Publications

[C]onference, [J]ournal, [P]reprint, [W]orkshop, *Equal contribution, †Equal advising

(NEW! ) [W3] Sequential Order-Robust Mamba for Time Series Forecasting

Seunghan Lee*, Juri Hong*, Kibok Lee†, Taeyoung Park†

In NeurIPS Workshop on Time Series in the Age of Large Models (TSALM), 2024. [coming soon]

(NEW! ) [W2] Partial Channel Dependence with Channel Masks for Time Series Foundation Models

Seunghan Lee, Taeyoung Park†, Kibok Lee†

In NeurIPS Workshop on Time Series in the Age of Large Models (TSALM), 2024. Oral presentation [coming soon]

(NEW! ) [C17] ANT: Adaptive Noise Schedule for Time Series Diffusion Models

Seunghan Lee, Kibok Lee†, Taeyoung Park†

In NeurIPS, 2024. [paper, arXiv:2410.14488][GitHub]

(NEW! ) [W1] Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference

Seongheon Park*, Hyuk Kwon*, Kwanghoon Sohn, Kibok Lee

In CVPR Workshop on Computer Vision in the Wild (CVinW), 2024. [arXiv:2405.20829]

(NEW! ) [C16] On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning

Jeongheon Oh, Kibok Lee

In ICML, 2024. [paper, arXiv:2406.10815][GitHub][poster]

(NEW! ) [C15] Learning to Embed Time Series Patches Independently

Seunghan Lee, Taeyoung Park, Kibok Lee

In ICLR, 2024. [paper, arXiv:2312.16427][GitHub]

Preliminary version was presented in NeurIPS Workshop on Self-Supervised Learning: Theory and Practice, 2023. Oral presentation

(NEW! ) [C14] Soft Contrastive Learning for Time Series

Seunghan Lee, Taeyoung Park, Kibok Lee

In ICLR, 2024. Spotlight (366/7262=5%) [paper, arXiv:2312.16424][GitHub]

Preliminary version was presented in NeurIPS Workshop on Self-Supervised Learning: Theory and Practice, 2023.

[P2] ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers

Achin Jain, Kibok Lee, Gurumurthy Swaminathan, Hao Yang, Bernt Schiele, Avinash Ravichandran, Onkar Dabeer

Preprint. [arXiv:2209.05654]

[C13] Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark

Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer

In ECCV, 2022. [paper, arXiv:2207.11169][GitHub]

[C12] Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, Jinwoo Shin

In NeurIPS, 2021. [paper, arXiv:2111.09613][GitHub]

Preliminary version was presented in ICML Workshop on Self-Supervised Learning for Reasoning and Perception, 2021.

[C11] i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee

In ICLR, 2021. [paper, arXiv:2010.08887][GitHub][poster]

Preliminary version was presented in NeurIPS Workshop on Self-Supervised Learning: Theory and Practice, 2020.

[P1] ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds

Kibok Lee, Zhuoyuan Chen, Xinchen Yan, Raquel Urtasun, Ersin Yumer

Preprint. [arXiv:2005.11626]

[C10] Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

Kimin Lee*, Kibok Lee*, Jinwoo Shin, Honglak Lee

In ICLR, 2020. [paper, arXiv:1910.05396][GitHub]

Preliminary version [poster] was presented in NeurIPS Workshop on Deep Reinforcement Learning, 2019. Contributed talk

[C9] Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild

Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee

In ICCV, 2019. [paper, arXiv:1903.12648][GitHub][poster]

Preliminary version [paper][slides] was presented in CVPR Workshop on Uncertainty and Robustness in Deep Visual Learning, 2019. Spotlight

[C8] Robust Inference via Generative Classifiers for Handling Noisy Labels

Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin

In ICML, 2019. Long presentation (159/3424=4.6%) [paper, arXiv:1901.11300][GitHub]

Preliminary version was presented in NeurIPS Workshop on Bayesian Deep Learning, 2018.

[C7] Automatic Correction of Lithography Hotspots with a Deep Generative Model

Woojoo Sim*, Kibok Lee*, Dingdong Yang, Jaeseung Jeong, Ji-Suk Hong, Sooryong Lee, Honglak Lee

In SPIE Advanced Lithography, 2019. Invited (long presentation) [paper]

[C6] A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin

In NeurIPS, 2018. Spotlight (168/4856=3.5%) [paper, arXiv:1807.03888][GitHub]

Preliminary version was presented in ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models, 2018.

Comment: Adversarial samples can be used to validate our proposed method without OOD samples; check the right side of the Table 2.

[C5] Hierarchical Novelty Detection for Visual Object Recognition

Kibok Lee, Kimin Lee, Kyle Min, Yuting Zhang, Jinwoo Shin, Honglak Lee

In CVPR, 2018. [paper, arXiv:1804.00722][GitHub][poster]

Comment: Hierarchical novelty detection is a generalization of generalized zero-shot learning, in the sense that it does not require semantic information about zero-shot classes.

[C4] Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin

In ICLR, 2018. [paper, arXiv:1711.09325][GitHub]

Preliminary version was presented in NeurIPS Workshop on Bayesian Deep Learning, 2017.

[C3] Towards Understanding the Invertibility of Convolutional Neural Networks

Anna Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee

In IJCAI, 2017. [paper, arXiv:1705.08664][slides][poster]

[C2] Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification

Yuting Zhang, Kibok Lee, Honglak Lee

In ICML, 2016. [paper, arXiv:1606.06582][GitHub][slides][poster]

[J1] A Flexible Framework for Online Document Segmentation by Pair-wise Stroke Distance Learning

Adrien Delaye, Kibok Lee

Pattern Recognition, 2015. [paper (ScienceDirect) (ResearchGate)]

[C1] On the Equivalence of Linear Discriminant Analysis and Least Squares

Kibok Lee, Junmo Kim

In AAAI, 2015. [paper (gdrive)] [supplementary]