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ORCIDMehryar Mohri
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- affiliation: New York University, Courant Institute of Mathematical Sciences, USA
- affiliation: Google Research
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2020 – today
- 2024
[j44]Corinna Cortes, Giulia DeSalvo
, Mehryar Mohri:
Theory and algorithms for learning with rejection in binary classification. Ann. Math. Artif. Intell. 92(2): 277-315 (2024)- [j43]Pranjal Awasthi, Corinna Cortes, Mehryar Mohri:
Best-effort adaptation. Ann. Math. Artif. Intell. 92(2): 393-438 (2024) - [c182]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention. AISTATS 2024: 4753-4761 - [c181]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms. ALT 2024: 822-867 - [c180]Raef Bassily, Corinna Cortes, Anqi Mao, Mehryar Mohri:
Differentially Private Domain Adaptation with Theoretical Guarantees. ICML 2024 - [c179]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Regression with Multi-Expert Deferral. ICML 2024 - [c178]Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Guarantees for Regression. ICML 2024 - [c177]Pranjal Awasthi, Corinna Cortes, Yishay Mansour, Mehryar Mohri:
A Theory of Learning with Competing Objectives and User Feedback. ISAIM 2024: 10-49 - [c176]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Principled Approaches for Learning to Defer with Multiple Experts. ISAIM 2024: 107-135 - [i96]Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Guarantees for Regression. CoRR abs/2403.19480 (2024) - [i95]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Regression with Multi-Expert Deferral. CoRR abs/2403.19494 (2024) - [i94]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Top-k Classification and Cardinality-Aware Prediction. CoRR abs/2403.19625 (2024) - [i93]Anqi Mao, Mehryar Mohri, Yutao Zhong:
A Universal Growth Rate for Learning with Smooth Surrogate Losses. CoRR abs/2405.05968 (2024) - [i92]Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan:
Rate-Preserving Reductions for Blackwell Approachability. CoRR abs/2406.07585 (2024) - [i91]Corinna Cortes, Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong:
Cardinality-Aware Set Prediction and Top-k Classification. CoRR abs/2407.07140 (2024) - [i90]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Enhanced H-Consistency Bounds. CoRR abs/2407.13722 (2024) - [i89]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer. CoRR abs/2407.13732 (2024) - [i88]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Multi-Label Learning with Stronger Consistency Guarantees. CoRR abs/2407.13746 (2024) - 2023
- [c175]Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh:
Principled Approaches for Private Adaptation from a Public Source. AISTATS 2023: 8405-8432 - [c174]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness. AISTATS 2023: 10077-10094 - [c173]Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan:
Pseudonorm Approachability and Applications to Regret Minimization. ALT 2023: 471-509 - [c172]Christoph Dann, Yishay Mansour, Mehryar Mohri:
Reinforcement Learning Can Be More Efficient with Multiple Rewards. ICML 2023: 6948-6967 - [c171]Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Bounds for Pairwise Misranking Loss Surrogates. ICML 2023: 23743-23802 - [c170]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Cross-Entropy Loss Functions: Theoretical Analysis and Applications. ICML 2023: 23803-23828 - [c169]Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Bounds: Characterization and Extensions. NeurIPS 2023 - [c168]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Structured Prediction with Stronger Consistency Guarantees. NeurIPS 2023 - [c167]Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong:
Two-Stage Learning to Defer with Multiple Experts. NeurIPS 2023 - [i87]Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan:
Pseudonorm Approachability and Applications to Regret Minimization. CoRR abs/2302.01517 (2023) - [i86]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Cross-Entropy Loss Functions: Theoretical Analysis and Applications. CoRR abs/2304.07288 (2023) - [i85]Pranjal Awasthi, Corinna Cortes, Mehryar Mohri:
Best-Effort Adaptation. CoRR abs/2305.05816 (2023) - [i84]Raef Bassily, Corinna Cortes, Anqi Mao, Mehryar Mohri:
Differentially Private Domain Adaptation with Theoretical Guarantees. CoRR abs/2306.08838 (2023) - [i83]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Ranking with Abstention. CoRR abs/2307.02035 (2023) - [i82]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention. CoRR abs/2310.14770 (2023) - [i81]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms. CoRR abs/2310.14772 (2023) - [i80]Anqi Mao, Mehryar Mohri, Yutao Zhong:
Principled Approaches for Learning to Defer with Multiple Experts. CoRR abs/2310.14774 (2023) - 2022
- [j42]Judy Hoffman, Mehryar Mohri, Ningshan Zhang:
Multiple-source adaptation theory and algorithms - addendum. Ann. Math. Artif. Intell. 90(6): 569-572 (2022) - [c166]Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan:
Strategizing against Learners in Bayesian Games. COLT 2022: 5221-5252 - [c165]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Bounds for Surrogate Loss Minimizers. ICML 2022: 1117-1174 - [c164]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. ICML 2022: 4666-4689 - [c163]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Multi-Class $H$-Consistency Bounds. NeurIPS 2022 - [c162]Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh:
Differentially Private Learning with Margin Guarantees. NeurIPS 2022 - [c161]Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert:
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality. NeurIPS 2022 - [i79]Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh:
Differentially Private Learning with Margin Guarantees. CoRR abs/2204.10376 (2022) - [i78]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Estimation Error of Surrogate Loss Minimizers. CoRR abs/2205.08017 (2022) - [i77]Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan:
Strategizing against Learners in Bayesian Games. CoRR abs/2205.08562 (2022) - [i76]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. CoRR abs/2206.09421 (2022) - [i75]Teodor V. Marinov, Mehryar Mohri, Julian Zimmert:
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality. CoRR abs/2206.10022 (2022) - [i74]Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh:
Private Domain Adaptation from a Public Source. CoRR abs/2208.06135 (2022) - [i73]Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert:
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. CoRR abs/2208.10904 (2022) - 2021
- [j41]Ningshan Zhang, Mehryar Mohri, Judy Hoffman:
Multiple-source adaptation theory and algorithms. Ann. Math. Artif. Intell. 89(3-4): 237-270 (2021) - [j40]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): 1-210 (2021) - [c160]Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri:
Corralling Stochastic Bandit Algorithms. AISTATS 2021: 2116-2124 - [c159]Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh, Ke Wu:
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data. AISTATS 2021: 2332-2340 - [c158]Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh:
Relative Deviation Margin Bounds. ICML 2021: 2122-2131 - [c157]Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang:
A Discriminative Technique for Multiple-Source Adaptation. ICML 2021: 2132-2143 - [c156]Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh:
Communication-Efficient Agnostic Federated Averaging. Interspeech 2021: 871-875 - [c155]Christoph Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert:
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. NeurIPS 2021: 1-12 - [c154]Pranjal Awasthi, Natalie Frank, Mehryar Mohri:
On the Existence of The Adversarial Bayes Classifier. NeurIPS 2021: 2978-2990 - [c153]Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Calibration and Consistency of Adversarial Surrogate Losses. NeurIPS 2021: 9804-9815 - [c152]Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert:
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. NeurIPS 2021: 12040-12051 - [c151]Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh:
Learning with User-Level Privacy. NeurIPS 2021: 12466-12479 - [c150]Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh:
Boosting with Multiple Sources. NeurIPS 2021: 17373-17387 - [c149]Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan:
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. NeurIPS 2021: 19033-19045 - [c148]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: 28663-28676 - [i72]Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh:
Learning with User-Level Privacy. CoRR abs/2102.11845 (2021) - [i71]Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh:
Communication-Efficient Agnostic Federated Averaging. CoRR abs/2104.02748 (2021) - [i70]Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Calibration and Consistency of Adversarial Surrogate Losses. CoRR abs/2104.09658 (2021) - [i69]Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
A Finer Calibration Analysis for Adversarial Robustness. CoRR abs/2105.01550 (2021) - [i68]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. CoRR abs/2106.11519 (2021) - [i67]Christoph Dann, Teodor V. Marinov, Mehryar Mohri, Julian Zimmert:
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. CoRR abs/2107.01264 (2021) - [i66]Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert:
Adapting to Misspecification in Contextual Bandits. CoRR abs/2107.05745 (2021) - [i65]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 Horváth, 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 abs/2107.06917 (2021) - [i64]Pranjal Awasthi, Natalie S. Frank, Mehryar Mohri:
On the Existence of the Adversarial Bayes Classifier (Extended Version). CoRR abs/2112.01694 (2021) - 2020
- [j39]Vitaly Kuznetsov, Mehryar Mohri:
Discrepancy-Based Theory and Algorithms for Forecasting Non-Stationary Time Series. Ann. Math. Artif. Intell. 88(4): 367-399 (2020) - [c147]Pranjal Awasthi, Natalie Frank, Mehryar Mohri:
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. ICML 2020: 431-441 - [c146]Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang:
Adaptive Region-Based Active Learning. ICML 2020: 2144-2153 - [c145]Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang:
Online Learning with Dependent Stochastic Feedback Graphs. ICML 2020: 2154-2163 - [c144]Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh:
FedBoost: A Communication-Efficient Algorithm for Federated Learning. ICML 2020: 3973-3983 - [c143]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: 5132-5143 - [c142]Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri:
PAC-Bayes Learning Bounds for Sample-Dependent Priors. NeurIPS 2020 - [c141]Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus:
Agnostic Learning with Multiple Objectives. NeurIPS 2020 - [c140]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Reinforcement Learning with Feedback Graphs. NeurIPS 2020 - [c139]Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert:
Adapting to Misspecification in Contextual Bandits. NeurIPS 2020 - [i63]Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang:
Adaptive Region-Based Active Learning. CoRR abs/2002.07348 (2020) - [i62]Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh:
Three Approaches for Personalization with Applications to Federated Learning. CoRR abs/2002.10619 (2020) - [i61]Pranjal Awasthi, Natalie Frank, Mehryar Mohri:
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. CoRR abs/2004.13617 (2020) - [i60]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Reinforcement Learning with Feedback Graphs. CoRR abs/2005.03789 (2020) - [i59]Raman Arora, Teodor V. Marinov, Mehryar Mohri:
Corralling Stochastic Bandit Algorithms. CoRR abs/2006.09255 (2020) - [i58]Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh:
Relative Deviation Margin Bounds. CoRR abs/2006.14950 (2020) - [i57]Yishay Mansour, Mehryar Mohri, Ananda Theertha Suresh, Ke Wu:
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data. CoRR abs/2007.09762 (2020) - [i56]Pranjal Awasthi, Natalie Frank, Mehryar Mohri:
On the Rademacher Complexity of Linear Hypothesis Sets. CoRR abs/2007.11045 (2020) - [i55]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 abs/2008.03606 (2020) - [i54]Pranjal Awasthi, Corinna Cortes, Yishay Mansour, Mehryar Mohri:
Beyond Individual and Group Fairness. CoRR abs/2008.09490 (2020) - [i53]Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang:
Multiple-Source Adaptation with Domain Classifiers. CoRR abs/2008.11036 (2020)
2010 – 2019
- 2019
- [j38]Corinna Cortes, Spencer Greenberg, Mehryar Mohri:
Relative deviation learning bounds and generalization with unbounded loss functions. Ann. Math. Artif. Intell. 85(1): 45-70 (2019) - [j37]Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina:
Adaptation Based on Generalized Discrepancy. J. Mach. Learn. Res. 20: 1:1-1:30 (2019) - [c138]Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang:
Region-Based Active Learning. AISTATS 2019: 2801-2809 - [c137]Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred K. Warmuth:
Online Non-Additive Path Learning under Full and Partial Information. ALT 2019: 274-299 - [c136]Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang:
Online Learning with Sleeping Experts and Feedback Graphs. ICML 2019: 1370-1378 - [c135]Corinna Cortes, Giulia DeSalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile:
Active Learning with Disagreement Graphs. ICML 2019: 1379-1387 - [c134]Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh:
Agnostic Federated Learning. ICML 2019: 4615-4625 - [c133]Corinna Cortes, Mehryar Mohri, Dmitry Storcheus:
Regularized Gradient Boosting. NeurIPS 2019: 5450-5459 - [c132]Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang:
Learning GANs and Ensembles Using Discrepancy. NeurIPS 2019: 5788-5799 - [c131]Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Hypothesis Set Stability and Generalization. NeurIPS 2019: 6726-6736 - [c130]Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri:
Bandits with Feedback Graphs and Switching Costs. NeurIPS 2019: 10397-10407 - [i52]Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh:
Agnostic Federated Learning. CoRR abs/1902.00146 (2019) - [i51]Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Hypothesis Set Stability and Generalization. CoRR abs/1904.04755 (2019) - [i50]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 abs/1905.00080 (2019) - [i49]Raman Arora, Teodor V. Marinov, Mehryar Mohri:
Bandits with Feedback Graphs and Switching Costs. CoRR abs/1907.12189 (2019) - [i48]Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. CoRR abs/1910.06378 (2019) - [i47]Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang:
Learning GANs and Ensembles Using Discrepancy. CoRR abs/1910.08965 (2019) - [i46]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 abs/1912.04977 (2019) - 2018
- [j36]Borja Balle, Mehryar Mohri:
Generalization bounds for learning weighted automata. Theor. Comput. Sci. 716: 89-106 (2018) - [c129]Mehryar Mohri, Scott Yang:
Competing with Automata-based Expert Sequences. AISTATS 2018: 1732-1740 - [c128]Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Logistic Regression: The Importance of Being Improper. COLT 2018: 167-208 - [c127]Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang:
Online Learning with Abstention. ICML 2018: 1067-1075 - [c126]Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov, Mehryar Mohri:
Policy Regret in Repeated Games. NeurIPS 2018: 6733-6742 - [c125]