default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
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
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 21:16 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
