Yoonho Lee

Orcid: 0000-0002-5146-5444

Affiliations:
  • Stanford University, CA, USA
  • AITRICS, Seoul, Korea (former)


According to our database1, Yoonho Lee authored at least 29 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2024
Conservative Prediction via Data-Driven Confidence Minimization.
Trans. Mach. Learn. Res., 2024

Test-Time Alignment via Hypothesis Reweighting.
CoRR, 2024

Bidirectional Decoding: Improving Action Chunking via Closed-Loop Resampling.
CoRR, 2024

AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data.
CoRR, 2024

Clarify: Improving Model Robustness With Natural Language Corrections.
Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology, 2024

Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Calibrating Language Models with Adaptive Temperature Scaling.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts.
CoRR, 2023

Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features.
CoRR, 2023

DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature.
Proceedings of the International Conference on Machine Learning, 2023

Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Surgical Fine-Tuning Improves Adaptation to Distribution Shifts.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Discrete Infomax Codes for Supervised Representation Learning.
Entropy, 2022

Diversify and Disambiguate: Learning From Underspecified Data.
CoRR, 2022

Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Divergence Measures for Bayesian Pseudocoresets.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
On the distribution of penultimate activations of classification networks.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Diversity Matters When Learning From Ensembles.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Attentive Clustering Processes.
CoRR, 2020

Bootstrapping neural processes.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Neural Complexity Measures.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Deep Amortized Clustering.
CoRR, 2019

Discrete Infomax Codes for Meta-Learning.
CoRR, 2019

Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Set Transformer.
CoRR, 2018

Meta-Learning with Adaptive Layerwise Metric and Subspace.
CoRR, 2018

Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace.
Proceedings of the 35th International Conference on Machine Learning, 2018


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