Yixuan Li

Int. J. Comput. Vis., February, 2024

On the Learnability of Out-of-distribution Detection.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2024

Are Vision Transformers Robust to Spurious Correlations?

[BibT_eX]

[DOI]

Int. J. Comput. Vis., 2024

Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts.

[BibT_eX]

[DOI]

CoRR, 2024

How Reliable Is Human Feedback For Aligning Large Language Models?

[BibT_eX]

[DOI]

CoRR, 2024

VLMGuard: Defending VLMs against Malicious Prompts via Unlabeled Data.

[BibT_eX]

[DOI]

CoRR, 2024

HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection.

[BibT_eX]

[DOI]

Xuefeng Du

Chaowei Xiao

CoRR, 2024

Your Weak LLM is Secretly a Strong Teacher for Alignment.

[BibT_eX]

[DOI]

Leitian Tao

CoRR, 2024

Out-of-Distribution Learning with Human Feedback.

[BibT_eX]

[DOI]

CoRR, 2024

Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey.

[BibT_eX]

[DOI]

CoRR, 2024

Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models.

[BibT_eX]

[DOI]

CoRR, 2024

DCAI: Data-centric Artificial Intelligence.

[BibT_eX]

[DOI]

Proceedings of the Companion Proceedings of the ACM on Web Conference 2024, 2024

Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

When and How Does In-Distribution Label Help Out-of-Distribution Detection?

[BibT_eX]

[DOI]

Xuefeng Du

Proceedings of the Forty-first International Conference on Machine Learning, 2024

ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

How Does Unlabeled Data Provably Help Out-of-Distribution Detection?

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

HYPO: Hyperspherical Out-Of-Distribution Generalization.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Targeted Representation Alignment for Open-World Semi-Supervised Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection?

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

OpenCon: Open-world Contrastive Learning.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Dream the Impossible: Outlier Imagination with Diffusion Models.

[BibT_eX]

[DOI]

CoRR, 2023

OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection.

[BibT_eX]

[DOI]

CoRR, 2023

Non-Parametric Outlier Synthesis.

[BibT_eX]

[DOI]

CoRR, 2023

Designing Fair AI Systems: Exploring the Interaction of Explainable AI and Task Objectivity on Users' Fairness Perception.

[BibT_eX]

[DOI]

Proceedings of the 27th Pacific Asia Conference on Information Systems, 2023

Learning to Augment Distributions for Out-of-distribution Detection.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning.

[BibT_eX]

[DOI]

Zhenmei Shi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Dream the Impossible: Outlier Imagination with Diffusion Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Mitigating Memorization of Noisy Labels by Clipping the Model Prediction.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Non-parametric Outlier Synthesis.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Critical Analysis of Document Out-of-Distribution Detection.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Distributionally Robust Optimization with Probabilistic Group.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges.

[BibT_eX]

[DOI]

Mohammad Hossein Rohban

Mohammad Sabokrou

Trans. Mach. Learn. Res., 2022

Logit Clipping for Robust Learning against Label Noise.

[BibT_eX]

[DOI]

CoRR, 2022

Open-world Contrastive Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Are Vision Transformers Robust to Spurious Correlations?

[BibT_eX]

[DOI]

CoRR, 2022

CIDER: Exploiting Hyperspherical Embeddings for Out-of-Distribution Detection.

[BibT_eX]

[DOI]

CoRR, 2022

OpenOOD: Benchmarking Generalized Out-of-Distribution Detection.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Delving into Out-of-Distribution Detection with Vision-Language Representations.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Is Out-of-Distribution Detection Learnable?

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

SIREN: Shaping Representations for Detecting Out-of-Distribution Objects.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Mitigating Neural Network Overconfidence with Logit Normalization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Out-of-Distribution Detection with Deep Nearest Neighbors.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

POEM: Out-of-Distribution Detection with Posterior Sampling.

[BibT_eX]

[DOI]

Ying Fan

Proceedings of the International Conference on Machine Learning, 2022

Training OOD Detectors in their Natural Habitats.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

PiCO: Contrastive Label Disambiguation for Partial Label Learning.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

VOS: Learning What You Don't Know by Virtual Outlier Synthesis.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

DICE: Leveraging Sparsification for Out-of-Distribution Detection.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision, 2022

Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

On the Impact of Spurious Correlation for Out-of-Distribution Detection.

[BibT_eX]

[DOI]

Hang Yin

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

On the Effectiveness of Sparsification for Detecting the Deep Unknowns.

[BibT_eX]

[DOI]

CoRR, 2021

ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

ReAct: Out-of-distribution Detection With Rectified Activations.

[BibT_eX]

[DOI]

Chuan Guo

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Robust Out-of-distribution Detection via Informative Outlier Mining.

[BibT_eX]

[DOI]

CoRR, 2020

Robust Out-of-distribution Detection in Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search.

[BibT_eX]

[DOI]

Abhimanyu Dubey

Laurens van der Maaten

Zeki Yalniz

Dhruv Mahajan

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Local Spectral Clustering for Overlapping Community Detection.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2018

Understanding the Loss Surface of Neural Networks for Binary Classification.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

Understanding the Loss Surface of Single-Layered Neural Networks for Binary Classification.

[BibT_eX]

[DOI]

Proceedings of the 6th International Conference on Learning Representations, 2018

Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

[BibT_eX]

[DOI]

Shiyu Liang

R. Srikant

Proceedings of the 6th International Conference on Learning Representations, 2018

Exploring the Limits of Weakly Supervised Pretraining.

[BibT_eX]

[DOI]

Laurens van der Maaten

Proceedings of the Computer Vision - ECCV 2018, 2018

2017

Principled Detection of Out-of-Distribution Examples in Neural Networks.

[BibT_eX]

[DOI]

Shiyu Liang