Research output

Research output per year 1993 2020

• 71 Conference contribution
• 41 Article
• 34 Conference article
• 15 Paper
• 6 More
  • 4 Chapter
  • 1 Book
  • 1 Editorial

Research output per year

Research output per year

Search results

• 2020

  Discrepancy-Based Theory and Algorithms for Forecasting Non-Stationary Time Series

  Kuznetsov, V. & Mohri, M., Apr 1 2020, In: Annals of Mathematics and Artificial Intelligence. 88, 4, p. 367-399 33 p.

  Research output: Contribution to journal › Article › peer-review

  • Non-stationary Time Series
  • Forecasting
  • Random processes
  • Discrepancy
  • Learning

• Multiple-source adaptation theory and algorithms

  Zhang, N., Mohri, M. & Hoffman, J., 2020, (Accepted/In press) In: Annals of Mathematics and Artificial Intelligence.

  Research output: Contribution to journal › Article › peer-review

  • Entropy Loss
  • Cross-entropy
  • Weighted Distributions
  • Learning
  • Mixture Distribution

• Region-based active learning

  Cortes, C., DeSalvo, G., Gentile, C., Mohri, M. & Zhang, N., 2020.

  Research output: Contribution to conference › Paper › peer-review

  • Active Learning
  • Problem-Based Learning
  • Learning algorithms
  • Learning Algorithm
  • Subroutines
• 2019

  Active learning with disagreement graphs

  Cortes, C., DeSalvo, G., Gentile, C., Mohri, M. & Zhang, N., Jan 1 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 2468-2483 16 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Problem-Based Learning
  • Labeling
  • Labels
  • Learning algorithms
  • learning

• Adaptation based on generalized discrepancy

  Cortes, C., Mohri, M. & Medina, A. M., Jan 1 2019, In: Journal of Machine Learning Research. 20, p. 1-30 30 p.

  Research output: Contribution to journal › Article › peer-review

  • Discrepancy
  • Training Samples
  • Convex optimization
  • Convex Optimization
  • Theoretical Analysis

• Agnostic federated learning

  Mohri, M., Sivek, G. & Suresh, A. T., Jan 1 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 8114-8124 11 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • learning
  • Cloud computing
  • Set theory
  • scenario
  • fairness

• Bandits with feedback graphs and switching costs

  Arora, R., Marinov, T. V. & Mohri, M., 2019, In: Advances in Neural Information Processing Systems. 32

  Research output: Contribution to journal › Conference article › peer-review

  • Feedback
  • Costs

• Hypothesis set stability and generalization

  Foster, D. J., Greenberg, S., Kale, S., Luo, H., Mohri, M. & Sridharan, K., 2019, In: Advances in Neural Information Processing Systems. 32

  Research output: Contribution to journal › Conference article › peer-review

  • Set theory

• Learning GANs and ensembles using discrepancy

  Adlam, B., Cortes, C., Mohri, M. & Zhang, N., 2019, In: Advances in Neural Information Processing Systems. 32

  Research output: Contribution to journal › Conference article › peer-review

  • Set theory
  • Experiments

• Regularized gradient boosting

  Cortes, C., Mohri, M. & Storcheus, D., 2019, In: Advances in Neural Information Processing Systems. 32

  Research output: Contribution to journal › Conference article › peer-review

  • Binary trees

• Relative deviation learning bounds and generalization with unbounded loss functions

  Cortes, C., Greenberg, S. & Mohri, M., Jan 1 2019, In: Annals of Mathematics and Artificial Intelligence. 85, 1, p. 45-70 26 p.

  Research output: Contribution to journal › Article › peer-review

  • Loss Function
  • Learning
  • Deviation
  • Generalization
  • Weighting
• 2018

  Algorithms and theory for multiple-source adaptation

  Hoffman, J., Mohri, M. & Zhang, N., 2018, In: Advances in Neural Information Processing Systems. 2018-December, p. 8246-8256 11 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Entropy
  • Experiments

• Competing with automata-based expert sequences

  Mohri, M. & Yang, S., 2018, p. 1732-1740. 9 p.

  Research output: Contribution to conference › Paper › peer-review

  • Automata
  • Framework
  • Efficient Algorithms
  • N-gram
  • Online Learning

• Efficient gradient computation for structured output learning with rational and tropical losses

  Cortes, C., Kuznetsov, V., Mohri, M., Storcheus, D. & Yang, S., 2018, In: Advances in Neural Information Processing Systems. 2018-December, p. 6810-6821 12 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Rational functions
  • Learning algorithms
  • Entropy
  • Neural networks

• Generalization bounds for learning weighted automata

  Balle, B. & Mohri, M., Mar 15 2018, In: Theoretical Computer Science. 716, p. 89-106 18 p.

  Research output: Contribution to journal › Article › peer-review

  • Learning Automata
  • Weighted Automata
  • Labels
  • Dependent Data
  • Generalization

• Online Learning with Abstention

  Cortes, C., Desalvo, G., Gentile, C., Mohri, M. & Yang, S., 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), p. 1726-1734 9 p. (35th International Conference on Machine Learning, ICML 2018; vol. 3).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Feedback
  • Costs
  • Experiments

• Policy regret in repeated games

  Arora, R., Dinitz, M., Marinov, T. V. & Mohri, M., 2018, In: Advances in Neural Information Processing Systems. 2018-December, p. 6732-6741 10 p.

  Research output: Contribution to journal › Conference article › peer-review
• 2017

  AdaNet: Adaptive structural learning of artificial neural networks

  Cortes, C., Gonzalvo, X., Kuznetsov, V., Mohri, M. & Yang, S., 2017, 34th International Conference on Machine Learning, ICML 2017. International Machine Learning Society (IMLS), p. 1452-1466 15 p. (34th International Conference on Machine Learning, ICML 2017; vol. 2).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Neural networks
  • Experiments

• A disambiguation algorithm for weighted automata

  Mohri, M. & Riley, M. D., May 30 2017, In: Theoretical Computer Science. 679, p. 53-68 16 p.

  Research output: Contribution to journal › Article › peer-review

  • Weighted Automata
  • Semiring
  • Machine Translation
  • Speech Recognition
  • Proof of correctness

• Discriminative state-space models

  Kuznetsov, V. & Mohri, M., 2017, In: Advances in Neural Information Processing Systems. 2017-December, p. 5672-5680 9 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Time series

• Generalization bounds for non-stationary mixing processes

  Kuznetsov, V. & Mohri, M., Jan 1 2017, In: Machine Learning. 106, 1, p. 93-117 25 p.

  Research output: Contribution to journal › Article › peer-review

  • Random processes
  • Random variables
  • Time series

• Online learning with transductive regret

  Mohri, M. & Yang, S., 2017, In: Advances in Neural Information Processing Systems. 2017-December, p. 5215-5225 11 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Byproducts
  • Transducers
  • Learning algorithms

• Parameter-free online learning via model selection

  Foster, D. J., Kale, S., Mohri, M. & Sridharan, K., 2017, In: Advances in Neural Information Processing Systems. 2017-December, p. 6021-6031 11 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Hilbert spaces
  • Banach spaces
  • Supervised learning
• 2016

  Accelerating online convex optimization via adaptive prediction

  Mohri, M. & Yang, S., 2016, p. 848-856. 9 p.

  Research output: Contribution to conference › Paper › peer-review

  • Online Optimization
  • Randomisation
  • Convex optimization
  • Convex Optimization
  • Prediction

• Adaptive algorithms and data-dependent guarantees for bandit convex optimization

  Mohri, M. & Yang, S., 2016, 32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016. Janzing, D. & Ihler, A. (eds.). Association For Uncertainty in Artificial Intelligence (AUAI), p. 815-824 10 p. (32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Convex optimization
  • Adaptive algorithms

• Boosting with abstention

  Cortes, C., De Salvo, G. & Mohri, M., 2016, In: Advances in Neural Information Processing Systems. p. 1668-1676 9 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Labels
  • Learning algorithms
  • Costs
  • Experiments

• Learning algorithms for second-price auctions with reserve

  Mohri, M. & Muñoz Medina, A., Apr 1 2016, In: Journal of Machine Learning Research. 17

  Research output: Contribution to journal › Article › peer-review

  • Auctions
  • Learning Algorithm
  • Learning algorithms
  • Search Engine
  • Optimise

• Learning N-gram language models from uncertain data

  Kuznetsov, V., Liao, H., Mohri, M., Riley, M. & Roark, B., 2016, In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. 08-12-September-2016, p. 2323-2327 5 p.

  Research output: Contribution to journal › Conference article › peer-review

  • N-gram
  • Language Model
  • Uncertain Data
  • Learning
  • Baseline

• Learning with rejection

  Cortes, C., DeSalvo, G. & Mohri, M., 2016, Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings. Simon, H. U., Zilles, S. & Ortner, R. (eds.). Springer Verlag, p. 67-82 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9925 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Rejection
  • Learning
  • Classifier
  • Classifiers
  • Confidence

• Optimistic bandit convex optimization

  Mohri, M. & Yang, S., 2016, In: Advances in Neural Information Processing Systems. p. 2297-2305 9 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Convex optimization
  • Polynomials

• Random composite forests

  DeSalvo, G. & Mohri, M., 2016, 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, p. 1540-1546 7 p. (30th AAAI Conference on Artificial Intelligence, AAAI 2016).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Composite materials
  • Decision trees
  • Classifiers
  • Experiments

• Structural online learning

  Mohri, M. & Yang, S., 2016, Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings. Simon, H. U., Zilles, S. & Ortner, R. (eds.). Springer Verlag, p. 223-237 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9925 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Online Learning
  • Batch
  • Ensemble
  • Family
  • Error analysis

• Time series prediction and online learning

  Kuznetsov, V. & Mohri, M., Jun 6 2016, In: Journal of Machine Learning Research. 49, June, p. 1190-1213 24 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Online Learning
  • Time Series Prediction
  • Time series
  • Learning
  • Ensemble Methods
• 2015

  Adaptation algorithm and theory based on generalized discrepancy

  Cortes, C., Mohri, M. & Medina, A. M., Aug 10 2015, KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, p. 169-178 10 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; vol. 2015-August).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Convex optimization
  • Experiments

• Automata and graph compression

  Mohri, M., Riley, M. & Suresh, A. T., Sep 28 2015, Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015. Institute of Electrical and Electronics Engineers Inc., p. 2989-2993 5 p. 7283005. (IEEE International Symposium on Information Theory - Proceedings; vol. 2015-June).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • UNIX
  • Speech processing
  • Compression
  • Statistical Models
  • Automata

• Learning theory and algorithms for forecasting non-stationary time series

  Kuznetsov, V. & Mohri, M., 2015, In: Advances in Neural Information Processing Systems. 2015-January, p. 541-549 9 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Random processes
  • Time series

• Learning weighted automata

  Balle, B. & Mohri, M., 2015, Algebraic Informatics 6th International Conference, CAI 2015, Proceedings. Maletti, A. (ed.). Springer Verlag, p. 1-21 21 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9270).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Learning with deep cascades

  DeSalvo, G., Mohri, M. & Syed, U., 2015, Algorithmic Learning Theory - 26th International Conference, ALT 2015. Gentile, C., Zilles, S. & Chaudhuri, K. (eds.). Springer Verlag, p. 254-269 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9355).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Cascade
  • Deep learning
  • Predictors
  • Learning
  • Leaves

• Non-parametric revenue optimization for generalized second price auctions

  Mohri, M. & Medina, A. M., 2015, p. 612-621. 10 p.

  Research output: Contribution to conference › Paper › peer-review

  • Learning algorithms

• On-line learning algorithms for path experts with non-additive losses

  Cortes, C., Kuznetsov, V., Mohri, M. & Warmuth, M. K., 2015, In: Journal of Machine Learning Research. 40, 2015

  Research output: Contribution to journal › Conference article › peer-review

  • Learning Algorithm
  • Learning algorithms
  • Path
  • Loss Function
  • Family

• On the disambiguation of weighted automata

  Mohri, M. & Riley, M. D., 2015, Implementation and Application of Automata - 20th International Conference, CIAA 2015, Proceedings. Drewes, F. (ed.). Springer Verlag, p. 263-278 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9223).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Weighted Automata
  • Machine Translation
  • Speech Recognition
  • Proof of correctness
  • Semiring

• On the rademacher complexity of weighted automata

  Balle, B. & Mohri, M., 2015, Algorithmic Learning Theory - 26th International Conference, ALT 2015. Gentile, C., Zilles, S. & Chaudhuri, K. (eds.). Springer Verlag, p. 179-193 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9355).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Weighted Automata
  • Finite automata
  • Hidden Markov models
  • Machine learning
  • Spectrality

• Revenue optimization against strategic buyers

  Mohri, M. & Medina, A. M., 2015, In: Advances in Neural Information Processing Systems. 2015-January, p. 2530-2538 9 p.

  Research output: Contribution to journal › Conference article › peer-review

• Structural maxent models

  Cortes, C., Kuznetsov, V., Mohri, M. & Syed, U., 2015, 32nd International Conference on Machine Learning, ICML 2015. Bach, F. & Blei, D. (eds.). International Machine Learning Society (IMLS), p. 391-399 9 p. (32nd International Conference on Machine Learning, ICML 2015; vol. 1).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Experiments
• 2014

  Conditional swap regret and conditional correlated equilibrium

  Mohri, M. & Yang, S., 2014, In: Advances in Neural Information Processing Systems. 2, January, p. 1314-1322 9 p.

  Research output: Contribution to journal › Conference article › peer-review

• Corporate learning at scale: Lessons from a large online course at Google

  Asuncion, A., De Haan, J., Mohri, M., Patel, K., Rostamizadeh, A., Syed, U. & Wong, L., 2014, p. 187-188. 2 p.

  Research output: Contribution to conference › Paper › peer-review

  • Students
  • Machine learning
  • Engineering education
  • Personnel
  • Industry

• Deep boosting

  Cortes, C., Mohri, M. & Syed, U., 2014, 31st International Conference on Machine Learning, ICML 2014. International Machine Learning Society (IMLS), p. 2917-2930 14 p. (31st International Conference on Machine Learning, ICML 2014; vol. 4).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Classifiers
  • Adaptive boosting
  • Decision trees
  • Logistics
  • Learning algorithms

• Domain adaptation and sample bias correction theory and algorithm for regression

  Cortes, C. & Mohri, M., Jan 30 2014, In: Theoretical Computer Science. 519, p. 103-126 24 p.

  Research output: Contribution to journal › Article › peer-review

  • Bias Correction
  • Discrepancy
  • Regression
  • Feature Space
  • kernel

• Ensemble methods for structured prediction

  Cortes, C., Kuznetsov, V. & Mohri, M., 2014, 31st International Conference on Machine Learning, ICML 2014. International Machine Learning Society (IMLS), p. 2856-2872 17 p. (31st International Conference on Machine Learning, ICML 2014; vol. 4).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Learning algorithms
  • Labels
  • Experiments

• Generalization bounds for time series prediction with non-stationary processes

  Kuznetsov, V. & Mohri, M., 2014, Algorithmic Learning Theory - 25th International Conference, ALT 2014, Proceedings. Auer, P., Clark, A., Zeugmann, T. & Zilles, S. (eds.). Springer Verlag, p. 260-274 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 8776).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Time Series Prediction
  • Nonstationary Processes
  • Mixing Processes
  • Time series
  • Random processes