We stand with Ukraine

We stand with Ukraine

Yutao Zhong

Affiliations:

New York University, Courant Institute of Mathematical Sciences, NY, USA

According to our database¹, Yutao Zhong authored at least 18 papers between 2021 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

On csauthors.net:

Bibliography

2024

Top-k Classification and Cardinality-Aware Prediction.

[BibT_eX]

[DOI]

,

,

CoRR, 2024

Regression with Multi-Expert Deferral.

[BibT_eX]

[DOI]

,

,

CoRR, 2024

H-Consistency Guarantees for Regression.

[BibT_eX]

[DOI]

,

,

CoRR, 2024

2023

Principled Approaches for Learning to Defer with Multiple Experts.

[BibT_eX]

[DOI]

,

,

CoRR, 2023

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

[BibT_eX]

[DOI]

,

,

CoRR, 2023

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

[BibT_eX]

[DOI]

,

,

CoRR, 2023

Ranking with Abstention.

[BibT_eX]

[DOI]

,

,

CoRR, 2023

Two-Stage Learning to Defer with Multiple Experts.

[BibT_eX]

[DOI]

,

Christopher Mohri

,

,

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]

Pranjal Awasthi

,

,

,

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

2022

H-Consistency Estimation Error of Surrogate Loss Minimizers.

[BibT_eX]

[DOI]

Pranjal Awasthi

,

,

,

CoRR, 2022

Multi-Class $H$-Consistency Bounds.

[BibT_eX]

[DOI]

Pranjal Awasthi

,

,

,

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]

Pranjal Awasthi

,

,

,

Proceedings of the International Conference on Machine Learning, 2022

2021

A Finer Calibration Analysis for Adversarial Robustness.

[BibT_eX]

[DOI]

Pranjal Awasthi

,

,

,

CoRR, 2021

Calibration and Consistency of Adversarial Surrogate Losses.

[BibT_eX]

[DOI]

Pranjal Awasthi

,

,

,

,

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

Loading...