1. Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts
    Annie S. Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn
    [abstract] [paper]
  2. Conservative Prediction via Data-Driven Confidence Minimization
    Caroline Choi*, Fahim Tajwar*, Yoonho Lee*, Huaxiu Yao, Ananya Kumar, Chelsea Finn
    ICLR 2023 workshops: TrustML, ME-FoMo [abstract] [paper] [code]
  3. Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features
    Annie S. Chen*, Yoonho Lee*, Amrith Setlur, Sergey Levine, Chelsea Finn
    ICLR 2023 workshops: TrustML (oral), ME-FoMo [abstract] [paper]
  4. ICML
    DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
    Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D Manning, Chelsea Finn
    ICML 2023 (oral) [abstract] [paper] [website] [code] [demo]
  5. ICLR
    Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
    Yoonho Lee*, Annie S. Chen*, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, Chelsea Finn
    ICLR 2023
    NeurIPS 2022 Workshops: DistShift, ICBINB
    [abstract] [paper] [code]
  6. ICLR
    Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement
    Yoonho Lee, Huaxiu Yao, Chelsea Finn
    ICLR 2023
    ICML workshops: PODS, SCIS
    [abstract] [paper] [website] [code]
  1. Relaxing the Kolmogorov Structure Function for Realistic Computational Constraints
    Yoonho Lee, Chelsea Finn, Stefano Ermon
    NeurIPS 2022 Workshop on Information-Theoretic Principles in Cognitive Systems [abstract] [paper]
  2. NeurIPS
    Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
    Huaxiu Yao*, Caroline Choi*, Bochuan Cao, Yoonho Lee, Pang Wei Koh, Chelsea Finn
    NeurIPS 2022 Datasets & Benchmarks Track
    ICML 2022 Shift Happens Workshop
    [abstract] [paper] [code]
  3. NeurIPS
    On Divergence Measures for Bayesian Pseudocoresets
    Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee
    NeurIPS 2022 [abstract] [paper] [code]
  4. Entropy
    Discrete Infomax Codes for Supervised Representation Learning
    Yoonho Lee, Wonjae Kim, Wonpyo Park, Seungjin Choi
    Entropy Special Issue "Theory and Applications of Information Processing Algorithms" [abstract] [paper]
  1. NeurIPS
    Diversity Matters When Learning From Ensembles
    Giung Nam*, Jongmin Yoon*, Yoonho Lee, Juho Lee
    NeurIPS 2021 [abstract] [paper] [code]
  2. Amortized Probabilistic Detection of Communities in Graphs
    Yueqi Wang*, Yoonho Lee*, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski, Ari Pakman
    [abstract] [paper] [code]
  3. UAI
    On the Distribution of Penultimate Activations of Classification Networks
    Minkyo Seo*, Yoonho Lee*, Suha Kwak
    UAI 2021 [abstract] [paper]
  1. NeurIPS
    Bootstrapping Neural Processes
    Juho Lee*, Yoonho Lee*, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh
    NeurIPS 2020 [abstract] [paper] [video] [code]
  2. NeurIPS
    Neural Complexity Measures
    Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, Seungjin Choi
    NeurIPS 2020 [abstract] [paper] [blog] [video] [code]
  1. NeurIPS-W
    Deep Amortized Clustering
    Juho Lee, Yoonho Lee, Yee Whye Teh
    NeurIPS 2019 Sets and Parts Workshop (oral) [abstract] [paper]
  2. NeurIPS
    Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning
    Wonjae Kim, Yoonho Lee
    NeurIPS 2019 [abstract] [paper] [code]
  3. ICML
    Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
    Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, Yee Whye Teh
    ICML 2019 [abstract] [paper] [code]
  1. ICML
    Gradient-based Meta-learning with Learned Layerwise Metric and Subspace
    Yoonho Lee, Seungjin Choi
    ICML 2018 [abstract] [paper] [video] [code]