← Authors

Juho Lee

67 papers · 2014–2025 · 13 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸƒ Academic Marathon (11) 🐝 Cross-Pollinator (13) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (9) 🌍 Conference Polyglot (13) 🏠 Conference Loyalist (20) πŸ† Grand Slam πŸ‘‘ Triple Crown 🀝 Dynamic Duo (23) πŸ”₯ Unstoppable (12) πŸ’Ž Century Club (67) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (182) ⚑ Prolific Year (14)

Conferences

ICLR (20) ICML (17) NIPS (15) AISTATS (3) AAAI (2) ACL (2) IJCNLP (2) CVPR (1) ECCV (1) EMNLP (1) ICCV (1) IJCAI (1) JMLR (1)

Top co-authors

Sung Ju Hwang (23) Hyungi Lee (12) Seanie Lee (12) Giung Nam (11) Eunho Yang (8) Hyunsu Kim (8) Seungjin Choi (8) Dong Bok Lee (5) Kenji Kawaguchi (5) Minki Kang (5)

Research topics

Optimization & Theory (1)

Keywords

variational inference (7) bayesian inference (6) markov chain monte carlo (4) attention mechanism (4) question answering (4) neural network (4) representation learning (3) set encoding (3) image classification (3) permutation invariance (3) domain generalization (2) out-of-distribution generalization (2) function space (2) posterior inference (2) model compression (2) bayesian nonparametrics (2) hierarchical clustering (2) weakly supervised learning (2) knowledge distillation (2) uncertainty quantification (2)

Papers

HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models ICLR 2025
Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series ICLR 2025
Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning ICLR 2025
Variational Bayesian Pseudo-Coreset ICLR 2025
SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models ACL 2025
Dimension Agnostic Neural Processes ICLR 2025
Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo ICLR 2025
StarFT: Robust Fine-tuning of Zero-shot Models via Spuriosity Alignment IJCAI 2025
Ensemble Distribution Distillation via Flow Matching ICML 2025
Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks ICML 2025
Active Learning with Selective Time-Step Acquisition for PDEs ICML 2025
Self-Supervised Dataset Distillation for Transfer Learning ICLR 2024
Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion ECCV 2024
Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance ICLR 2024
Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning ICLR 2024
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling ICLR 2024
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models ICML 2024
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts ICML 2024
Learning to Explore for Stochastic Gradient MCMC ICML 2024
Stochastic Optimal Control for Diffusion Bridges in Function Spaces NIPS 2024
Model Fusion through Bayesian Optimization in Language Model Fine-Tuning NIPS 2024
Learning Infinitesimal Generators of Continuous Symmetries from Data NIPS 2024
Ex Uno Pluria: Insights on Ensembling in Low Precision Number Systems NIPS 2024
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs AAAI 2024
Fast Ensembling with Diffusion SchrΓΆdinger Bridge ICLR 2024
Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility JMLR 2023
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks NIPS 2023
A Simple Yet Powerful Deep Active Learning With Snapshots Ensembles ICLR 2023
Decoupled Training for Long-Tailed Classification With Stochastic Representations ICLR 2023
Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders ICLR 2023
Self-Distillation for Further Pre-training of Transformers ICLR 2023
Martingale Posterior Neural Processes ICLR 2023
Probabilistic Imputation for Time-series Classification with Missing Data ICML 2023
Regularizing Towards Soft Equivariance Under Mixed Symmetries ICML 2023
Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation ICML 2023
Traversing Between Modes in Function Space for Fast Ensembling ICML 2023
Set Based Stochastic Subsampling ICML 2022
Set-based Meta-Interpolation for Few-Task Meta-Learning NIPS 2022
On Divergence Measures for Bayesian Pseudocoresets NIPS 2022
Scale Mixtures of Neural Network Gaussian Processes ICLR 2022
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty ICLR 2022
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation ICML 2022
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning ICLR 2022
A Multi-Mode Modulator for Multi-Domain Few-Shot Classification ICCV 2021
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data CVPR 2021
Learning to Perturb Word Embeddings for Out-of-distribution QA ACL 2021
Adversarial Purification with Score-based Generative Models ICML 2021
Learning to Perturb Word Embeddings for Out-of-distribution QA IJCNLP 2021
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding NIPS 2021
Diversity Matters When Learning From Ensembles NIPS 2021
Bootstrapping neural processes NIPS 2020
Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare AAAI 2020
Neural Complexity Measures NIPS 2020
Cost-Effective Interactive Attention Learning with Neural Attention Processes ICML 2020
Learning with Limited Data for Multilingual Reading Comprehension IJCNLP 2019
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure AISTATS 2019
Learning with Limited Data for Multilingual Reading Comprehension EMNLP 2019
LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING ICLR 2019
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks ICML 2019
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior ICML 2019
Uncertainty-Aware Attention for Reliable Interpretation and Prediction NIPS 2018
DropMax: Adaptive Variational Softmax NIPS 2018
Bayesian inference on random simple graphs with power law degree distributions ICML 2017
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models NIPS 2016
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models NIPS 2015
Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility AISTATS 2015
Incremental Tree-Based Inference with Dependent Normalized Random Measures AISTATS 2014