Mehryar Mohri

ORCID

Publication Activity (10 Years)

Years Active: 1994-2024
Publications (10 Years): 163

Top Venues

Publications

Anqi Mao, Mehryar Mohri, Yutao Zhong
Top-k Classification and Cardinality-Aware Prediction. CoRR (2024)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Regression with Multi-Expert Deferral. CoRR (2024)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer. CoRR (2024)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Enhanced H-Consistency Bounds. CoRR (2024)
Corinna Cortes, Giulia DeSalvo, Mehryar Mohri
Theory and algorithms for learning with rejection in binary classification. Ann. Math. Artif. Intell. 92 (2) (2024)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Principled Approaches for Learning to Defer with Multiple Experts. ISAIM (2024)
Anqi Mao, Mehryar Mohri, Yutao Zhong
A Universal Growth Rate for Learning with Smooth Surrogate Losses. CoRR (2024)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms. ALT (2024)
Pranjal Awasthi, Corinna Cortes, Yishay Mansour, Mehryar Mohri
A Theory of Learning with Competing Objectives and User Feedback. ISAIM (2024)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Multi-Label Learning with Stronger Consistency Guarantees. CoRR (2024)
Anqi Mao, Mehryar Mohri, Yutao Zhong
H-Consistency Guarantees for Regression. CoRR (2024)
Corinna Cortes, Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong
Cardinality-Aware Set Prediction and Top-k Classification. CoRR (2024)
Pranjal Awasthi, Corinna Cortes, Mehryar Mohri
Best-effort adaptation. Ann. Math. Artif. Intell. 92 (2) (2024)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention. AISTATS (2024)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan
Rate-Preserving Reductions for Blackwell Approachability. CoRR (2024)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan
Pseudonorm Approachability and Applications to Regret Minimization. ALT (2023)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Principled Approaches for Learning to Defer with Multiple Experts. CoRR (2023)
Anqi Mao, Mehryar Mohri, Yutao Zhong
H-Consistency Bounds: Characterization and Extensions. NeurIPS (2023)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms. CoRR (2023)
Anqi Mao, Mehryar Mohri, Yutao Zhong
H-Consistency Bounds for Pairwise Misranking Loss Surrogates. ICML (2023)
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness. AISTATS (2023)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Structured Prediction with Stronger Consistency Guarantees. NeurIPS (2023)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention. CoRR (2023)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan
Pseudonorm Approachability and Applications to Regret Minimization. CoRR (2023)
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
Principled Approaches for Private Adaptation from a Public Source. AISTATS (2023)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Ranking with Abstention. CoRR (2023)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Cross-Entropy Loss Functions: Theoretical Analysis and Applications. CoRR (2023)
Christoph Dann, Yishay Mansour, Mehryar Mohri
Reinforcement Learning Can Be More Efficient with Multiple Rewards. ICML (2023)
Raef Bassily, Corinna Cortes, Anqi Mao, Mehryar Mohri
Differentially Private Domain Adaptation with Theoretical Guarantees. CoRR (2023)
Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong
Two-Stage Learning to Defer with Multiple Experts. NeurIPS (2023)
Anqi Mao, Mehryar Mohri, Yutao Zhong
Cross-Entropy Loss Functions: Theoretical Analysis and Applications. ICML (2023)
Pranjal Awasthi, Corinna Cortes, Mehryar Mohri
Best-Effort Adaptation. CoRR (2023)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. CoRR (2022)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. ICML (2022)
Teodor V. Marinov, Mehryar Mohri, Julian Zimmert
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality. CoRR (2022)
Judy Hoffman, Mehryar Mohri, Ningshan Zhang
Multiple-source adaptation theory and algorithms - addendum. Ann. Math. Artif. Intell. 90 (6) (2022)
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
H-Consistency Bounds for Surrogate Loss Minimizers. ICML (2022)
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
Differentially Private Learning with Margin Guarantees. NeurIPS (2022)
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
H-Consistency Estimation Error of Surrogate Loss Minimizers. CoRR (2022)
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
Differentially Private Learning with Margin Guarantees. CoRR (2022)
Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan
Strategizing against Learners in Bayesian Games. COLT (2022)
Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan
Strategizing against Learners in Bayesian Games. CoRR (2022)
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
Private Domain Adaptation from a Public Source. CoRR (2022)
Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality. NeurIPS (2022)
Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. CoRR (2022)
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
Multi-Class $H$-Consistency Bounds. NeurIPS (2022)
Christoph Dann, Teodor V. Marinov, Mehryar Mohri, Julian Zimmert
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. CoRR (2021)
Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh
Communication-Efficient Agnostic Federated Averaging. Interspeech (2021)
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
A Field Guide to Federated Optimization. CoRR (2021)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. CoRR (2021)
Pranjal Awasthi, Natalie S. Frank, Mehryar Mohri
On the Existence of the Adversarial Bayes Classifier (Extended Version). CoRR (2021)
Ningshan Zhang, Mehryar Mohri, Judy Hoffman
Multiple-source adaptation theory and algorithms. Ann. Math. Artif. Intell. 89 (3-4) (2021)
Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. NeurIPS (2021)
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang
A Discriminative Technique for Multiple-Source Adaptation. ICML (2021)
Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong
Calibration and Consistency of Adversarial Surrogate Losses. CoRR (2021)
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh
Relative Deviation Margin Bounds. ICML (2021)
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
On the Existence of The Adversarial Bayes Classifier. NeurIPS (2021)
Christoph Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. NeurIPS (2021)
Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. NeurIPS (2021)
Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert
Adapting to Misspecification in Contextual Bandits. CoRR (2021)
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
A Finer Calibration Analysis for Adversarial Robustness. CoRR (2021)
Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong
Calibration and Consistency of Adversarial Surrogate Losses. NeurIPS (2021)
Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh, Ke Wu
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data. AISTATS (2021)
Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh
Boosting with Multiple Sources. NeurIPS (2021)
Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri
Corralling Stochastic Bandit Algorithms. AISTATS (2021)
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
Breaking the centralized barrier for cross-device federated learning. NeurIPS (2021)
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Learning with User-Level Privacy. NeurIPS (2021)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14 (1-2) (2021)
Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh
Communication-Efficient Agnostic Federated Averaging. CoRR (2021)
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Learning with User-Level Privacy. CoRR (2021)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Reinforcement Learning with Feedback Graphs. CoRR (2020)
Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus
Agnostic Learning with Multiple Objectives. NeurIPS (2020)
Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh
FedBoost: A Communication-Efficient Algorithm for Federated Learning. ICML (2020)
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. CoRR (2020)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Reinforcement Learning with Feedback Graphs. NeurIPS (2020)
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
Online Learning with Dependent Stochastic Feedback Graphs. ICML (2020)
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
Adaptive Region-Based Active Learning. CoRR (2020)
Raman Arora, Teodor V. Marinov, Mehryar Mohri
Corralling Stochastic Bandit Algorithms. CoRR (2020)
Vitaly Kuznetsov, Mehryar Mohri
Discrepancy-Based Theory and Algorithms for Forecasting Non-Stationary Time Series. Ann. Math. Artif. Intell. 88 (4) (2020)
Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert
Adapting to Misspecification in Contextual Bandits. NeurIPS (2020)
Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri
PAC-Bayes Learning Bounds for Sample-Dependent Priors. NeurIPS (2020)
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh
Relative Deviation Margin Bounds. CoRR (2020)
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. ICML (2020)
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. ICML (2020)
Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh
Three Approaches for Personalization with Applications to Federated Learning. CoRR (2020)
Pranjal Awasthi, Corinna Cortes, Yishay Mansour, Mehryar Mohri
Beyond Individual and Group Fairness. CoRR (2020)
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
Adaptive Region-Based Active Learning. ICML (2020)
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
On the Rademacher Complexity of Linear Hypothesis Sets. CoRR (2020)
Yishay Mansour, Mehryar Mohri, Ananda Theertha Suresh, Ke Wu
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data. CoRR (2020)
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. CoRR (2020)
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang
Multiple-Source Adaptation with Domain Classifiers. CoRR (2020)
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred K. Warmuth
Online Non-Additive Path Learning under Full and Partial Information. ALT (2019)
Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang
Learning GANs and Ensembles Using Discrepancy. CoRR (2019)
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh
Agnostic Federated Learning. CoRR (2019)
Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles. CoRR (2019)
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Hypothesis Set Stability and Generalization. NeurIPS (2019)
Raman Arora, Teodor V. Marinov, Mehryar Mohri
Bandits with Feedback Graphs and Switching Costs. CoRR (2019)
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh
Agnostic Federated Learning. ICML (2019)
Corinna Cortes, Giulia DeSalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile
Active Learning with Disagreement Graphs. ICML (2019)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Mehryar Mohri