Juho Lee

You may also find my recent articles on Google Scholar.

*: equal contribution †: co-corresponding

Preprints

• Hybrid generative-contrastive representation learning
  Saehoon Kim, Sungwoong Kim, Juho Lee

• Learning to pool in graph neural networks for extrapolation
  Jihoon Ko, Taehyung Kwon, Kijung Shin, Juho Lee

• Deep amortized clustering
  Juho Lee, Yoonho Lee, Yee Whye Teh
  A preliminary version of this work has been accepted to NeurIPS 2019 Sets & Partitions workshop as an oral presentation.

• Adaptive network sparsification with dependent variational beta-Bernoulli dropout
  Juho Lee, Saehoon Kim, Jaehong Yoon, Hae Beom Lee, Eunho Yang, Sung Ju Hwang

Journals

• Over-parameterised shallow neural networks with asymmetrical node scaling: global convergence guarantees and feature learning
  François Caron, Fadhel Ayed, Paul Jung, Hoil Lee, Juho Lee, Hongseok Yang
  Transactions on Machine Learning Research, 2025

• 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 Analysis, 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

Conferences

• Stochastic optimal control for continuous-time fMRI representation learning
  Joonhyeong Park*, Byoungwoo Park*, Chang-Bae Bang, Jungwon Choi, Hyungjin Chung, Byung-Hoon Kim, Juho Lee
  To appear in ICLR 2026

• Soft equivariance regularization for invariant self-supervised learning
  Joohyung Lee, Changhun Kim, Hyunsu Kim, Kwanhyung Lee, Juho Lee
  To appear in ICLR 2026

• ForestPersons: a large-scale dataset for under-canopy missing person detection
  Deokyun Kim, Jeongjun Lee, Jungwon Choi, Jonggeon Park, Giyoung Lee, Yookyung Kim, Myungseok Ki, Juho Lee, Jihun Cha
  To appear in ICLR 2026

• Cost-sensitive freeze-thaw Bayesian optimization for efficient hyperparameter tuning
  Dong Bok Lee, Aoxuan Silvia Zhang, Byungjoo Kim, Junhyeon Park, Steven Adriaensen, Juho Lee, Sung Ju Hwang, Hae Beom Lee
  NeurIPS 2025

• PANGEA: projection-based augmentation with non-relevant general data for enhanced domain adaptation in LLMs
  Seungyoo Lee, Giung Nam, Moonseok Choi, Hyungi Lee†, Juho Lee†
  NeurIPS 2025

• Axial neural networks for dimension-free foundation models
  Hyunsu Kim, Jonggeon Park, Joan Bruna, Hongseok Yang, Juho Lee
  NeurIPS 2025 (spotlight)

• Test time scaling for neural processes
  Hyungi Lee, Moonseok Choi, Hyunsu Kim, Kyunghyun Cho, Rajesh Ranganath, Juho Lee
  NeurIPS 2025

• FedSVD: adaptive orthogonalization for private federated learning with LoRA
  Seanie Lee, Sangwoo Park, Dong Bok Lee, Dominik Wagner, Haebin Seong, Tobias Bocklet, Juho Lee, Sung Ju Hwang
  NeurIPS 2025

• Reliable decision-making via calibrated retrieval-augmented generation
  Chaeyun Jang, Deukhwan Cho, Seanie Lee, Hyungi Lee†, Juho Lee†
  NeurIPS 2025

• Compact memory for continual learning
  Yohan Jung, Hyungi Lee, Wenlong Chen, Thomas Möllenhoff, Yingzhen Li, Juho Lee, Mohammad Emtiyaz Khan
  NeurIPS 2025

• Bayesian neural scaling laws extrapolation with prior-fitted networks
  Dongwoo Lee, Dong Bok Lee, Steven Adriaensen, Juho Lee, Sung Ju Hwang, Frank Hutter, Seon Joo Kim, and Hae Beom Lee
  ICML 2025

• Ensemble distribution distillation via flow matching
  Jonggeon Park, Giung Nam, Hyunsu Kim, Jongmin Yoon, and Juho Lee
  ICML 2025

• Active learning with selective time-step acquisition for PDEs
  Yegon Kim, Hyunsu Kim, Gyeonghoon Ko, and Juho Lee
  ICML 2025

• StarFT: robust fine-tuning of zero-shot models via spuriosity alignment
  Younghyun Kim*, Jongheon Jeong*, Sangkyung Kwak, Kyungmin Lee, Juho Lee and Jinwoo Shin
  IJCAI 2025

• Parameter expanded stochastic gradient Markov chain Monte Carlo
  Hyunsu Kim, Giung Nam, Chulhee Yun, Hongseok Yang, and Juho Lee
  ICLR 2025

• Dimension agnostic neural processes
  Hyungi Lee, Chaeyun Jang, Dong Bok Lee, and Juho Lee
  ICLR 2025

• Variational Bayesian pseudo-coreset
  Hyungi Lee, Seungyoo Lee, and Juho Lee
  ICLR 2025

• Amortized control of continuous state space Feynman-Kac model for irregular time series
  Byoungwoo Park, Hyungi Lee, and Juho Lee
  ICLR 2025 (oral presentation)

• 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
  ICLR 2025

• 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
  ICLR 2025

• Model fusion through Bayesian optimization in language model fine-tuning
  Chaeyun Jang*, Hyungi Lee*, Jungtaek Kim†, and Juho Lee†
  NeurIPS 2024 (spotlight)

• Ex Uno Pluria: insights on ensembling in low precision number systems
  Giung Nam, Juho Lee
  NeurIPS 2024

• Learning infinitesimal generators of continuous symmetries from data
  Gyeonghoon Ko, Hyunsu Kim, Juho Lee
  NeurIPS 2024

• Stochastic optimal control for diffusion bridges in function spaces
  Byoungwoo Park, Jungwon Choi, Sungbin Lim†, Juho Lee†
  NeurIPS 2024

• Safeguard text-to-image diffusion models with human feedback inversion
  Sanghyun Kim, Seohyeon Jung, Balhae Kim, Moonseok Choi, Jinwoo Shin, Juho Lee
  ECCV 2024

• Variational partial group convolutions for input-aware partial equivariance of rotations and color-shifts
  Hyunsu Kim, Yegon Kim, Hongseok Yang, and Juho Lee
  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, and Juho Lee
  ICML 2024

• Learning to explore for stochastic gradient MCMC
  Seunghyun Kim, Seohyeon Jung, Seonghyeon Kim, and Juho Lee
  ICML 2024

• Fast ensembling with diffusion Schrödinger bridge
  Hyunsu Kim, Jongmin Yoon, and Juho Lee
  ICLR 2024

• Sparse weight averaging with multiple particles for iterative magnitude pruning
  Moonseok Choi, Hyungi Lee, Giung Nam, and Juho Lee
  ICLR 2024

• Lipsum-FT: Robust fine-tuning of zero-shot models using random text guidance
  Giung Nam, Byeongho Heo, and Juho Lee
  ICLR 2024

• Enhancing transfer learning with flexible nonparametric posterior sampling
  Hyungi Lee, Giung Nam, Edwin Fong, and Juho Lee
  ICLR 2024

• Self-supervised dataset distillation for transfer learning
  Dong Bok Lee, Seanie Lee, Joonho Ko, Kenji Kawaguchi, Juho Lee, and Sung Ju Hwang
  ICLR 2024

• Spear and shield: adversarial attacks and defense methods for model-based link prediction on continuous-time dynamic graphs
  Dongjin Lee, Juho Lee, and Kijung Shin
  AAAI 2024

• Function Space Bayesian pseudocoreset for Bayesian neural networks
  Balhae Kim, Hyungi Lee, Juho Lee
  NeurIPS 2023

• Probabilistic imputation for time-series classification with missing data
  SeungHyun Kim*, Hyunsu Kim*, Eunggu Yun*, Hwangrae Lee, Jaehun Lee, Juho Lee
  ICML 2023

• Traversing between modes in function space for fast ensembling
  Eunggu Yun*, Hyungi Lee*, Giung Nam*, Juho Lee
  ICML 2023

• Regularizing towards soft equivariance under mixed symmetries
  Hyunsu Kim, Hyungi Lee, Hongseok Yang, Juho Lee
  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
  ICML 2023

• Martingale posterior neural processes
  Hyungi Lee, Eunggu Yun, Giung Nam, Edwin Fong, Juho Lee
  ICLR 2023 (Notable top 25%)

• Decoupled training for long-tailed classification with stochastic representations
  Giung Nam*, Sunguk Jang*, Juho Lee
  ICLR 2023

• A simple yet powerful deep active learning with snapshot ensembles
  Seohyeon Jung*, Sanghyun Kim*, Juho Lee
  ICLR 2023

• Self-distillation for further pre-training of transformers
  Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi
  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
  ICLR 2023

• On divergence measures for Bayesian pseudocoresets
  Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee
  NeurIPS 2022

• Set-based meta-interpolation for few-task meta-learning
  Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang
  NeurIPS 2022

• Improving ensemble distillation with weight averaging and diversifying perturbation
  Giung Nam, Hyungi Lee, Byeongho Heo, Juho Lee
  ICML 2022
  Code

• Set based stochastic subsampling
  Bruno Andreis, Seanie Lee, A. Tuan Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang
  ICML 2022

• Scale mixtures of neural network Gaussian processes
  Hyungi Lee, Eunggu Yoon, Hongseok Yang, Juho Lee
  ICLR 2022

• Sequential Reptile: inter-task gradient alignment for multilingual learning
  Seanie Lee, Hae Beom Lee, Juho Lee, Sung Ju Hwang
  ICLR 2022

• Meta learning low rank covariance factors for energy-based deterministic uncertainty
  Jeffrey Ryan Willette, Hae Beom Lee, Juho Lee, Sung Ju Hwang
  ICLR 2022

• Diversity matters when learning from ensembles
  Giung Nam*, Jongmin Yoon*, Yoonho Lee, Juho Lee
  NeurIPS 2021
  Code

• Mini-batch consistent slot set encoder for scalable set encoding
  Bruno Andreis, Jeffrey Ryan Willette, Juho Lee, Sung Ju Hwang
  NeurIPS 2021

• A multi-mode modulator for multi-domain few-shot classification
  Yanbin Liu, Juho Lee, Linchao Zhu, Ling Chen, Humphrey Shi, Yi Yang
  ICCV 2021

• Adversarial purification with score-based generative models
  Jongmin Yoon, Sung Ju Hwang, Juho Lee
  ICML 2021
  Code

• Learning to perturb word embeddings for out-of-distribution QA
  Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang
  ACL 2021

• SetVAE: learning hierarchical composition for generative modeling of set-structured data
  Jinwoo Kim, Jaehoon Yoo, Juho Lee, Seunghoon Hong
  CVPR 2021
  Code

• Bootstrapping neural processes
  Juho Lee*, Yoonho Lee*, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh
  Code
  NeurIPS 2020

• Neural complexity measures
  Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, Seungjin Choi
  Code
  NeurIPS 2020

• 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
  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
  AAAI 2020

• Beyond the Chinese restaurant and Pitman-Yor processes: statistical models with double power-law behavior
  Fadhel Ayed*, Juho Lee*, and François Caron
  ICML 2019 (long oral presentation)

• Set transformer: a framework for attention-based permutation-invariant neural networks
  Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, and Yee Whye Teh
  Code
  ICML 2019

• Learning to propagate labels: transductive propagation network for few-shot learning
  Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang and Yi Yang
  ICLR 2019

• A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
  Juho Lee, Lancelot F. James, Seungjin Choi, and François Caron
  AISTATS 2019 (oral presentation)
  Code

• Uncertainty-aware attention for reliable interpretation and prediction
  Jay Heo*, Hae Beom Lee*, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, and Sung Ju Hwang
  NeurIPS 2018

• Dropmax: adaptive variational softmax
  Hae Beom Lee, Juho Lee, Saehoon Kim, Eunho Yang, and Sung Ju Hwang
  NeurIPS 2018
  Code

• Bayesian inference on random simple graphs with power law degree distributions
  Juho Lee, Creighton Heakulani, Zoubin Ghahramani, Lancelot F. James, and Seingjin Choi
  ICML 2017
  Code

• Finite-dimensional BFRY priors and variational Bayesian inference for power law models
  Juho Lee, Lancelot F. James, and Seungjin Choi
  NIPS 2016

• Tree-guided MCMC inference for normalized random measure mixture models
  Juho Lee and Seungjin choi
  NIPS 2015
  Code

• Bayesian hierarchical clustering with exponential family: small-variance asymptotics and reducibility
  Juho Lee and Seungjin Choi
  AISTATS 2015

• Incremental tree-based inference with dependent normalized random measures
  Juho Lee and Seungjin Choi
  AISTATS 2014

• Online video segmentation by Bayesian split-merge clustering
  Juho Lee, Suha Kwak, Bohyung Han, and Seungjin Choi
  ECCV 2012

Workshops

• 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
  AAAI23 Bridge on Continual Casality (oral presentation), 2023

• 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 (contributed talk), 2019

• Graph embedding VAE: a permutation invariant model of graph structure
  Tony Duan, Juho Lee
  NeurIPS 2019 Graph Representation Learning workshop, 2019