Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace. - Yoonho Lee 001; Seungjin Choi (ICML, 2018)

Meta-Learning with Adaptive Layerwise Metric and Subspace. - Yoonho Lee 001; Seungjin Choi (CoRR, 2018)

Set Transformer. - Juho Lee 001; Yoonho Lee 001; Jungtaek Kim 001; Adam R. Kosiorek; Seungjin Choi; Yee Whye Teh (CoRR, 2018)

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. - Juho Lee 001; Yoonho Lee 001; Jungtaek Kim 001; Adam R. Kosiorek; Seungjin Choi; Yee Whye Teh (ICML, 2019)

Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning. - Wonjae Kim; Yoonho Lee 001 (NeurIPS, 2019)

Discrete Infomax Codes for Meta-Learning. - Yoonho Lee 001; Wonjae Kim; Seungjin Choi (CoRR, 2019)

Deep Amortized Clustering. - Juho Lee 001; Yoonho Lee 001; Yee Whye Teh (CoRR, 2019)

Neural Complexity Measures. - Yoonho Lee 001; Juho Lee 001; Sung Ju Hwang; Eunho Yang; Seungjin Choi (NeurIPS, 2020)

Bootstrapping neural processes. - Juho Lee 001; Yoonho Lee 001; Jungtaek Kim 001; Eunho Yang; Sung Ju Hwang; Yee Whye Teh (NeurIPS, 2020)

Attentive Clustering Processes. - Ari Pakman; Yueqi Wang; Yoonho Lee 001; Pallab Basu; Juho Lee 001; Yee Whye Teh; Liam Paninski (CoRR, 2020)

Diversity Matters When Learning From Ensembles. - Giung Nam; Jongmin Yoon; Yoonho Lee 001; Juho Lee 001 (NeurIPS, 2021)

On the distribution of penultimate activations of classification networks. - Minkyo Seo; Yoonho Lee 001; Suha Kwak (UAI, 2021)

On Divergence Measures for Bayesian Pseudocoresets. - Balhae Kim; Jungwon Choi; Seanie Lee; Yoonho Lee 001; Jung-Woo Ha 001; Juho Lee 001 (NeurIPS, 2022)

Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time. - Huaxiu Yao; Caroline Choi; Bochuan Cao; Yoonho Lee 001; Pang Wei Koh; Chelsea Finn (NeurIPS, 2022)

Diversify and Disambiguate: Learning From Underspecified Data. - Yoonho Lee 001; Huaxiu Yao; Chelsea Finn (CoRR, 2022)

Discrete Infomax Codes for Supervised Representation Learning. - Yoonho Lee 001; Wonjae Kim; Wonpyo Park; Seungjin Choi (Entropy, 2022)

Surgical Fine-Tuning Improves Adaptation to Distribution Shifts. - Yoonho Lee 001; Annie S. Chen; Fahim Tajwar; Ananya Kumar; Huaxiu Yao; Percy Liang; Chelsea Finn (ICLR, 2023)

Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement. - Yoonho Lee 001; Huaxiu Yao; Chelsea Finn (ICLR, 2023)

DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature. - Eric Mitchell; Yoonho Lee 001; Alexander Khazatsky; Christopher D. Manning; Chelsea Finn (ICML, 2023)

Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features. - Annie S. Chen; Yoonho Lee 001; Amrith Setlur; Sergey Levine; Chelsea Finn (CoRR, 2023)

Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts. - Annie S. Chen; Yoonho Lee 001; Amrith Setlur; Sergey Levine; Chelsea Finn (CoRR, 2023)

Calibrating Language Models with Adaptive Temperature Scaling. - Johnathan Xie; Annie S. Chen; Yoonho Lee 001; Eric Mitchell; Chelsea Finn (EMNLP, 2024)

Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features. - Annie S. Chen; Yoonho Lee 001; Amrith Setlur; Sergey Levine; Chelsea Finn (ICLR, 2024)

Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning. - Johnathan Xie; Yoonho Lee 001; Annie S. Chen; Chelsea Finn (ICLR, 2024)

Clarify: Improving Model Robustness With Natural Language Corrections. - Yoonho Lee 001; Michelle S. Lam; Helena Vasconcelos; Michael S. Bernstein; Chelsea Finn (UIST, 2024)

AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data. - Caroline Choi; Yoonho Lee 001; Annie S. Chen; Allan Zhou; Aditi Raghunathan; Chelsea Finn (CoRR, 2024)

Bidirectional Decoding: Improving Action Chunking via Closed-Loop Resampling. - Yuejiang Liu; Jubayer Ibn Hamid; Annie Xie; Yoonho Lee 001; Maximilian Du; Chelsea Finn (CoRR, 2024)

Test-Time Alignment via Hypothesis Reweighting. - Yoonho Lee 001; Jonathan Williams; Henrik Marklund; Archit Sharma; Eric Mitchell; Anikait Singh; Chelsea Finn (CoRR, 2024)

Conservative Prediction via Data-Driven Confidence Minimization. - Caroline Choi; Fahim Tajwar; Yoonho Lee 001; Huaxiu Yao; Ananya Kumar; Chelsea Finn (Trans. Mach. Learn. Res., 2024)

Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling. - Yuejiang Liu; Jubayer Ibn Hamid; Annie Xie; Yoonho Lee 001; Max Du; Chelsea Finn (ICLR, 2025)