph-structured data requires learning the underlying distribution of graphs. Yet, this is a challenging problem, and the pre...

Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation

ICML 2022 Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation  Giung Nam, Hyungi Lee, Byeongho Heo, Juho Lee   Ensembles of deep neural networks have demonstrated superior performance, but their heavy computational cost hinders applying t...