Journal
Deep neural networks with dependent weights: Gaussian process mixture limit, heavy tails, sparsity and compressibility
Hoil Lee, Fadhel Ayed, Paul Jung, Juho Lee, Hongseok Yang, François Caron
Journal of Machine Learning Research, September 2023
A unified construction for series representations and finite approximations of completely random measures
Juho Lee, Xenia Miscouridou, François Caron
Bernoulli, August 2023
The Normal-Generalised Gamma-Pareto process: A novel pure-jump Lévy process with flexible tail and jump-activity properties
Fadhel Ayed, Juho Lee, François Caron
Bayesian Anaylsis, December 2022
Benefits of stochastic weight averaging in developing neural network radiation scheme for numerical weather prediction
Hwan-Jin Song, Soonyoung Roh, Juho Lee, Giung Nam, Eunggu Yun, Jongmin Yoon, Park Sa Kim
Journal of Advances in Modeling Earth Systems, October 2022
Conference
Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
Hyunsu Kim, Giung Nam, Chulhee Yun, Hongseok Yang, and Juho Lee
International Conference on Learning Representations (ICLR), 2025
Dimension Agnostic Neural Processes
Hyungi Lee, Chaeyun Jang, Dong Bok Lee, and Juho Lee
International Conference on Learning Representations (ICLR), 2025
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Variational Bayesian Pseudo-Coreset
Hyungi Lee, Seungyoo Lee, and Juho Lee
International Conference on Learning Representations (ICLR), 2025
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Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning
Seanie Lee, Minsu Kim, Lynn Cherif, David Dobre, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, and Moksh Jain
International Conference on Learning Representations (ICLR), 2025
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HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models
Seanie Lee*, Haebin Seong*, Dong Bok Lee, Minki Kang, Xiaoyin Chen, Dominik Wagner, Yoshua Bengio, Juho Lee, and Sung Ju Hwang
International Conference on Learning Representations (ICLR), 2025
Model fusion through Bayesian optimization in language model fine-tuning
Chaeyun Jang*, Hyungi Lee*, Jungtaek Kim†, Juho Lee†
Neural Information Processing Systems (NeurIPS), 2024
(Spotlight Presentation)
Ex uno pluria: insights on ensembling in low precision number systems
Giung Nam, Juho Lee
Neural Information Processing Systems (NeurIPS), 2024
Learning infinitesimal generators of continuous symmetries from data
Gyeonghoon Ko, Hyunsu Kim, Juho Lee
Neural Information Processing Systems (NeurIPS), 2024
Variational partial group convolutions for input-aware partial equivariance of rotations and color-shifts
Hyunsu Kim, Yegon Kim, Hongseok Yang, Juho Lee
International Conference on Machine Learning (ICML), 2024
A simple early exiting framework for accelerated sampling in diffusion models
Taehong Moon, Moonseok Choi, EungGu Yun, Jongmin Yoon, Gayoung Lee, Jaewoong Cho, Juho Lee
International Conference on Machine Learning (ICML), 2024
Learning to explore for stochastic gradient MCMC
Seunghyun Kim*, Seohyeon Jung*, Seonghyeon Kim, Juho Lee
International Conference on Machine Learning (ICML), 2024
Fast ensembling with diffusion Schrödinger bridge
Hyunsu Kim*, Jongmin Yoon*, Juho Lee
International Conference on Learning Representations (ICLR), 2024
Sparse weight averaging with multiple particles for iterative magnitude pruning
Moonseok Choi*, Hyungi Lee*, Giung Nam*, Juho Lee
International Conference on Learning Representations (ICLR), 2024
Lipsum-FT: robust fine-tuning of zero-shot models using random text guidance
Giung Nam, Byeongho Heo, Juho Lee
International Conference on Learning Representations (ICLR), 2024
Enhancing transfer learning with flexible nonparametric posterior sampling
Hyungi Lee*, Giung Nam*, Edwin Fong, Juho Lee
International Conference on Learning Representations (ICLR), 2024
Self-supervised dataset distillation for transfer learning
Dong Bok Lee*, Seanie Lee*, Joonho Ko, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang
International Conference on Learning Representations (ICLR), 2024
Spear and shield: adversarial attacks and defense methods for model-based link prediction on continuous-time dynamic graphs
Dongjin Lee, Juho Lee, Kijung Shin
Association for the Advancement of Artificial Intelligence (AAAI), 2024
Function space Bayesian pseudocoreset for Bayesian neural networks
Balhae Kim, Hyungi Lee, Juho Lee
Neural Information Processing Systems (NeurIPS), 2023
Probabilistic imputation for time-series classification with missing data
SeungHyun Kim*, Hyunsu Kim*, Eunggu Yun*, Hwangrae Lee, Jaehun Lee, Juho Lee
International Conference on Machine Learning (ICML), 2023
Traversing between modes in function space for fast ensembling
Eunggu Yun*, Hyungi Lee*, Giung Nam*, Juho Lee
International Conference on Machine Learning (ICML), 2023
Regularizing towards soft equivariance under mixed symmetries
Hyunsu Kim, Hyungi Lee, Hongseok Yang, Juho Lee
International Conference on Machine Learning (ICML), 2023
Scalable set encoding with universal mini-batch consistency and unbiased full set gradient approximation
Jeffrey Ryan Willette*, Seanie Lee*, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang
International Conference on Machine Learning (ICML), 2023
Martingale posterior neural processes
Hyungi Lee, Eunggu Yun, Giung Nam, Edwin Fong, Juho Lee
International Conference on Learning Representations (ICLR), 2023
(Spotlight Presentation; notable-top-25%)
Decoupled training for long-tailed classification with stochastic representations
Giung Nam*, Sunguk Jang*, Juho Lee
International Conference on Learning Representations (ICLR), 2023
A simple yet powerful deep active learning with snapshot ensembles
Seohyeon Jung*, Sanghyun Kim*, Juho Lee
International Conference on Learning Representations (ICLR), 2023
Self-distillation for further pre-training of transformers
Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi
International Conference on Learning Representations (ICLR), 2023
Exploring the role of mean teachers in self-supervised masked auto-encoders
Youngwan Lee*, Jeffrey Ryan Willette*, Jonghee Kim, Juho Lee, Sung Ju Hwang
International Conference on Learning Representations (ICLR), 2023
On divergence measures for Bayesian pseudocoresets
Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee
Neural Information Processing Systems (NeurIPS), 2022
Set-based meta-interpolation for few-task meta-learning
Seanie Lee*, Bruno Andreis*, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang
Neural Information Processing Systems (NeurIPS), 2022
Set based stochastic subsampling
Bruno Andreis, Seanie Lee, A. Tuan Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang
International Conference on Machine Learning (ICML), 2022
Sequential Reptile: inter-task gradient alignment for multilingual learning
Seanie Lee*, Hae Beom Lee*, Juho Lee, Sung Ju Hwang
International Conference on Learning Representations (ICLR), 2022
Meta learning low rank covariance factors for energy-based deterministic uncertainty
Jeffrey Ryan Willette, Hae Beom Lee, Juho Lee, Sung Ju Hwang
International Conference on Learning Representations (ICLR), 2022
Mini-batch consistent slot set encoder for scalable set encoding
Andreis Bruno, Jeffrey Ryan Willette, Juho Lee, Sung Ju Hwang
Neural Information Processing Systems (NeurIPS), 2021
A multi-mode modulator for multi-domain few-shot classification
Yanbin Liu, Juho Lee, Linchao Zhu, Ling Chen, Humphrey Shi, Yi Yang
International Conference on Computer Vision (ICCV), 2021
Learning to perturb word embeddings for out-of-distribution QA
Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang
Association for Computational Linguistics (ACL), 2021
Cost-effective interactive attention learning with neural attention processes
Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang
International Conference on Machine Learning (ICML), 2020
Deep mixed effect model using Gaussian processes: a personalized and reliable prediction for healthcare
Ingyo Chung, Saehoon Kim, Juho Lee, Sung Ju Hwang, Eunho Yang
Association for the Advancement of Artificial Intelligence (AAAI), 2020
Beyond the Chinese restaurant and Pitman-Yor processes: statistical models with double power-law behavior
Fadhel Ayed*, Juho Lee*, François Caron
International Conference on Machine Learning (ICML), 2019
(Long Oral Presentation)
Learning to propagate labels: transductive propagation network for few-shot learning
Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang
International Conference on Learning Representations (ICLR), 2019
Uncertainty-aware attention for reliable interpretation and prediction
Jay Heo*, Hae Beom Lee*, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, Sung Ju Hwang
Neural Information Processing Systems (NeurIPS), 2018
Finite-dimensional BFRY priors and variational Bayesian inference for power law models
Juho Lee, Lancelot F. James, Seungjin Choi
Neural Information Processing Systems (NIPS; NeurIPS), 2016
Bayesian hierarchical clustering with exponential family: small-variance asymptotics and reducibility
Juho Lee, Seungjin Choi
International Conference on Artificial Intelligence and Statistics (AISTATS), 2015
Incremental tree-based inference with dependent normalized random measures
Juho Lee, Seungjin Choi
International Conference on Artificial Intelligence and Statistics (AISTATS), 2014
Online video segmentation by Bayesian split-merge clustering
Juho Lee, Suha Kwak, Bohyung Han, Seungjin Choi
European Conference on Computer Vision (ECCV), 2012
Symposium and Workshop
Learning Dynamic Brain Connectome with Graph Transformers for Psychiatric Diagnosis Classification
Byung-Hoon Kim*, Jungwon Choi*, EungGu Yun, Kyungsang Kim, Xiang Li, Juho Lee
IEEE International Symposium on Biomedical Imaging (ISBI), 2024
(Oral Presentation)
A generative self-supervised framework using functional connectivity in fMRI data
Jungwon Choi, Seongho Keum, EungGu Yun, Byung-Hoon Kim, Juho Lee
NeurIPS 2023 workshop on Temporal Graph Learning, 2023
Large-scale graph representation learning of dynamic brain connectome with transformers
Byung-Hoon Kim*, Jungwon Choi*, EungGu Yun, Kyungsang Kim, Xiang Li, Juho Lee
NeurIPS 2023 workshop on Temporal Graph Learning, 2023
Early exiting for accelerated inference in diffusion models
Taehong Moon, Moonseok Choi, EungGu Yun, Jongmin Yoon, Gayoung Lee, Juho Lee
ICML 2023 workshop on Structured Probabilistic Inference & Generative Modeling, 2023
Function space Bayesian pseudocoreset for Bayesian neural networks
Balhae Kim, Hyungi Lee, Juho Lee
ICML 2023 workshop on Structured Probabilistic Inference & Generative Modeling, 2023
Towards safe self-distillation of internet-scale text-to-image diffusion models
Sanghyun Kim, Seohyeon Jung, Balhae Kim, Moonseok Choi, Jinwoo Shin, Juho Lee
ICML 2023 Workshop on Challenges in Deployable Generative AI, 2023
Modeling uplift from observational time-series in continual scenarios
Sanghyun Kim, Jungwon Choi, NamHee Kim, Jaesung Ryu, Juho Lee
AAAI-23 Bridge on Continual Casality, 2023
(Oral Presentation)
Fine-tuning diffusion models with limited data
Taehong Moon, Moonseok Choi, Gayoung Lee, Jung-Woo Ha, Juho Lee
NeurIPS 2022 Workshop on Score-Based Methods, 2022
Adaptive strategy for resetting a non-stationary Markov chain during learning via joint stochastic optimization
Hyunsu Kim, Juho Lee, Hongseok Yang
Third Symposium on Advances in Approximate Bayesian Inference, 2021
Towards deep amortized clustering
Juho Lee, Yoonho Lee, Yee Whye Teh
NeurIPS 2019 Sets & Partitions workshop, 2019
(Contributed Talk)
Graph embedding VAE: a permutation invariant model of graph structure
Tony Duan, Juho Lee
NeurIPS 2019 Graph Representation Learning workshop, 2019