AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler

NeurIPS Workshop on Table Representation Learning (NeurIPSW-TRL) 2024 AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler  Changhun Kim, Taewon Kim, Seungyeon Woo, June Yong Yang, Eunho Yang   In re...

A Simple Remedy for Dataset Bias via Self-Influence: A Mislabeled Sample Perspective

NeurIPS 2024 A Simple Remedy for Dataset Bias via Self-Influence: A Mislabeled Sample Perspective Yeonsung Jung, Jaeyun Song, June Yong Yang, Jin-Hwa Kim, Sung-Yub Kim, Eunho Yang  Learning generalized models from biased data is an important undertaking toward fairness in de...

Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds

NeurIPS 2023 Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds  Jihun Yun and Eunho Yang   Nowadays, understanding the geometry of the loss landscape shows promise in enhancing a model's generalization ability. In this work, we draw upon prior wo...

GEX: A flexible method for approximating influence via Geometric Ensemble

NeurIPS 2023 GEX: A flexible method for approximating influence via Geometric Ensemble Sung-Yub Kim, Kyungsu Kim and Eunho Yang   Through a deeper understanding of predictions of neural networks, Influence Function (IF) has been applied to various tasks such as detectin...

Function Space Bayesian Pseudocoreset for Bayesian Neural Networks

NeurIPS 2023 Function Space Bayesian Pseudocoreset for Bayesian Neural Networks  Balhae Kim, Hyungi Lee, Juho Lee   A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential information of a large-scale dataset and thus can be used as a proxy da...

Set-based Meta-Interpolation for Few-Task Meta-Learning

NeurIPS 2022 Set-based Meta-Interpolation for Few-Task Meta-Learning Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang  Meta-learning approaches enable machine learning systems to adapt to new tasks given few examples by leveraging knowledge from related tasks. ...

On Divergence Measures for Bayesian Pseudocoresets

NeurIPS 2022 On Divergence Measures for Bayesian Pseudocoresets  Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee   A Bayesian pseudocoreset is a small synthetic dataset for which the posterior over parameters approximates that of the origina...

Graph Self-supervised Learning with Accurate Discrepancy Learning

NeurIPS 2022 Graph Self-supervised Learning with Accurate Discrepancy Learning Dongki Kim, Jinheon Baek, Sung Ju Hwang   Self-supervised learning of graph neural networks (GNNs) aims to learn an accurate representation of the graphs in an unsupervised manner, to obtain trans...

Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning

NeurIPS 2021 Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning Youngkyu Hong, Eunho Yang  Datasets for training machine learning models tend to be biased unless the data is collected with complete care. In such a biased dataset, models are susceptible to...

Task-Adaptive Neural Network Retrieval with Meta-Contrastive Learning

NeurIPS 2021 Task-Adaptive Neural Network Retrieval with Meta-Contrastive Learning Wonyong Jeong, Hayeon Lee,Gun Park, Eunyoung Hyung, Jinheon Baek andSung Ju Hwang  Most conventional Neural Architecture Search (NAS) approaches are limited in that they only generate architectures...