Improved Reversible Data hiding based on Encrypted Signals with Public Key Cryptosystem. - Ruiping Li; Jing Zhang; Anqi Mao; Ming Li (J. Inf. Hiding Multim. Signal Process., 2018)

Variational training of neural network approximations of solution maps for physical models. - Yingzhou Li; Jianfeng Lu 001; Anqi Mao (J. Comput. Phys., 2020)

Calibration and Consistency of Adversarial Surrogate Losses. - Pranjal Awasthi; Natalie Frank; Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (NeurIPS, 2021)

A Finer Calibration Analysis for Adversarial Robustness. - Pranjal Awasthi; Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (CoRR, 2021)

H-Consistency Bounds for Surrogate Loss Minimizers. - Pranjal Awasthi; Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (ICML, 2022)

Multi-Class $H$-Consistency Bounds. - Pranjal Awasthi; Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (NeurIPS, 2022)

H-Consistency Estimation Error of Surrogate Loss Minimizers. - Pranjal Awasthi; Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (CoRR, 2022)

Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness. - Pranjal Awasthi; Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (AISTATS, 2023)

H-Consistency Bounds for Pairwise Misranking Loss Surrogates. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (ICML, 2023)

Cross-Entropy Loss Functions: Theoretical Analysis and Applications. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (ICML, 2023)

H-Consistency Bounds: Characterization and Extensions. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (NeurIPS, 2023)

Structured Prediction with Stronger Consistency Guarantees. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (NeurIPS, 2023)

Two-Stage Learning to Defer with Multiple Experts. - Anqi Mao; Christopher Mohri; Mehryar Mohri; Yutao Zhong 002 (NeurIPS, 2023)

Ranking with Abstention. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (CoRR, 2023)

Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (AISTATS, 2024)

Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (ALT, 2024)

Learning to Reject with a Fixed Predictor: Application to Decontextualization. - Christopher Mohri; Daniel Andor; Eunsol Choi; Michael Collins 001; Anqi Mao; Yutao Zhong 002 (ICLR, 2024)

Differentially Private Domain Adaptation with Theoretical Guarantees. - Raef Bassily; Corinna Cortes; Anqi Mao; Mehryar Mohri (ICML, 2024)

Regression with Multi-Expert Deferral. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (ICML, 2024)

H-Consistency Guarantees for Regression. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (ICML, 2024)

Principled Approaches for Learning to Defer with Multiple Experts. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (ISAIM, 2024)

Cardinality-Aware Set Prediction and Top-$k$ Classification. - Corinna Cortes; Anqi Mao; Christopher Mohri; Mehryar Mohri; Yutao Zhong 002 (NeurIPS, 2024)

Multi-Label Learning with Stronger Consistency Guarantees. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (NeurIPS, 2024)

A Universal Growth Rate for Learning with Smooth Surrogate Losses. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (NeurIPS, 2024)

Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (NeurIPS, 2024)

Top-k Classification and Cardinality-Aware Prediction. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (CoRR, 2024)

Enhanced H-Consistency Bounds. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (ALT, 2025)

Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data. - Corinna Cortes; Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (CoRR, 2025)

Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defer. - Anqi Mao; Mehryar Mohri; Yutao Zhong 002 (CoRR, 2025)