Kibok Lee - RankMe

50 papers A* 17A 1Journal 28Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Calibrated mixup for imbalanced regression on tabular data. Pattern Recognit. Nathaniel Kang, Kibok Lee, Jongho Im
2026	J	`jnl`	When and Where to Reset Matters for Long-Term Test-Time Adaptation. CoRR Taejun Lim, Joong-Won Hwang, Kibok Lee
2025	A	`conf`	A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning. AISTATS Chungpa Lee, Jeongheon Oh, Kibok Lee, Jy-yong Sohn
2025	J	`jnl`	A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning. CoRR Chungpa Lee, Jeongheon Oh, Kibok Lee, Jy-yong Sohn
2025	J	`jnl`	Automated Model Evaluation for Object Detection via Prediction Consistency and Reliablity. CoRR Seungju Yoo, Hyuk Kwon, Joong-Won Hwang, Kibok Lee
2025	A*	`conf`	Channel Normalization for Time Series Channel Identification. ICML Seunghan Lee, Taeyoung Park, Kibok Lee
2025	J	`jnl`	Channel Normalization for Time Series Channel Identification. CoRR Seunghan Lee, Taeyoung Park, Kibok Lee
2025	A*	`conf`	On the Similarities of Embeddings in Contrastive Learning. ICML Chungpa Lee, Sehee Lim, Kibok Lee, Jy-yong Sohn
2025	J	`jnl`	On the Similarities of Embeddings in Contrastive Learning. CoRR Chungpa Lee, Sehee Lim, Kibok Lee, Jy-yong Sohn
2025	J	`jnl`	Soft Task-Aware Routing of Experts for Equivariant Representation Learning. CoRR Jaebyeong Jeon, Hyeonseo Jang, Jy-yong Sohn, Kibok Lee
2025	A*	`conf`	To Predict or Not to Predict? Proportionally Masked Autoencoders for Tabular Data Imputation. AAAI Jungkyu Kim, Kibok Lee, Taeyoung Park
2024	A*	`conf`	ANT: Adaptive Noise Schedule for Time Series Diffusion Models. NeurIPS Seunghan Lee, Kibok Lee, Taeyoung Park
2024	J	`jnl`	ANT: Adaptive Noise Schedule for Time Series Diffusion Models. CoRR Seunghan Lee, Kibok Lee, Taeyoung Park
2024	A*	`conf`	Learning to Embed Time Series Patches Independently. ICLR Seunghan Lee, Taeyoung Park, Kibok Lee
2024	A*	`conf`	On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning. ICML Jeongheon Oh, Kibok Lee
2024	J	`jnl`	On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning. CoRR Jeongheon Oh, Kibok Lee
2024	J	`jnl`	Partial Channel Dependence with Channel Masks for Time Series Foundation Models. CoRR Seunghan Lee, Taeyoung Park, Kibok Lee
2024	J	`jnl`	Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference. CoRR Seongheon Park, Hyuk Kwon, Kwanghoon Sohn, Kibok Lee
2024	J	`jnl`	Sequential Order-Robust Mamba for Time Series Forecasting. CoRR Seunghan Lee, Juri Hong, Kibok Lee, Taeyoung Park
2024	A*	`conf`	Soft Contrastive Learning for Time Series. ICLR Seunghan Lee, Taeyoung Park, Kibok Lee
2024	J	`jnl`	To Predict or Not To Predict? Proportionally Masked Autoencoders for Tabular Data Imputation. CoRR Jungkyu Kim, Kibok Lee, Taeyoung Park
2023	J	`jnl`	Learning to Embed Time Series Patches Independently. CoRR Seunghan Lee, Taeyoung Park, Kibok Lee
2023	J	`jnl`	Soft Contrastive Learning for Time Series. CoRR Seunghan Lee, Taeyoung Park, Kibok Lee
2022	J	`jnl`	ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers. CoRR Achin Jain, Kibok Lee, Gurumurthy Swaminathan, Hao Yang, Bernt Schiele, Avinash Ravichandran, Onkar Dabeer
2022	—	`conf`	Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark. ECCV (20) Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer
2022	J	`jnl`	Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark. CoRR Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer
2021	A*	`conf`	Improving Transferability of Representations via Augmentation-Aware Self-Supervision. NeurIPS Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, Jinwoo Shin
2021	J	`jnl`	Improving Transferability of Representations via Augmentation-Aware Self-Supervision. CoRR Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, Jinwoo Shin
2021	A*	`conf`	i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning. ICLR Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee
2020	A*	`conf`	Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning. ICLR Kimin Lee, Kibok Lee, Jinwoo Shin, Honglak Lee
2020	—	`—`	Robust Deep Learning in the Open World with Lifelong Learning and Representation Learning. Kibok Lee
2020	J	`jnl`	ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds. CoRR Kibok Lee, Zhuoyuan Chen, Xinchen Yan, Raquel Urtasun, Ersin Yumer
2020	J	`jnl`	i-Mix: A Strategy for Regularizing Contrastive Representation Learning. CoRR Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee
2019	J	`jnl`	A Simple Randomization Technique for Generalization in Deep Reinforcement Learning. CoRR Kimin Lee, Kibok Lee, Jinwoo Shin, Honglak Lee
2019	—	`conf`	Incremental Learning with Unlabeled Data in the Wild. CVPR Workshops Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee
2019	J	`jnl`	Incremental Learning with Unlabeled Data in the Wild. CoRR Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee
2019	A*	`conf`	Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild. ICCV Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee
2019	A*	`conf`	Robust Inference via Generative Classifiers for Handling Noisy Labels. ICML Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin
2019	J	`jnl`	Robust Inference via Generative Classifiers for Handling Noisy Labels. CoRR Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin
2018	A*	`conf`	A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. NeurIPS Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin
2018	J	`jnl`	A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. CoRR Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin
2018	A*	`conf`	Hierarchical Novelty Detection for Visual Object Recognition. CVPR Kibok Lee, Kimin Lee, Kyle Min, Yuting Zhang, Jinwoo Shin, Honglak Lee
2018	J	`jnl`	Hierarchical Novelty Detection for Visual Object Recognition. CoRR Kibok Lee, Kimin Lee, Kyle Min, Yuting Zhang, Jinwoo Shin, Honglak Lee
2018	—	`conf`	Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. ICLR (Poster) Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin
2017	A*	`conf`	Towards Understanding the Invertibility of Convolutional Neural Networks. IJCAI Anna C. Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee
2017	J	`jnl`	Towards Understanding the Invertibility of Convolutional Neural Networks. CoRR Anna C. Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee
2017	J	`jnl`	Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. CoRR Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin
2016	A*	`conf`	Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification. ICML Yuting Zhang, Kibok Lee, Honglak Lee
2016	J	`jnl`	Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification. CoRR Yuting Zhang, Kibok Lee, Honglak Lee
2015	A*	`conf`	On the Equivalence of Linear Discriminant Analysis and Least Squares. AAAI Kibok Lee, Junmo Kim