Robert E. Schapire

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1987 …2021

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  • A comparison of new and old algorithms for a mixture estimation problem

    Helmbold, D. P., Singer, Y., Schapire, R. E. & Warmuth, M. K., Jul 5 1995, Proceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995. Association for Computing Machinery, Inc, p. 69-78 10 p. (Proceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995; vol. 1995-January).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    7 Scopus citations
  • A contextual-bandit approach to personalized news article recommendation

    Li, L., Chu, W., Langford, J. & Schapire, R. E., 2010, Proceedings of the 19th International Conference on World Wide Web, WWW '10. p. 661-670 10 p. (Proceedings of the 19th International Conference on World Wide Web, WWW '10).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1271 Scopus citations
  • Adding dense, weighted connections to WordNet

    Boyd-Graber, J., Fellbaum, C., Osherson, D. & Schapire, R., 2005, GWC 2006: 3rd International Global WordNet Conference, Proceedings. Masaryk University, p. 29-35 7 p. (GWC 2006: 3rd International Global WordNet Conference, Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    86 Scopus citations
  • A decision-theoretic generalization of on-line learning and an application to boosting

    Freund, Y. & Schapire, R. E., Jan 1 1995, Computational Learning Theory - 2nd European Conference, EuroCOLT 1995, Proceedings. Vitanyi, P. (ed.). Springer Verlag, p. 23-37 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 904).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2514 Scopus citations
  • Adversarial bandits with knapsacks

    Immorlica, N., Sankararaman, K. A., Schapire, R. & Slivkins, A., Nov 2019, Proceedings - 2019 IEEE 60th Annual Symposium on Foundations of Computer Science, FOCS 2019. IEEE Computer Society, p. 202-219 18 p. 8948695. (Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS; vol. 2019-November).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    11 Scopus citations
  • A game-theoretic approach to apprenticeship learning

    Syed, U. & Schapire, R. E., 2008, Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. Curran Associates Inc., (Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    121 Scopus citations
  • A game theoretic approach to expander-based compressive sensing

    Jafarpour, S., Cevher, V. & Schapire, R. E., 2011, 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011. p. 464-468 5 p. 6034169. (IEEE International Symposium on Information Theory - Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Scopus citations
  • A generalization of principal component analysis to the exponential family

    Collins, M., Dasgupta, S. & Schapire, R. E., Jan 1 2002, Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001. Neural information processing systems foundation, (Advances in Neural Information Processing Systems).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    147 Scopus citations
  • Algorithms for portfolio management based on the Newton method

    Agarwal, A., Hazan, E., Kale, S. & Schapire, R. E., Oct 6 2006, ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning. p. 9-16 8 p. (ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning; vol. 2006).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    49 Scopus citations
  • Algorithms for portfolio management based on the Newton method

    Agarwal, A., Hazan, E., Kale, S. & Schapire, R. E., 2006, ACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006. p. 9-16 8 p. (ACM International Conference Proceeding Series; vol. 148).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    91 Scopus citations
  • A maximum entropy approach to species distribution modeling

    Phillips, S. J., Dudík, M. & Schapire, R. E., Dec 1 2004, Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004. Greiner, R. & Schuurmans, D. (eds.). p. 655-662 8 p. (Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1377 Scopus citations
  • Apprenticeship learning using linear programming

    Syed, U., Bowling, M. & Schapire, R. E., 2008, Proceedings of the 25th International Conference on Machine Learning. Association for Computing Machinery (ACM), p. 1032-1039 8 p. (Proceedings of the 25th International Conference on Machine Learning).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    121 Scopus citations
  • A reduction from apprenticeship learning to classification

    Syed, U. & Schapire, R. E., 2010, Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010. (Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    38 Scopus citations
  • A theory of multiclass boosting

    Mukherjee, I. & Schapire, R. E., Dec 1 2010, Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010. (Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    23 Scopus citations
  • ATTac-2001: A learning, autonomous bidding agent

    Stone, P., Schapire, R. E., Csirik, J. A., Littman, M. L. & McAllester, D., 2002, Agent-Mediated Electronic Commerce IV: Designing Mechanisms and Systems - AAMAS 2002 Workshop on Agent-Mediated Electronic Commerce, Revised Papers. Padget, J., Shehory, O., Parkes, D., Sadeh, N. & Walsh, W. E. (eds.). Springer Verlag, p. 143-160 18 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 2531).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    11 Scopus citations
  • Bayesian decision-making under misspecified priors with applications to meta-learning

    Simchowitz, M., Tosh, C., Krishnamurthy, A., Hsu, D., Lykouris, T., Dudík, M. & Schapire, R., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 26382-26394 13 p. (Advances in Neural Information Processing Systems; vol. 32).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Collaborative place models

    Kapicioglu, B., Rosenberg, D. S., Schapire, R. E. & Jebara, T., Jan 1 2015, IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. Wooldridge, M. & Yang, Q. (eds.). International Joint Conferences on Artificial Intelligence, p. 3612-3618 7 p. (IJCAI International Joint Conference on Artificial Intelligence; vol. 2015-January).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Scopus citations
  • Combining spatial and telemetric features for learning animal movement models

    Kapicioglu, B., Schapire, R. E., Wikelski, M. & Broderick, T., Dec 1 2010, Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010. p. 260-267 8 p. (Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Compressive sensing meets game theory

    Jafarpour, S., Schapire, R. E. & Cevher, V., 2011, 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings. p. 3660-3663 4 p. 5947144. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Scopus citations
  • Contextual decision processes with low Bellman rank are PAC-learnable

    Jiang, N., Krishnamurthy, A., Agarwal, A., Langford, J. & Schapire, R. E., Jan 1 2017, 34th International Conference on Machine Learning, ICML 2017. International Machine Learning Society (IMLS), p. 2671-2707 37 p. (34th International Conference on Machine Learning, ICML 2017; vol. 4).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    22 Scopus citations
  • Contextual search in the presence of irrational agents

    Krishnamurthy, A., Lykouris, T., Podimata, C. & Schapire, R., Jun 15 2021, STOC 2021 - Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing. Khuller, S. & Williams, V. V. (eds.). Association for Computing Machinery, p. 910-918 9 p. (Proceedings of the Annual ACM Symposium on Theory of Computing).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
    2 Scopus citations
  • Convergence and consistency of regularized Boosting algorithms with stationary β-mixing observations

    Lozano, A. C., Kulkarni, S. R. & Schapire, R. E., Dec 1 2005, Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference. p. 819-826 8 p. (Advances in Neural Information Processing Systems).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    16 Scopus citations
  • Correcting sample selection bias in maximum entropy density estimation

    Dudík, M., Schapire, R. E. & Phillips, S. J., Dec 1 2005, Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference. p. 323-330 8 p. (Advances in Neural Information Processing Systems).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    128 Scopus citations
  • DIVERSITY-BASED INFERENCE OF FINITE AUTOMATA.

    Rivest, R. L. & Schapire, R. E., 1987, Annual Symposium on Foundations of Computer Science (Proceedings). IEEE, p. 78-87 10 p. (Annual Symposium on Foundations of Computer Science (Proceedings)).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    26 Scopus citations
  • Efficient algorithms for adversarial contextual learning

    Syrgkanis, V., Krishnamurthy, A. & Schapire, R. E., Jan 1 2016, 33rd International Conference on Machine Learning, ICML 2016. Weinberger, K. Q. & Balcan, M. F. (eds.). International Machine Learning Society (IMLS), p. 3184-3206 23 p. (33rd International Conference on Machine Learning, ICML 2016; vol. 5).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    15 Scopus citations
  • Efficient distribution-free learning of probabilistic concepts (abstract)

    Kearns, M. J. & Schapire, R. E., Jul 1 1990, Proceedings of the 3rd Annual Workshop on Computational Learning Theory, COLT 1990. Association for Computing Machinery, Inc, 1 p. (Proceedings of the 3rd Annual Workshop on Computational Learning Theory, COLT 1990).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    11 Scopus citations
  • Efficient learning of typical finite automata from random walks

    Freund, Y., Keams, M., Ron, D., Rubinfeld, R., Schapire, R. E. & Sellie, L., Jun 1 1993, Proceedings of the 25th Annual ACM Symposium on Theory of Computing, STOC 1993. Association for Computing Machinery, p. 315-324 10 p. (Proceedings of the Annual ACM Symposium on Theory of Computing; vol. Part F129585).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    31 Scopus citations
  • Error-adaptive classifier boosting (EACB): Exploiting data-driven training for highly fault-tolerant hardware

    Wang, Z., Schapire, R. & Verma, N., 2014, 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014. Institute of Electrical and Electronics Engineers Inc., p. 3884-3888 5 p. 6854329. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    13 Scopus citations
  • Exact identification of circuits using fixed points of amplification functions (abstract)

    Goldman, S. A., Kearns, M. J. & Schapire, R. E., Jul 1 1990, Proceedings of the 3rd Annual Workshop on Computational Learning Theory, COLT 1990. Association for Computing Machinery, Inc, p. 388 1 p. (Proceedings of the 3rd Annual Workshop on Computational Learning Theory, COLT 1990).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    8 Scopus citations
  • FilterBoost: Regression and classification on large datasets

    Bradley, J. K. & Schapire, R. E., 2008, Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. Curran Associates Inc., (Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    44 Scopus citations
  • How boosting the margin can also boost classifier complexity

    Reyzin, L. & Schapire, R. E., Oct 6 2006, ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning. p. 753-760 8 p. (ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning; vol. 2006).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    76 Scopus citations
  • How boosting the margin can also boost classifier complexity

    Reyzin, L. & Schapire, R. E., Dec 1 2006, ACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006. p. 753-760 8 p. (ACM International Conference Proceeding Series; vol. 148).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    84 Scopus citations
  • How to use expert advice

    Cesa-Biancht, N., Freund, Y., Helmbol, D. P., Haussled, D., Schapire, R. E. & Warmuth, M. K., Jun 1 1993, Proceedings of the 25th Annual ACM Symposium on Theory of Computing, STOC 1993. Association for Computing Machinery, p. 382-391 10 p. (Proceedings of the Annual ACM Symposium on Theory of Computing; vol. Part F129585).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    67 Scopus citations
  • Imitation learning with a value-based prior

    Syed, U. & Schapire, R. E., Dec 1 2007, Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007. p. 384-391 8 p. (Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Scopus citations
  • Inference of finite automata using homing sequences

    Rivest, R. L. & Schapire, R. E., Jan 1 1989, Proc Twenty First Annu ACM Symp Theory Comput. Publ by ACM, p. 411-420 10 p. (Proc Twenty First Annu ACM Symp Theory Comput).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    75 Scopus citations
  • Learning binary relations and total orders

    Goldman, S. A., Rivest, R. L. & Schapire, R. E., 1989, Annual Symposium on Foundations of Computer Science (Proceedings). Publ by IEEE, p. 46-51 6 p. (Annual Symposium on Foundations of Computer Science (Proceedings)).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    13 Scopus citations
  • Learning deep ResNet blocks sequentially using boosting theory

    Huang, F., Ash, J. T., Langford, J. & Schapire, R. E., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Dy, J. & Krause, A. (eds.). International Machine Learning Society (IMLS), p. 3272-3290 19 p. (35th International Conference on Machine Learning, ICML 2018; vol. 5).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    11 Scopus citations
  • Learning sparse multivariate polynomials over a field with queries and counterexamples

    Schapire, R. E. & Sellie, L. M., Dec 1 1993, Proc 6 Annu ACM Conf Comput Learn Theory. Anon (ed.). Publ by ACM, p. 17-26 10 p. (Proc 6 Annu ACM Conf Comput Learn Theory).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    25 Scopus citations
  • Learning to order things

    Cohen, W. W., Schapire, R. E. & Singer, Y., Jan 1 1998, Advances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997. Neural information processing systems foundation, p. 451-457 7 p. (Advances in Neural Information Processing Systems).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    149 Scopus citations
  • Margin-based ranking meets boosting in the middle

    Rudin, C., Cortes, C., Mohri, M. & Schapire, R. E., 2005, Learning Theory - 18th Annual Conference on Learning Theory, COLT 2005, Proceedings. Springer Verlag, p. 63-78 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 3559 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    36 Scopus citations
  • Maximum entropy distribution estimation with generalized regularization

    Dudík, M. & Schapire, R. E., 2006, Learning Theory - 19th Annual Conference on Learning Theory, COLT 2006, Proceedings. Springer Verlag, p. 123-138 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 4005 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    27 Scopus citations
  • Multiclass Boosting and the Cost of Weak Learning

    Brukhim, N., Hazan, E., Moran, S., Mukherjee, I. & Schapire, R. E., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 3057-3067 11 p. (Advances in Neural Information Processing Systems; vol. 4).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • On the dynamics of boosting

    Rudin, C., Daubechies, I. & Schapire, R. E., 2004, Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003. Neural information processing systems foundation, (Advances in Neural Information Processing Systems).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Scopus citations
  • On the learnability of discrete distributions

    Kearns, M., Rubinfeld, R., Mansour, Y., Schapire, R. E., Ron, D. & Univemity, H., May 23 1994, Proceedings of the 26th Annual ACM Symposium on Theory of Computing, STOC 1994. Association for Computing Machinery, p. 273-282 10 p. (Proceedings of the Annual ACM Symposium on Theory of Computing; vol. Part F129502).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    159 Scopus citations
  • On the sample complexity of weak learning

    Goldman, S. A., Kearns, M. J. & Schapire, R. E., Jul 1 1990, Proceedings of the 3rd Annual Workshop on Computational Learning Theory, COLT 1990. Association for Computing Machinery, p. 217-231 15 p. (Proceedings of the 3rd Annual Workshop on Computational Learning Theory, COLT 1990).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    6 Scopus citations
  • Oracle-efficient online learning and auction design

    Dudik, M., Haghtalab, N., Luo, H., Schapire, R. E., Syrgkanis, V. & Vaughan, J. W., Nov 10 2017, Proceedings - 58th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2017. IEEE Computer Society, p. 528-539 12 p. 8104087. (Annual Symposium on Foundations of Computer Science - Proceedings; vol. 2017-October).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    18 Scopus citations
  • Pattern languages are not learnable

    Schapire, R. E., Jul 1 1990, Proceedings of the 3rd Annual Workshop on Computational Learning Theory, COLT 1990. Association for Computing Machinery, p. 122-129 8 p. (Proceedings of the 3rd Annual Workshop on Computational Learning Theory, COLT 1990).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    17 Scopus citations
  • Practical Contextual Bandits with Regression Oracles

    Foster, D. J., Agarwal, A., Dudik, M., Haipeng, L. & Schapire, R. E., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Dy, J. & Krause, A. (eds.). International Machine Learning Society (IMLS), p. 2482-2517 36 p. (35th International Conference on Machine Learning, ICML 2018; vol. 4).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    12 Scopus citations
  • Predicting nearly as well as the best pruning of a decision tree

    Helmbold, D. P. & Schapire, R. E., Jul 5 1995, Proceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995. Association for Computing Machinery, Inc, p. 61-68 8 p. (Proceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995; vol. 1995-January).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
    8 Scopus citations
  • Strength of weak learnability

    Schapire, R. E., 1989, Annual Symposium on Foundations of Computer Science (Proceedings). Publ by IEEE, p. 28-33 6 p. (Annual Symposium on Foundations of Computer Science (Proceedings)).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    38 Scopus citations