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ORCIDRobert E. Schapire
Person information
- affiliation: Microsoft Research, Cambridge, MA, USA
- affiliation (former): Princeton University, USA
- award (2004): Paris Kanellakis Award
- award (2003): Gödel Prize
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2020 – today
- 2024
[c124]Jacob D. Abernethy, Robert E. Schapire, Umar Syed:
Lexicographic Optimization: Algorithms and Stability. AISTATS 2024: 2503-2511- [c123]Dipendra Misra, Aldo Pacchiano, Robert E. Schapire:
Provable Interactive Learning with Hindsight Instruction Feedback. ICML 2024 - [i40]Dipendra Misra, Aldo Pacchiano, Robert E. Schapire:
Provable Interactive Learning with Hindsight Instruction Feedback. CoRR abs/2404.09123 (2024) - 2023
- [j44]Akshay Krishnamurthy
, Thodoris Lykouris, Chara Podimata, Robert E. Schapire:
Contextual Search in the Presence of Adversarial Corruptions. Oper. Res. 71(4): 1120-1135 (2023) - [c122]Nataly Brukhim, Miro Dudík, Aldo Pacchiano, Robert E. Schapire:
A Unified Model and Dimension for Interactive Estimation. NeurIPS 2023 - [i39]Nataly Brukhim, Miroslav Dudík, Aldo Pacchiano, Robert E. Schapire:
A Unified Model and Dimension for Interactive Estimation. CoRR abs/2306.06184 (2023) - 2022
- [j43]Nicole Immorlica, Karthik Abinav Sankararaman, Robert E. Schapire, Aleksandrs Slivkins:
Adversarial Bandits with Knapsacks. J. ACM 69(6): 40:1-40:47 (2022) - [c121]Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire:
Provably sample-efficient RL with side information about latent dynamics. NeurIPS 2022 - [i38]Miroslav Dudík, Ziwei Ji, Robert E. Schapire, Matus Telgarsky:
Convex Analysis at Infinity: An Introduction to Astral Space. CoRR abs/2205.03260 (2022) - [i37]Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire:
Provably Sample-Efficient RL with Side Information about Latent Dynamics. CoRR abs/2205.14237 (2022) - 2021
- [c120]Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miroslav Dudík, Patrick Shafto:
Interactive Learning from Activity Description. ICML 2021: 8096-8108 - [c119]Nataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee, Robert E. Schapire:
Multiclass Boosting and the Cost of Weak Learning. NeurIPS 2021: 3057-3067 - [c118]Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire:
Bayesian decision-making under misspecified priors with applications to meta-learning. NeurIPS 2021: 26382-26394 - [c117]Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire:
Contextual search in the presence of irrational agents. STOC 2021: 910-918 - [i36]Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miroslav Dudík, Patrick Shafto:
Interactive Learning from Activity Description. CoRR abs/2102.07024 (2021) - [i35]Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire:
Bayesian decision-making under misspecified priors with applications to meta-learning. CoRR abs/2107.01509 (2021) - 2020
- [j42]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-efficient Online Learning and Auction Design. J. ACM 67(5): 26:1-26:57 (2020) - [c116]Ehsan Emamjomeh-Zadeh, David Kempe, Mohammad Mahdian, Robert E. Schapire:
Interactive Learning of a Dynamic Structure. ALT 2020: 277-296 - [c115]Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky:
Gradient descent follows the regularization path for general losses. COLT 2020: 2109-2136 - [i34]Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky:
Gradient descent follows the regularization path for general losses. CoRR abs/2006.11226 (2020)
2010 – 2019
- 2019
- [c114]Nicole Immorlica, Karthik Abinav Sankararaman, Robert E. Schapire, Aleksandrs Slivkins:
Adversarial Bandits with Knapsacks. FOCS 2019: 202-219 - [c113]Sobhan Miryoosefi, Kianté Brantley, Hal Daumé III, Miroslav Dudík, Robert E. Schapire:
Reinforcement Learning with Convex Constraints. NeurIPS 2019: 14070-14079 - [i33]Sobhan Miryoosefi, Kianté Brantley, Hal Daumé III, Miroslav Dudík, Robert E. Schapire:
Reinforcement Learning with Convex Constraints. CoRR abs/1906.09323 (2019) - 2018
- [c112]Uriel Feige, Yishay Mansour, Robert E. Schapire:
Robust Inference for Multiclass Classification. ALT 2018: 368-386 - [c111]Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire:
Practical Contextual Bandits with Regression Oracles. ICML 2018: 1534-1543 - [c110]Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire:
Learning Deep ResNet Blocks Sequentially using Boosting Theory. ICML 2018: 2063-2072 - [c109]Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Oracle-Efficient PAC RL with Rich Observations. NeurIPS 2018: 1429-1439 - [i32]Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Polynomial Time PAC Reinforcement Learning with Rich Observations. CoRR abs/1803.00606 (2018) - [i31]Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire:
Practical Contextual Bandits with Regression Oracles. CoRR abs/1803.01088 (2018) - [i30]Nicole Immorlica, Karthik Abinav Sankararaman, Robert E. Schapire, Aleksandrs Slivkins:
Adversarial Bandits with Knapsacks. CoRR abs/1811.11881 (2018) - 2017
- [c108]Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire:
Open Problem: First-Order Regret Bounds for Contextual Bandits. COLT 2017: 4-7 - [c107]Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire:
Corralling a Band of Bandit Algorithms. COLT 2017: 12-38 - [c106]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-Efficient Online Learning and Auction Design. FOCS 2017: 528-539 - [c105]Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
Contextual Decision Processes with low Bellman rank are PAC-Learnable. ICML 2017: 1704-1713 - [i29]Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire:
Learning Deep ResNet Blocks Sequentially using Boosting Theory. CoRR abs/1706.04964 (2017) - 2016
- [c104]Akshay Balsubramani, Zohar S. Karnin, Robert E. Schapire, Masrour Zoghi:
Instance-dependent Regret Bounds for Dueling Bandits. COLT 2016: 336-360 - [c103]Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire:
Efficient Algorithms for Adversarial Contextual Learning. ICML 2016: 2159-2168 - [c102]Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire:
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits. NIPS 2016: 3135-3143 - [i28]Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire:
Efficient Algorithms for Adversarial Contextual Learning. CoRR abs/1602.02454 (2016) - [i27]Jordan T. Ash, Robert E. Schapire:
Multi-Source Domain Adaptation Using Approximate Label Matching. CoRR abs/1602.04889 (2016) - [i26]David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire:
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains. CoRR abs/1603.04119 (2016) - [i25]Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire:
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits. CoRR abs/1606.00313 (2016) - [i24]Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable. CoRR abs/1610.09512 (2016) - [i23]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-Efficient Learning and Auction Design. CoRR abs/1611.01688 (2016) - [i22]Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire:
Corralling a Band of Bandit Algorithms. CoRR abs/1612.06246 (2016) - 2015
- [j41]Zhuo Wang, Robert E. Schapire, Naveen Verma:
Error Adaptive Classifier Boosting (EACB): Leveraging Data-Driven Training Towards Hardware Resilience for Signal Inference. IEEE Trans. Circuits Syst. I Regul. Pap. 62-I(4): 1136-1145 (2015) - [c101]Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi:
Contextual Dueling Bandits. COLT 2015: 563-587 - [c100]Uriel Feige, Yishay Mansour, Robert E. Schapire:
Learning and inference in the presence of corrupted inputs. COLT 2015: 637-657 - [c99]Haipeng Luo, Robert E. Schapire:
Achieving All with No Parameters: AdaNormalHedge. COLT 2015: 1286-1304 - [c98]Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara:
Collaborative Place Models. IJCAI 2015: 3612-3618 - [c97]Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire:
Efficient and Parsimonious Agnostic Active Learning. NIPS 2015: 2755-2763 - [c96]Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire:
Fast Convergence of Regularized Learning in Games. NIPS 2015: 2989-2997 - [i21]Haipeng Luo, Robert E. Schapire:
Achieving All with No Parameters: Adaptive NormalHedge. CoRR abs/1502.05934 (2015) - [i20]Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi:
Contextual Dueling Bandits. CoRR abs/1502.06362 (2015) - [i19]Matus Telgarsky, Miroslav Dudík, Robert E. Schapire:
Convex Risk Minimization and Conditional Probability Estimation. CoRR abs/1506.04513 (2015) - [i18]Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire:
Efficient and Parsimonious Agnostic Active Learning. CoRR abs/1506.08669 (2015) - [i17]Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire:
Fast Convergence of Regularized Learning in Games. CoRR abs/1507.00407 (2015) - [i16]Chu Wang, Yingfei Wang, Weinan E, Robert E. Schapire:
Functional Frank-Wolfe Boosting for General Loss Functions. CoRR abs/1510.02558 (2015) - 2014
- [j40]Aurélie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire:
Convergence and Consistency of Regularized Boosting With Weakly Dependent Observations. IEEE Trans. Inf. Theory 60(1): 651-660 (2014) - [c95]Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara:
Collaborative Ranking for Local Preferences. AISTATS 2014: 466-474 - [c94]Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudík, Robert E. Schapire, Aleksandrs Slivkins:
Robust Multi-objective Learning with Mentor Feedback. COLT 2014: 726-741 - [c93]Zhuo Wang, Robert E. Schapire, Naveen Verma:
Error-adaptive classifier boosting (EACB): Exploiting data-driven training for highly fault-tolerant hardware. ICASSP 2014: 3884-3888 - [c92]Haipeng Luo, Robert E. Schapire:
Towards Minimax Online Learning with Unknown Time Horizon. ICML 2014: 226-234 - [c91]Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire:
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. ICML 2014: 1638-1646 - [c90]Haipeng Luo, Robert E. Schapire:
A Drifting-Games Analysis for Online Learning and Applications to Boosting. NIPS 2014: 1368-1376 - [i15]Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire:
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. CoRR abs/1402.0555 (2014) - [i14]Haipeng Luo, Robert E. Schapire:
A Drifting-Games Analysis for Online Learning and Applications to Boosting. CoRR abs/1406.1856 (2014) - 2013
- [j39]Indraneel Mukherjee, Robert E. Schapire:
A theory of multiclass boosting. J. Mach. Learn. Res. 14(1): 437-497 (2013) - [j38]Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire:
The rate of convergence of AdaBoost. J. Mach. Learn. Res. 14(1): 2315-2347 (2013) - [c89]Robert E. Schapire:
Explaining AdaBoost. Empirical Inference 2013: 37-52 - [i13]Robert E. Schapire:
Advances in Boosting (Invited Talk). CoRR abs/1301.0599 (2013) - [i12]Haipeng Luo, Robert E. Schapire:
Online Learning with Unknown Time Horizon. CoRR abs/1307.8187 (2013) - 2012
- [c88]Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire:
Contextual Bandit Learning with Predictable Rewards. AISTATS 2012: 19-26 - [c87]Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies:
Open Problem: Does AdaBoost Always Cycle? COLT 2012: 46.1-46.4 - [i11]Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire:
Contextual Bandit Learning with Predictable Rewards. CoRR abs/1202.1334 (2012) - [i10]Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick:
Combining Spatial and Telemetric Features for Learning Animal Movement Models. CoRR abs/1203.3486 (2012) - [i9]Umar Syed, Robert E. Schapire:
Imitation Learning with a Value-Based Prior. CoRR abs/1206.5290 (2012) - 2011
- [c86]Sina Jafarpour, Robert E. Schapire, Volkan Cevher:
Compressive sensing meets game theory. ICASSP 2011: 3660-3663 - [c85]Sina Jafarpour, Volkan Cevher, Robert E. Schapire:
A game theoretic approach to expander-based compressive sensing. ISIT 2011: 464-468 - [c84]Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire:
Contextual Bandit Algorithms with Supervised Learning Guarantees. AISTATS 2011: 19-26 - [c83]Wei Chu, Lihong Li, Lev Reyzin, Robert E. Schapire:
Contextual Bandits with Linear Payoff Functions. AISTATS 2011: 208-214 - [c82]Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire:
The Rate of Convergence of Adaboost. COLT 2011: 537-558 - [i8]William W. Cohen, Robert E. Schapire, Yoram Singer:
Learning to Order Things. CoRR abs/1105.5464 (2011) - [i7]János A. Csirik, Michael L. Littman, David A. McAllester, Robert E. Schapire, Peter Stone:
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. CoRR abs/1106.5270 (2011) - [i6]Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire:
The Rate of Convergence of AdaBoost. CoRR abs/1106.6024 (2011) - [i5]Indraneel Mukherjee, Robert E. Schapire:
A theory of multiclass boosting. CoRR abs/1108.2989 (2011) - 2010
- [j37]Indraneel Mukherjee, Robert E. Schapire:
Learning with continuous experts using drifting games. Theor. Comput. Sci. 411(29-30): 2670-2683 (2010) - [c81]Robert E. Schapire:
The Convergence Rate of AdaBoost. COLT 2010: 308-309 - [c80]Satyen Kale, Lev Reyzin, Robert E. Schapire:
Non-Stochastic Bandit Slate Problems. NIPS 2010: 1054-1062 - [c79]Indraneel Mukherjee, Robert E. Schapire:
A Theory of Multiclass Boosting. NIPS 2010: 1714-1722 - [c78]Umar Syed, Robert E. Schapire:
A Reduction from Apprenticeship Learning to Classification. NIPS 2010: 2253-2261 - [c77]Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick:
Combining Spatial and Telemetric Features for Learning Animal Movement Models. UAI 2010: 260-267 - [c76]Lihong Li, Wei Chu, John Langford, Robert E. Schapire:
A contextual-bandit approach to personalized news article recommendation. WWW 2010: 661-670 - [i4]Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire:
An Optimal High Probability Algorithm for the Contextual Bandit Problem. CoRR abs/1002.4058 (2010) - [i3]Lihong Li, Wei Chu, John Langford, Robert E. Schapire:
A Contextual-Bandit Approach to Personalized News Article Recommendation. CoRR abs/1003.0146 (2010)
2000 – 2009
- 2009
- [j36]Zafer Barutçuoglu, Edoardo M. Airoldi, Vanessa Dumeaux, Robert E. Schapire, Olga G. Troyanskaya:
Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields. Bioinform. 25(10): 1307-1313 (2009) - [j35]Cynthia Rudin, Robert E. Schapire:
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost. J. Mach. Learn. Res. 10: 2193-2232 (2009) - [c75]Yongxin Taylor Xi, Zhen James Xiang, Peter J. Ramadge, Robert E. Schapire:
Speed and Sparsity of Regularized Boosting. AISTATS 2009: 615-622 - 2008
- [j34]Chris Bourke, Kun Deng, Stephen D. Scott, Robert E. Schapire, N. V. Vinodchandran:
On reoptimizing multi-class classifiers. Mach. Learn. 71(2-3): 219-242 (2008) - [c74]Indraneel Mukherjee, Robert E. Schapire:
Learning with Continuous Experts Using Drifting Games. ALT 2008: 240-255 - [c73]Umar Syed, Michael H. Bowling, Robert E. Schapire:
Apprenticeship learning using linear programming. ICML 2008: 1032-1039 - [c72]Ioannis C. Avramopoulos, Jennifer Rexford, Robert E. Schapire:
From Optimization to Regret Minimization and Back Again. SysML 2008 - 2007
- [j33]Miroslav Dudík, Steven J. Phillips, Robert E. Schapire:
Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling. J. Mach. Learn. Res. 8: 1217-1260 (2007) - [c71]Miroslav Dudík, David M. Blei, Robert E. Schapire:
Hierarchical maximum entropy density estimation. ICML 2007: 249-256 - [c70]Joseph K. Bradley, Robert E. Schapire:
FilterBoost: Regression and Classification on Large Datasets. NIPS 2007: 185-192 - [c69]Umar Syed, Robert E. Schapire:
A Game-Theoretic Approach to Apprenticeship Learning. NIPS 2007: 1449-1456 - [c68]Umar Syed, Robert E. Schapire:
Imitation Learning with a Value-Based Prior. UAI 2007: 384-391 - [c67]Luis E. Ortiz, Robert E. Schapire, Sham M. Kakade:
Maximum Entropy Correlated Equilibria. AISTATS 2007: 347-354 - 2006
- [j32]Zafer Barutçuoglu, Robert E. Schapire, Olga G. Troyanskaya:
Hierarchical multi-label prediction of gene function. Bioinform. 22(7): 830-836 (2006) - [c66]Miroslav Dudík, Robert E. Schapire:
Maximum Entropy Distribution Estimation with Generalized Regularization. COLT 2006: 123-138 - [c65]Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire:
Algorithms for portfolio management based on the Newton method. ICML 2006: 9-16 - [c64]Lev Reyzin, Robert E. Schapire:
How boosting the margin can also boost classifier complexity. ICML 2006: 753-760 - 2005
- [j31]Gökhan Tür, Dilek Hakkani-Tür, Robert E. Schapire:
Combining active and semi-supervised learning for spoken language understanding. Speech Commun. 45(2): 171-186 (2005) - [j30]Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra K. Gupta:
Boosting with prior knowledge for call classification. IEEE Trans. Speech Audio Process. 13(2): 174-181 (2005) - [c63]Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire:
Margin-Based Ranking Meets Boosting in the Middle. COLT 2005: 63-78 - [c62]Miroslav Dudík, Robert E. Schapire, Steven J. Phillips:
Correcting sample selection bias in maximum entropy density estimation. NIPS 2005: 323-330 - [c61]Aurélie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire:
Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. NIPS 2005: 819-826 - [i2]Patrick Haffner, Steven J. Phillips, Robert E. Schapire:
Efficient Multiclass Implementations of L1-Regularized Maximum Entropy. CoRR abs/cs/0506101 (2005) - 2004
- [j29]Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire:
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins. J. Mach. Learn. Res. 5: 1557-1595 (2004) - [c60]Miroslav Dudík, Steven J. Phillips, Robert E. Schapire:
Performance Guarantees for Regularized Maximum Entropy Density Estimation. COLT 2004: 472-486 - [c59]Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies:
Boosting Based on a Smooth Margin. COLT 2004: 502-517 - [c58]Steven J. Phillips, Miroslav Dudík, Robert E. Schapire:
A maximum entropy approach to species distribution modeling. ICML 2004 - 2003
- [j28]Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, David A. McAllester:
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. J. Artif. Intell. Res. 19: 209-242 (2003) - [j27]Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer:
An Efficient Boosting Algorithm for Combining Preferences. J. Mach. Learn. Res. 4: 933-969 (2003) - [c57]Gökhan Tür, Robert E. Schapire, Dilek Hakkani-Tür:
Active learning for spoken language understanding. ICASSP (1) 2003: 276-279 - [c56]Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire:
On the Dynamics of Boosting. NIPS 2003: 1101-1108 - 2002
- [j26]Michael Collins, Robert E. Schapire, Yoram Singer:
Logistic Regression, AdaBoost and Bregman Distances. Mach. Learn. 48(1-3): 253-285 (2002) - [j25]Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire:
The Nonstochastic Multiarmed Bandit Problem. SIAM J. Comput. 32(1): 48-77 (2002) - [c55]Peter Stone, Robert E. Schapire, János A. Csirik, Michael L. Littman, David A. McAllester:
ATTac-2001: A Learning, Autonomous Bidding Agent. AMEC 2002: 143-160 - [c54]Marie Rochery, Robert E. Schapire, Mazin G. Rahim, Narendra K. Gupta, Giuseppe Riccardi, Srinivas Bangalore, Hiyan Alshawi, Shona Douglas:
Combining prior knowledge and boosting for call classification in spoken language dialogue. ICASSP 2002: 29-32 - [c53]Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra K. Gupta:
Incorporating Prior Knowledge into Boosting. ICML 2002: 538-545 - [c52]Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik:
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. ICML 2002: 546-553 - [c51]Giuseppe Di Fabbrizio, Dawn Dutton, Narendra K. Gupta, Barbara Hollister, Mazin G. Rahim, Giuseppe Riccardi, Robert E. Schapire, Juergen Schroeter:
AT&t help desk. INTERSPEECH 2002: 2681-2684 - [c50]Robert E. Schapire:
Advances in Boosting. UAI 2002: 446-452 - 2001
- [j24]Robert E. Schapire:
Drifting Games. Mach. Learn. 43(3): 265-291 (2001) - [c49]Yoav Freund, Yishay Mansour, Robert E. Schapire:
Why averaging classifiers can protect against overfitting. AISTATS 2001: 98-105 - [c48]Michael Collins, Sanjoy Dasgupta, Robert E. Schapire:
A Generalization of Principal Components Analysis to the Exponential Family. NIPS 2001: 617-624 - 2000
- [j23]Erin L. Allwein, Robert E. Schapire, Yoram Singer:
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. J. Mach. Learn. Res. 1: 113-141 (2000) - [j22]Robert E. Schapire, Yoram Singer:
BoosTexter: A Boosting-based System for Text Categorization. Mach. Learn. 39(2/3): 135-168 (2000) - [c47]Raj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer, Amit Singhal:
Boosting for Document Routing. CIKM 2000: 70-77 - [c46]David A. McAllester, Robert E. Schapire:
On the Convergence Rate of Good-Turing Estimators. COLT 2000: 1-6 - [c45]Michael Collins, Robert E. Schapire, Yoram Singer:
Logistic Regression, AdaBoost and Bregman Distances. COLT 2000: 158-169 - [c44]Erin L. Allwein, Robert E. Schapire, Yoram Singer:
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. ICML 2000: 9-16 - [i1]Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire:
Gambling in a rigged casino: The adversarial multi-armed bandit problem. Electron. Colloquium Comput. Complex. TR00 (2000)
1990 – 1999
- 1999
- [j21]William W. Cohen, Robert E. Schapire, Yoram Singer:
Learning to Order Things. J. Artif. Intell. Res. 10: 243-270 (1999) - [j20]Yoav Freund, Robert E. Schapire:
Large Margin Classification Using the Perceptron Algorithm. Mach. Learn. 37(3): 277-296 (1999) - [j19]Robert E. Schapire, Yoram Singer:
Improved Boosting Algorithms Using Confidence-rated Predictions. Mach. Learn. 37(3): 297-336 (1999) - [c43]Robert E. Schapire:
Theoretical Views of Boosting and Applications. ALT 1999: 13-25 - [c42]Robert E. Schapire:
Drifting Games. COLT 1999: 114-124 - [c41]Steven Abney, Robert E. Schapire, Yoram Singer:
Boosting Applied to Tagging and PP Attachment. EMNLP 1999 - [c40]Robert E. Schapire:
Theoretical Views of Boosting. EuroCOLT 1999: 1-10 - [c39]Robert E. Schapire:
A Brief Introduction to Boosting. IJCAI 1999: 1401-1406 - 1998
- [c38]Robert E. Schapire, Yoram Singer:
Improved Boosting Algorithms using Confidence-Rated Predictions. COLT 1998: 80-91 - [c37]Yoav Freund, Robert E. Schapire:
Large Margin Classification Using the Perceptron Algorithm. COLT 1998: 209-217 - [c36]Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer:
An Efficient Boosting Algorithm for Combining Preferences. ICML 1998: 170-178 - [c35]Robert E. Schapire, Yoram Singer, Amit Singhal:
Boosting and Rocchio Applied to Text Filtering. SIGIR 1998: 215-223 - 1997
- [j18]Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie:
Efficient Learning of Typical Finite Automata from Random Walks. Inf. Comput. 138(1): 23-48 (1997) - [j17]Nicolò Cesa-Bianchi, Yoav Freund, David Haussler, David P. Helmbold, Robert E. Schapire, Manfred K. Warmuth:
How to use expert advice. J. ACM 44(3): 427-485 (1997) - [j16]Yoav Freund, Robert E. Schapire:
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J. Comput. Syst. Sci. 55(1): 119-139 (1997) - [j15]David P. Helmbold, Robert E. Schapire:
Predicting Nearly As Well As the Best Pruning of a Decision Tree. Mach. Learn. 27(1): 51-68 (1997) - [j14]David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth:
A Comparison of New and Old Algorithms for a Mixture Estimation Problem. Mach. Learn. 27(1): 97-119 (1997) - [c34]Robert E. Schapire:
Using output codes to boost multiclass learning problems. ICML 1997: 313-321 - [c33]Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee:
Boosting the margin: A new explanation for the effectiveness of voting methods. ICML 1997: 322-330 - [c32]William W. Cohen, Robert E. Schapire, Yoram Singer:
Learning to Order Things. NIPS 1997: 451-457 - [c31]Yoav Freund, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth:
Using and Combining Predictors That Specialize. STOC 1997: 334-343 - [e1]Yoav Freund, Robert E. Schapire:
Proceedings of the Tenth Annual Conference on Computational Learning Theory, COLT 1997, Nashville, Tennessee, USA, July 6-9, 1997. ACM 1997, ISBN 0-89791-891-6 [contents] - 1996
- [j13]Robert E. Schapire, Linda Sellie:
Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples. J. Comput. Syst. Sci. 52(2): 201-213 (1996) - [j12]Robert E. Schapire, Manfred K. Warmuth:
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. Mach. Learn. 22(1-3): 95-121 (1996) - [c30]Yoav Freund, Robert E. Schapire:
Game Theory, On-Line Prediction and Boosting. COLT 1996: 325-332 - [c29]Yoav Freund, Robert E. Schapire:
Experiments with a New Boosting Algorithm. ICML 1996: 148-156 - [c28]David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth:
On-Line Portfolio Selection Using Multiplicative Updates. ICML 1996: 243-251 - [c27]David D. Lewis, Robert E. Schapire, James P. Callan, Ron Papka:
Training Algorithms for Linear Text Classifiers. SIGIR 1996: 298-306 - 1995
- [j11]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
On the Sample Complexity of Weakly Learning. Inf. Comput. 117(2): 276-287 (1995) - [c26]David P. Helmbold, Robert E. Schapire:
Predicting Nearly as Well as the Best Pruning of a Decision Tree. COLT 1995: 61-68 - [c25]David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth:
A Comparison of New and Old Algorithms for a Mixture Estimation Problem. COLT 1995: 69-78 - [c24]Yoav Freund, Robert E. Schapire:
A decision-theoretic generalization of on-line learning and an application to boosting. EuroCOLT 1995: 23-37 - [c23]Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire:
Gambling in a Rigged Casino: The Adversarial Multi-Arm Bandit Problem. FOCS 1995: 322-331 - [c22]Yoav Freund, Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire:
Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries. FOCS 1995: 332-341 - 1994
- [j10]Ronald L. Rivest, Robert E. Schapire:
Diversity-Based Inference of Finite Automata. J. ACM 41(3): 555-589 (1994) - [j9]Michael J. Kearns, Robert E. Schapire:
Efficient Distribution-Free Learning of Probabilistic Concepts. J. Comput. Syst. Sci. 48(3): 464-497 (1994) - [j8]Robert E. Schapire:
Learning Probabilistic Read-once Formulas on Product Distributions. Mach. Learn. 14(1): 47-81 (1994) - [j7]David Haussler, Michael J. Kearns, Robert E. Schapire:
Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. Mach. Learn. 14(1): 83-113 (1994) - [j6]Michael J. Kearns, Robert E. Schapire, Linda Sellie:
Toward Efficient Agnostic Learning. Mach. Learn. 17(2-3): 115-141 (1994) - [c21]Robert E. Schapire, Manfred K. Warmuth:
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. ICML 1994: 266-274 - [c20]Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie:
On the learnability of discrete distributions. STOC 1994: 273-282 - 1993
- [j5]Ronald L. Rivest, Robert E. Schapire:
Inference of Finite Automata Using Homing Sequences. Inf. Comput. 103(2): 299-347 (1993) - [j4]Harris Drucker, Robert E. Schapire, Patrice Y. Simard:
Boosting Performance in Neural Networks. Int. J. Pattern Recognit. Artif. Intell. 7(4): 705-719 (1993) - [j3]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions. SIAM J. Comput. 22(4): 705-726 (1993) - [j2]Sally A. Goldman, Ronald L. Rivest, Robert E. Schapire:
Learning Binary Relations and Total Orders. SIAM J. Comput. 22(5): 1006-1034 (1993) - [c19]Robert E. Schapire, Linda Sellie:
Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples. COLT 1993: 17-26 - [c18]Ronald L. Rivest, Robert E. Schapire:
Inference of Finite Automata Using Homing Sequences. Machine Learning: From Theory to Applications 1993: 51-73 - [c17]Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie:
Efficient learning of typical finite automata from random walks. STOC 1993: 315-324 - [c16]Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert E. Schapire, Manfred K. Warmuth:
How to use expert advice. STOC 1993: 382-391 - 1992
- [b1]Robert E. Schapire:
Design and analysis of efficient learning algorithms. ACM Doctoral dissertation award ; 1991, MIT Press 1992, ISBN 978-0-262-19325-2, pp. I-IX, 1-217 - [c15]Michael J. Kearns, Robert E. Schapire, Linda Sellie:
Toward Efficient Agnostic Learning. COLT 1992: 341-352 - [c14]Harris Drucker, Robert E. Schapire, Patrice Y. Simard:
Improving Performance in Neural Networks Using a Boosting Algorithm. NIPS 1992: 42-49 - 1991
- [c13]David Haussler, Michael J. Kearns, Robert E. Schapire:
Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. COLT 1991: 61-74 - [c12]Robert E. Schapire:
Learning Probabilistic Read-Once Formulas on Product Distributions. COLT 1991: 184-198 - [c11]David Haussler, Michael J. Kearns, Manfred Opper, Robert E. Schapire:
Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. NIPS 1991: 855-862 - 1990
- [j1]Robert E. Schapire:
The Strength of Weak Learnability. Mach. Learn. 5: 197-227 (1990) - [c10]Robert E. Schapire:
Pattern Languages are not Learnable. COLT 1990: 122-129 - [c9]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
On the Sample Complexity of Weak Learning. COLT 1990: 217-231 - [c8]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). COLT 1990: 388 - [c7]Michael J. Kearns, Robert E. Schapire:
Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract). COLT 1990: 389 - [c6]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
Exact Identification of Circuits Using Fixed Points of Amplification Functions (Extended Abstract). FOCS 1990: 193-202 - [c5]Michael J. Kearns, Robert E. Schapire:
Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract). FOCS 1990: 382-391
1980 – 1989
- 1989
- [c4]Robert E. Schapire:
The Strength of Weak Learnability (Extended Abstract). FOCS 1989: 28-33 - [c3]Sally A. Goldman, Ronald L. Rivest, Robert E. Schapire:
Learning Binary Relations and Total Orders (Extended Abstract). FOCS 1989: 46-51 - [c2]Ronald L. Rivest, Robert E. Schapire:
Inference of Finite Automata Using Homing Sequences (Extended Abstract). STOC 1989: 411-420 - 1987
- [c1]Ronald L. Rivest, Robert E. Schapire:
Diversity-Based Inference of Finite Automata (Extended Abstract). FOCS 1987: 78-87
Coauthor Index
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