Juho Lee

A Model-Free Universal AI UAI 2026 Active Learning with Selective Time-Step Acquisition for PDEs ICML 2025 Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series ICLR 2025 Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks ICML 2025 Dimension Agnostic Neural Processes ICLR 2025 Ensemble Distribution Distillation via Flow Matching ICML 2025 HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models ICLR 2025 Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning ICLR 2025 Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo ICLR 2025 SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models ACL 2025 StarFT: Robust Fine-tuning of Zero-shot Models via Spuriosity Alignment IJCAI 2025 Variational Bayesian Pseudo-Coreset ICLR 2025 A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models ICML 2024 Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling ICLR 2024 Ex Uno Pluria: Insights on Ensembling in Low Precision Number Systems NeurIPS 2024 Fast Ensembling with Diffusion Schrödinger Bridge ICLR 2024 Learning Infinitesimal Generators of Continuous Symmetries from Data NeurIPS 2024 Learning to Explore for Stochastic Gradient MCMC ICML 2024 Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance ICLR 2024 Model Fusion through Bayesian Optimization in Language Model Fine-Tuning NeurIPS 2024 Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion ECCV 2024 Self-Supervised Dataset Distillation for Transfer Learning ICLR 2024 Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning ICLR 2024 Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs AAAI 2024 Stochastic Optimal Control for Diffusion Bridges in Function Spaces NeurIPS 2024 Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts ICML 2024 A Simple Yet Powerful Deep Active Learning With Snapshots Ensembles ICLR 2023 Decoupled Training for Long-Tailed Classification With Stochastic Representations ICLR 2023 Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility JMLR 2023 Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders ICLR 2023 Function Space Bayesian Pseudocoreset for Bayesian Neural Networks NeurIPS 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 Self-Distillation for Further Pre-training of Transformers ICLR 2023 Traversing Between Modes in Function Space for Fast Ensembling ICML 2023 Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation ICML 2022 Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty ICLR 2022 On Divergence Measures for Bayesian Pseudocoresets NeurIPS 2022 Scale Mixtures of Neural Network Gaussian Processes ICLR 2022 Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning ICLR 2022 Set-based Meta-Interpolation for Few-Task Meta-Learning NeurIPS 2022 Set Based Stochastic Subsampling ICML 2022 Adversarial Purification with Score-based Generative Models ICML 2021 A Multi-Mode Modulator for Multi-Domain Few-Shot Classification ICCV 2021 Diversity Matters When Learning From Ensembles NeurIPS 2021 Learning to Perturb Word Embeddings for Out-of-distribution QA ACL 2021 Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding NeurIPS 2021 SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data CVPR 2021 Bootstrapping neural processes NeurIPS 2020 Cost-Effective Interactive Attention Learning with Neural Attention Processes ICML 2020 Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare AAAI 2020 Neural Complexity Measures NeurIPS 2020 A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure AISTATS 2019 Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior ICML 2019 LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING ICLR 2019 Learning with Limited Data for Multilingual Reading Comprehension EMNLP 2019 Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks ICML 2019 DropMax: Adaptive Variational Softmax NeurIPS 2018 Uncertainty-Aware Attention for Reliable Interpretation and Prediction NeurIPS 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 NeurIPS 2016 Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility AISTATS 2015 Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models NeurIPS 2015 Incremental Tree-Based Inference with Dependent Normalized Random Measures AISTATS 2014