Research output per year
Research output per year
- 1 - 50 out of 239 results
Search results
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2025
Enhanced H-Consistency Bounds
Mao, A., Mohri, M. & Zhong, Y., 2025, In: Proceedings of Machine Learning Research. 272, p. 772-813 42 p.Research output: Contribution to journal › Conference article › peer-review
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2024
A Theory of Learning with Competing Objectives and User Feedback
Awasthi, P., Cortes, C., Mansour, Y. & Mohri, M., 2024, Artificial Intelligence and Image Analysis - 18th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2024, and 22nd International Workshop on Combinatorial Image Analysis, IWCIA 2024, Revised Selected Papers. Barneva, R. P., Brimkov, V. E., Brimkov, V. E., Gentile, C. & Pacchiano, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 10-49 40 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14494 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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A Universal Growth Rate for Learning with Smooth Surrogate Losses
Mao, A., Mohri, M. & Zhong, Y., 2024, In: Advances in Neural Information Processing Systems. 37Research output: Contribution to journal › Conference article › peer-review
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Best-effort adaptation
Awasthi, P., Cortes, C. & Mohri, M., Apr 2024, In: Annals of Mathematics and Artificial Intelligence. 92, 2, p. 393-438 46 p.Research output: Contribution to journal › Article › peer-review
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Cardinality-Aware Set Prediction and Top-k Classification
Cortes, C., Mao, A., Mohri, C., Mohri, M. & Zhong, Y., 2024, In: Advances in Neural Information Processing Systems. 37Research output: Contribution to journal › Conference article › peer-review
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DC-programming for neural network optimizations
Awasthi, P., Mao, A., Mohri, M. & Zhong, Y., 2024, (Accepted/In press) In: Journal of Global Optimization.Research output: Contribution to journal › Article › peer-review
Open Access -
Differentially Private Domain Adaptation with Theoretical Guarantees
Bassily, R., Cortes, C., Mao, A. & Mohri, M., 2024, In: Proceedings of Machine Learning Research. 235, p. 3168-3196 29 p.Research output: Contribution to journal › Conference article › peer-review
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H-Consistency Guarantees for Regression
Mao, A., Mohri, M. & Zhong, Y., 2024, In: Proceedings of Machine Learning Research. 235, p. 34712-34737 26 p.Research output: Contribution to journal › Conference article › peer-review
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Multi-Label Learning with Stronger Consistency Guarantees
Mao, A., Mohri, M. & Zhong, Y., 2024, In: Advances in Neural Information Processing Systems. 37Research output: Contribution to journal › Conference article › peer-review
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Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms
Mao, A., Mohri, M. & Zhong, Y., 2024, In: Proceedings of Machine Learning Research. 237, p. 822-867 46 p.Research output: Contribution to journal › Conference article › peer-review
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Principled Approaches for Learning to Defer with Multiple Experts
Mao, A., Mohri, M. & Zhong, Y., 2024, Artificial Intelligence and Image Analysis - 18th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2024, and 22nd International Workshop on Combinatorial Image Analysis, IWCIA 2024, Revised Selected Papers. Barneva, R. P., Brimkov, V. E., Brimkov, V. E., Gentile, C. & Pacchiano, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 107-135 29 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14494 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer
Mao, A., Mohri, M. & Zhong, Y., 2024, In: Advances in Neural Information Processing Systems. 37Research output: Contribution to journal › Conference article › peer-review
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Regression with Multi-Expert Deferral
Mao, A., Mohri, M. & Zhong, Y., 2024, In: Proceedings of Machine Learning Research. 235, p. 34738-34759 22 p.Research output: Contribution to journal › Conference article › peer-review
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Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention
Mao, A., Mohri, M. & Zhong, Y., 2024, In: Proceedings of Machine Learning Research. 238, p. 4753-4761 9 p.Research output: Contribution to journal › Conference article › peer-review
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Theory and algorithms for learning with rejection in binary classification
Cortes, C., DeSalvo, G. & Mohri, M., Apr 2024, In: Annals of Mathematics and Artificial Intelligence. 92, 2, p. 277-315 39 p.Research output: Contribution to journal › Article › peer-review
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2023
Cross-Entropy Loss Functions: Theoretical Analysis and Applications
Mao, A., Mohri, M. & Zhong, Y., 2023, In: Proceedings of Machine Learning Research. 202, p. 23803-23828 26 p.Research output: Contribution to journal › Conference article › peer-review
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H-Consistency Bounds: Characterization and Extensions
Mao, A., Mohri, M. & Zhong, Y., 2023, In: Advances in Neural Information Processing Systems. 36Research output: Contribution to journal › Conference article › peer-review
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H-Consistency Bounds for Pairwise Misranking Loss Surrogates
Mao, A., Mohri, M. & Zhong, Y., 2023, In: Proceedings of Machine Learning Research. 202, p. 23743-23802 60 p.Research output: Contribution to journal › Conference article › peer-review
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Principled Approaches for Private Adaptation from a Public Source
Bassily, R., Mohri, M. & Suresh, A. T., 2023, In: Proceedings of Machine Learning Research. 206, p. 8405-8432 28 p.Research output: Contribution to journal › Conference article › peer-review
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Pseudonorm Approachability and Applications to Regret Minimization
Dann, C., Mansour, Y., Mohri, M., Schneider, J. & Sivan, B., 2023, In: Proceedings of Machine Learning Research. 201, p. 471-509 39 p.Research output: Contribution to journal › Conference article › peer-review
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Reinforcement Learning Can Be More Efficient with Multiple Rewards
Dann, C., Mansour, Y. & Mohri, M., 2023, In: Proceedings of Machine Learning Research. 202, p. 6948-6967 20 p.Research output: Contribution to journal › Conference article › peer-review
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Structured Prediction with Stronger Consistency Guarantees
Mao, A., Mohri, M. & Zhong, Y., 2023, In: Advances in Neural Information Processing Systems. 36Research output: Contribution to journal › Conference article › peer-review
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Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness
Awasthi, P., Mao, A., Mohri, M. & Zhong, Y., 2023, In: Proceedings of Machine Learning Research. 206, p. 10077-10094 18 p.Research output: Contribution to journal › Conference article › peer-review
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Two-Stage Learning to Defer with Multiple Experts
Mao, A., Mohri, C., Mohri, M. & Zhong, Y., 2023, In: Advances in Neural Information Processing Systems. 36Research output: Contribution to journal › Conference article › peer-review
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2022
Differentially Private Learning with Margin Guarantees
Bassily, R., Mohri, M. & Suresh, A. T., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Dann, C., Mansour, Y., Mohri, M., Sekhari, A. & Sridharan, K., 2022, In: Proceedings of Machine Learning Research. 162, p. 4666-4689 24 p.Research output: Contribution to journal › Conference article › peer-review
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H-Consistency Bounds for Surrogate Loss Minimizers
Awasthi, P., Mao, A., Mohri, M. & Zhong, Y., 2022, In: Proceedings of Machine Learning Research. 162, p. 1117-1174 58 p.Research output: Contribution to journal › Conference article › peer-review
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Multi-Class H-Consistency Bounds
Awasthi, P., Mohri, M., Mao, A. & Zhong, Y., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Multiple-source adaptation theory and algorithms – addendum
Hoffman, J., Mohri, M. & Zhang, N., Jun 2022, In: Annals of Mathematics and Artificial Intelligence. 90, 6, p. 569-572 4 p.Research output: Contribution to journal › Article › peer-review
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Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees
Bassily, R., Mohri, M. & Suresh, A. T., 2022, In: Proceedings of Machine Learning Research. 178, p. 5638-5643 6 p.Research output: Contribution to journal › Conference article › peer-review
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Open Problem: Finite-Time Instance Dependent Optimality for Stochastic Online Learning with Feedback Graphs
Marinov, T. V., Mohri, M. & Zimmert, J., 2022, In: Proceedings of Machine Learning Research. 178, p. 5644-5649 6 p.Research output: Contribution to journal › Conference article › peer-review
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Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
Marinov, T. V., Mohri, M. & Zimmert, J., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Strategizing against Learners in Bayesian Games
Mansour, Y., Mohri, M., Schneider, J. & Sivan, B., 2022, In: Proceedings of Machine Learning Research. 178, p. 5221-5252 32 p.Research output: Contribution to journal › Conference article › peer-review
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2021
A Discriminative Technique for Multiple-Source Adaptation
Cortes, C., Mohri, M., Suresh, A. T. & Zhang, N., 2021, Proceedings of the 38th International Conference on Machine Learning, ICML 2021. ML Research Press, p. 2132-2143 12 p. (Proceedings of Machine Learning Research; vol. 139).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D'Oliveira, R. G. L., Eichner, H., El Rouayheb, S., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B. & Gruteser, M. & 39 others, , Jun 23 2021, In: Foundations and Trends in Machine Learning. 14, 1-2, p. 1-210 210 p.Research output: Contribution to journal › Review article › peer-review
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Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
Dann, C., Mansour, Y., Mohri, M., Sekhari, A. & Sridharan, K., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 19033-19045 13 p. (Advances in Neural Information Processing Systems; vol. 23).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning
Dann, C., Mohri, M., Zhang, T. & Zimmert, J., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 12040-12051 12 p. (Advances in Neural Information Processing Systems; vol. 15).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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A Theory of Multiple-Source Adaptation with Limited Target Labeled Data
Mansour, Y., Mohri, M., Ro, J., Suresh, A. T. & Wu, K., 2021, In: Proceedings of Machine Learning Research. 130, p. 2332-2340 9 p.Research output: Contribution to journal › Conference article › peer-review
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Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
Dann, C., Marinov, T. V., Mohri, M. & Zimmert, J., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 1-12 12 p. (Advances in Neural Information Processing Systems; vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Boosting with Multiple Sources
Cortes, C., Mohri, M., Storcheus, D. & Suresh, A. T., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 17373-17387 15 p. (Advances in Neural Information Processing Systems; vol. 21).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Breaking the centralized barrier for cross-device federated learning
Karimireddy, S. P., Jaggi, M., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U. & Suresh, A. T., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 28663-28676 14 p. (Advances in Neural Information Processing Systems; vol. 34).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Calibration and Consistency of Adversarial Surrogate Losses
Awasthi, P., Frank, N. S., Mao, A., Mohri, M. & Zhong, Y., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 9804-9815 12 p. (Advances in Neural Information Processing Systems; vol. 12).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Communication-efficient agnostic federated averaging
Ro, J., Chen, M., Mathews, R., Mohri, M. & Suresh, A. T., 2021, 22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021. International Speech Communication Association, p. 1753-1757 5 p. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH; vol. 3).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open Access -
Corralling Stochastic Bandit Algorithms
Arora, R., Marinov, T. V. & Mohri, M., 2021, In: Proceedings of Machine Learning Research. 130, p. 2116-2124 9 p.Research output: Contribution to journal › Conference article › peer-review
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Learning with User-Level Privacy
Levy, D., Sun, Z., Amin, K., Kale, S., Kulesza, A., Mohri, M. & Suresh, A. T., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 12466-12479 14 p. (Advances in Neural Information Processing Systems; vol. 15).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Multiple-source adaptation theory and algorithms
Zhang, N., Mohri, M. & Hoffman, J., Mar 2021, In: Annals of Mathematics and Artificial Intelligence. 89, 3-4, p. 237-270 34 p.Research output: Contribution to journal › Article › peer-review
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On The Existence of The Adversarial Bayes Classifier
Awasthi, P., Frank, N. S. & Mohri, M., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 2978-2990 13 p. (Advances in Neural Information Processing Systems; vol. 4).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Relative Deviation Margin Bounds
Cortes, C., Mohri, M. & Suresh, A. T., 2021, Proceedings of the 38th International Conference on Machine Learning, ICML 2021. ML Research Press, p. 2122-2131 10 p. (Proceedings of Machine Learning Research; vol. 139).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2020
Adapting to misspecification in contextual bandits
Foster, D. J., Gentile, C., Mohri, M. & Zimmert, J., 2020, In: Advances in Neural Information Processing Systems. 2020-DecemberResearch output: Contribution to journal › Conference article › peer-review
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Adaptive region-based active learning
Cortes, C., DeSalvo, G., Gentile, C., Mohri, M. & Zhang, N., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 2122-2131 10 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-3).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution