Beygelzimer, Alina

26 publications

JMLR 2021 Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program) Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Lariviere, Alina Beygelzimer, Florence d'Alche-Buc, Emily Fox, Hugo Larochelle

ICML 2019 Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case Alina Beygelzimer, David Pal, Balazs Szorenyi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang

COLT 2019 Conference on Learning Theory 2019: Preface Alina Beygelzimer, Daniel Hsu

ICML 2019 Contextual Memory Trees Wen Sun, Alina Beygelzimer, Hal Daumé Iii, John Langford, Paul Mineiro

ICML 2018 A Reductions Approach to Fair Classification Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach

ICML 2017 Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret Alina Beygelzimer, Francesco Orabona, Chicheng Zhang

IJCAI 2016 Optimal and Adaptive Algorithms for Online Boosting Alina Beygelzimer, Satyen Kale, Haipeng Luo

NeurIPS 2016 Search Improves Label for Active Learning Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang

NeurIPS 2015 Online Gradient Boosting Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo

ICML 2015 Optimal and Adaptive Algorithms for Online Boosting Alina Beygelzimer, Satyen Kale, Haipeng Luo

NeurIPS 2014 Scalable Non-Linear Learning with Adaptive Polynomial Expansions Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus J Telgarsky

AISTATS 2011 Contextual Bandit Algorithms with Supervised Learning Guarantees Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert Schapire

NeurIPS 2010 Agnostic Active Learning Without Constraints Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang

UAI 2009 Conditional Probability Tree Estimation Analysis and Algorithms Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl

ALT 2009 Error-Correcting Tournaments Alina Beygelzimer, John Langford, Pradeep Ravikumar

ICML 2009 Importance Weighted Active Learning Alina Beygelzimer, Sanjoy Dasgupta, John Langford

ICML 2009 Tutorial Summary: Reductions in Machine Learning Alina Beygelzimer, John Langford, Bianca Zadrozny

MLJ 2008 Robust Reductions from Ranking to Classification Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin

COLT 2007 Robust Reductions from Ranking to Classification Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin

ICML 2006 Agnostic Active Learning Maria-Florina Balcan, Alina Beygelzimer, John Langford

ICML 2006 Cover Trees for Nearest Neighbor Alina Beygelzimer, Sham M. Kakade, John Langford

UAI 2005 Efficient Test Selection in Active Diagnosis via Entropy Approximation Alice X. Zheng, Irina Rish, Alina Beygelzimer

ICML 2005 Error Limiting Reductions Between Classification Tasks Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny

COLT 2005 Sensitive Error Correcting Output Codes John Langford, Alina Beygelzimer

AAAI 2005 Weighted One-Against-All Alina Beygelzimer, John Langford, Bianca Zadrozny

NeurIPS 2003 Approximability of Probability Distributions Alina Beygelzimer, Irina Rish