Anqi Mao

CoRR, 2024

Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer.

[BibT_eX]

[DOI]

CoRR, 2024

Enhanced H-Consistency Bounds.

[BibT_eX]

[DOI]

CoRR, 2024

Cardinality-Aware Set Prediction and Top-k Classification.

[BibT_eX]

[DOI]

CoRR, 2024

A Universal Growth Rate for Learning with Smooth Surrogate Losses.

[BibT_eX]

[DOI]

CoRR, 2024

Top-k Classification and Cardinality-Aware Prediction.

[BibT_eX]

[DOI]

CoRR, 2024

Principled Approaches for Learning to Defer with Multiple Experts.

[BibT_eX]

[DOI]

Proceedings of the Artificial Intelligence and Image Analysis, 2024

H-Consistency Guarantees for Regression.

[BibT_eX]

[DOI]

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

Regression with Multi-Expert Deferral.

[BibT_eX]

[DOI]

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

Differentially Private Domain Adaptation with Theoretical Guarantees.

[BibT_eX]

[DOI]

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

Learning to Reject with a Fixed Predictor: Application to Decontextualization.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Algorithmic Learning Theory, 2024

Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Ranking with Abstention.

[BibT_eX]

[DOI]

CoRR, 2023

Two-Stage Learning to Defer with Multiple Experts.

[BibT_eX]

[DOI]

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

Structured Prediction with Stronger Consistency Guarantees.

[BibT_eX]

[DOI]

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

H-Consistency Bounds: Characterization and Extensions.

[BibT_eX]

[DOI]

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

Cross-Entropy Loss Functions: Theoretical Analysis and Applications.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

H-Consistency Bounds for Pairwise Misranking Loss Surrogates.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

H-Consistency Estimation Error of Surrogate Loss Minimizers.

[BibT_eX]

[DOI]

CoRR, 2022

Multi-Class $H$-Consistency Bounds.

[BibT_eX]

[DOI]

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

H-Consistency Bounds for Surrogate Loss Minimizers.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

A Finer Calibration Analysis for Adversarial Robustness.

[BibT_eX]

[DOI]

CoRR, 2021

Calibration and Consistency of Adversarial Surrogate Losses.

[BibT_eX]

[DOI]

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

2020

Variational training of neural network approximations of solution maps for physical models.

[BibT_eX]

[DOI]

Yingzhou Li

Jianfeng Lu