Lee, Kibok

23 publications

AISTATS 2025 A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning Chungpa Lee, Jeongheon Oh, Kibok Lee, Jy-yong Sohn

ICCV 2025 Automated Model Evaluation for Object Detection via Prediction Consistency and Reliability Seungju Yoo, Hyuk Kwon, Joong-Won Hwang, Kibok Lee

ICML 2025 Channel Normalization for Time Series Channel Identification Seunghan Lee, Taeyoung Park, Kibok Lee

ICML 2025 On the Similarities of Embeddings in Contrastive Learning Chungpa Lee, Sehee Lim, Kibok Lee, Jy-Yong Sohn

NeurIPS 2025 Soft Task-Aware Routing of Experts for Equivariant Representation Learning Jaebyeong Jeon, Hyeonseo Jang, Jy-yong Sohn, Kibok Lee

AAAI 2025 To Predict or Not to Predict? Proportionally Masked Autoencoders for Tabular Data Imputation Jungkyu Kim, Kibok Lee, Taeyoung Park

NeurIPS 2024 ANT: Adaptive Noise Schedule for Time Series Diffusion Models Seunghan Lee, Kibok Lee, Taeyoung Park

ICLR 2024 Learning to Embed Time Series Patches Independently Seunghan Lee, Taeyoung Park, Kibok Lee

ICML 2024 On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning Jeongheon Oh, Kibok Lee

NeurIPSW 2024 Partial Channel Dependence with Channel Masks for Time Series Foundation Model Seunghan Lee, Taeyoung Park, Kibok Lee

NeurIPSW 2024 Sequential Order-Robust Mamba for Time Series Forecasting Seunghan Lee, Juri Hong, Kibok Lee, Taeyoung Park

ICLR 2024 Soft Contrastive Learning for Time Series Seunghan Lee, Taeyoung Park, Kibok Lee

ECCV 2022 Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer

ICLR 2021 $i$-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee

NeurIPS 2021 Improving Transferability of Representations via Augmentation-Aware Self-Supervision Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, Jinwoo Shin

ICLR 2020 Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning Kimin Lee, Kibok Lee, Jinwoo Shin, Honglak Lee

CVPRW 2019 Incremental Learning with Unlabeled Data in the Wild Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee

ICML 2019 Robust Inference via Generative Classifiers for Handling Noisy Labels Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin

NeurIPS 2018 A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin

ICLR 2018 Training Confidence-Calibrated Classifiers for Detecting Out-of-Distribution Samples Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin

IJCAI 2017 Towards Understanding the Invertibility of Convolutional Neural Networks Anna C. Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee

ICML 2016 Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-Scale Image Classification Yuting Zhang, Kibok Lee, Honglak Lee

AAAI 2015 On the Equivalence of Linear Discriminant Analysis and Least Squares Kibok Lee, Junmo Kim