| 2025 | AAAI | To Predict or Not to Predict? Proportionally Masked Autoencoders for Tabular Data Imputation. | Jungkyu Kim, Kibok Lee, Taeyoung Park |
| 2025 | AISTATS | A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning. | Chungpa Lee, Jeongheon Oh, Kibok Lee, Jy-yong Sohn |
| 2025 | ICCV | Automated Model Evaluation for Object Detection Via Prediction Consistency and Reliability. | Seungju Yoo, Hyuk Kwon, Joong-Won Hwang, Kibok Lee |
| 2025 | ICML | On the Similarities of Embeddings in Contrastive Learning. | Chungpa Lee, Sehee Lim, Kibok Lee, Jy-yong Sohn |
| 2025 | ICML | Channel Normalization for Time Series Channel Identification. | Seunghan Lee, Taeyoung Park, Kibok Lee |
| 2024 | ICLR | Soft Contrastive Learning for Time Series. | Seunghan Lee, Taeyoung Park, Kibok Lee |
| 2024 | ICLR | Learning to Embed Time Series Patches Independently. | Seunghan Lee, Taeyoung Park, Kibok Lee |
| 2024 | ICML | On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning. | Jeongheon Oh, Kibok Lee |
| 2022 | ECCV | Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark. | Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer |
| 2021 | ICLR | i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning. | Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee |
| 2020 | ICLR | Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning. | Kimin Lee, Kibok Lee, Jinwoo Shin, Honglak Lee |
| 2019 | CVPR | Incremental Learning with Unlabeled Data in the Wild. | Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee |
| 2019 | ICCV | Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild. | Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee |
| 2019 | ICML | Robust Inference via Generative Classifiers for Handling Noisy Labels. | Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin |
| 2018 | CVPR | Hierarchical Novelty Detection for Visual Object Recognition. | Kibok Lee, Kimin Lee, Kyle Min, Yuting Zhang, Jinwoo Shin, Honglak Lee |
| 2018 | ICLR | Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. | Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin |
| 2017 | IJCAI | Towards Understanding the Invertibility of Convolutional Neural Networks. | Anna C. Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee |
| 2016 | ICML | Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification. | Yuting Zhang, Kibok Lee, Honglak Lee |
| 2015 | AAAI | On the Equivalence of Linear Discriminant Analysis and Least Squares. | Kibok Lee, Junmo Kim |