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Yutao Zhong 0002
Person information
- affiliation: New York University, Courant Institute of Mathematical Sciences, NY, USA
Other persons with the same name
- Yutao Zhong 0001 — George Mason University, Department of Computer Science, Fairfax, VA, USA (and 1 more)
- Yutao Zhong 0003 — South China University of Technology, Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, Guangzhou, China
- Yutao Zhong 0004 — Nanjing Normal University, School of Geography, Nanjing, China
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2020 – today
- 2024
- [c15]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention. AISTATS 2024: 4753-4761 - [c14]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms. ALT 2024: 822-867 - [c13]Christopher Mohri, Daniel Andor, Eunsol Choi, Michael Collins, Anqi Mao, Yutao Zhong:
Learning to Reject with a Fixed Predictor: Application to Decontextualization. ICLR 2024 - [c12]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Regression with Multi-Expert Deferral. ICML 2024 - [c11]Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Guarantees for Regression. ICML 2024 - [c10]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Principled Approaches for Learning to Defer with Multiple Experts. ISAIM 2024: 107-135 - [i16]Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Guarantees for Regression. CoRR abs/2403.19480 (2024) - [i15]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Regression with Multi-Expert Deferral. CoRR abs/2403.19494 (2024) - [i14]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Top-k Classification and Cardinality-Aware Prediction. CoRR abs/2403.19625 (2024) - [i13]Anqi Mao, Mehryar Mohri, Yutao Zhong:
A Universal Growth Rate for Learning with Smooth Surrogate Losses. CoRR abs/2405.05968 (2024) - [i12]Corinna Cortes, Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong:
Cardinality-Aware Set Prediction and Top-k Classification. CoRR abs/2407.07140 (2024) - [i11]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Enhanced H-Consistency Bounds. CoRR abs/2407.13722 (2024) - [i10]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer. CoRR abs/2407.13732 (2024) - [i9]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Multi-Label Learning with Stronger Consistency Guarantees. CoRR abs/2407.13746 (2024) - 2023
- [c9]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness. AISTATS 2023: 10077-10094 - [c8]Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Bounds for Pairwise Misranking Loss Surrogates. ICML 2023: 23743-23802 - [c7]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Cross-Entropy Loss Functions: Theoretical Analysis and Applications. ICML 2023: 23803-23828 - [c6]Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Bounds: Characterization and Extensions. NeurIPS 2023 - [c5]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Structured Prediction with Stronger Consistency Guarantees. NeurIPS 2023 - [c4]Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong:
Two-Stage Learning to Defer with Multiple Experts. NeurIPS 2023 - [i8]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Cross-Entropy Loss Functions: Theoretical Analysis and Applications. CoRR abs/2304.07288 (2023) - [i7]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Ranking with Abstention. CoRR abs/2307.02035 (2023) - [i6]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention. CoRR abs/2310.14770 (2023) - [i5]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms. CoRR abs/2310.14772 (2023) - [i4]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Principled Approaches for Learning to Defer with Multiple Experts. CoRR abs/2310.14774 (2023) - 2022
- [c3]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Bounds for Surrogate Loss Minimizers. ICML 2022: 1117-1174 - [c2]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Multi-Class $H$-Consistency Bounds. NeurIPS 2022 - [i3]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Estimation Error of Surrogate Loss Minimizers. CoRR abs/2205.08017 (2022) - 2021
- [c1]Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Calibration and Consistency of Adversarial Surrogate Losses. NeurIPS 2021: 9804-9815 - [i2]Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Calibration and Consistency of Adversarial Surrogate Losses. CoRR abs/2104.09658 (2021) - [i1]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
A Finer Calibration Analysis for Adversarial Robustness. CoRR abs/2105.01550 (2021)
Coauthor Index
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last updated on 2024-09-05 02:07 CEST by the dblp team
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