Chicheng Zhang

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Kamalika ChaudhuriAssociate Professor of Computer Science, UCSD.Verified email at eng.ucsd.edu
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Akshay KrishnamurthyUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Alekh AgarwalGoogleVerified email at google.com
Jordan T. AshPostdoc at Microsoft Research NYCVerified email at princeton.edu
Aleksandrs SlivkinsMicrosoft Research NYCVerified email at microsoft.com
Songbai YanUniversity of California San DiegoVerified email at eng.ucsd.edu
Jie ShenAssistant Professor, Stevens Institute of TechnologyVerified email at stevens.edu
Zhi WangPhD Student, University of California San DiegoVerified email at ucsd.edu
pranjal awasthiRutgers UniversityVerified email at rutgers.edu
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandVerified email at umiacs.umd.edu
Sahand NegahbanAssociate Professor, Yale UniversityVerified email at yale.edu
Yining ChenStanford UniversityVerified email at stanford.edu
Tengyu MAStanford UniversityVerified email at stanford.edu
Haipeng LuoUniversity of Southern CaliforniaVerified email at usc.edu
Maryam MajzoubiGoogle, PhD New York University 2021Verified email at google.com
Francesco OrabonaAssociate Professor, Boston UniversityVerified email at orabona.com
Dávid PálStaff Machine Learning Engineer, InstacartVerified email at instacart.com
Balazs SzorenyiSr Research Scientist at Yahoo ResearchVerified email at yahooinc.com
Devanathan ThiruvenkatachariYahoo Research / VerizonmediaVerified email at verizonmedia.com

Chicheng Zhang

University of Arizona

Verified email at cs.arizona.edu - Homepage

Machine Learning Learning Theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Deep batch active learning by diverse, uncertain gradient lower bounds JT Ash, C Zhang, A Krishnamurthy, J Langford, A Agarwal International Conference on Learning Representations, 2020	197	2020
Beyond disagreement-based agnostic active learning C Zhang, K Chaudhuri Neural Information Processing Systems, 2014	88	2014
Active learning from weak and strong labelers C Zhang, K Chaudhuri Neural Information Processing Systems, 2015	81	2015
Contextual bandits with continuous actions: Smoothing, zooming, and adapting A Krishnamurthy, J Langford, A Slivkins, C Zhang Conference on Learning Theory, 2025-2027, 2019	41	2019
Revisiting perceptron: Efficient and label-optimal learning of halfspaces S Yan, C Zhang Neural Information Processing Systems, 2017	39*	2017
Efficient active learning of sparse halfspaces C Zhang Conference on Learning Theory, 1856-1880, 2018	27	2018
Search improves label for active learning A Beygelzimer, D Hsu, J Langford, C Zhang Neural Information Processing Systems, 2016	27	2016
Efficient active learning of sparse halfspaces with arbitrary bounded noise C Zhang, J Shen, P Awasthi Neural Information Processing Systems, 2020	21	2020
Warm-starting contextual bandits: Robustly combining supervised and bandit feedback C Zhang, A Agarwal, H Daumé III, J Langford, SN Negahban International Conference on Machine Learning, 2019	20	2019
The extended Littlestone’s dimension for learning with mistakes and abstentions C Zhang, K Chaudhuri Conference on Learning Theory, 1584-1616, 2016	16	2016
Efficient Contextual Bandits with Continuous Actions M Majzoubi, C Zhang, R Chari, A Krishnamurthy, J Langford, A Slivkins Neural Information Processing Systems, 2020	12	2020
Efficient Online Bandit Multiclass Learning with \sqrt{T} Regret A Beygelzimer, F Orabona, C Zhang International Conference on Machine Learning, 488-497, 2017	11	2017
Active online learning with hidden shifting domains Y Chen, H Luo, T Ma, C Zhang International Conference on Artificial Intelligence and Statistics, 2053-2061, 2021	7*	2021
Multitask bandit learning through heterogeneous feedback aggregation Z Wang, C Zhang, MK Singh, L Riek, K Chaudhuri International Conference on Artificial Intelligence and Statistics, 1531-1539, 2021	7	2021
Improved algorithms for efficient active learning halfspaces with massart and tsybakov noise C Zhang, Y Li Proceedings of Machine Learning Research vol 134, 1-2, 2021	7	2021
Attribute-efficient learning of halfspaces with malicious noise: Near-optimal label complexity and noise tolerance J Shen, C Zhang Algorithmic Learning Theory, 2021	5	2021
Crush optimism with pessimism: Structured bandits beyond asymptotic optimality KS Jun, C Zhang Neural Information Processing Systems, 2020	5	2020
Bandit multiclass linear classification: Efficient algorithms for the separable case A Beygelzimer, D Pal, B Szorenyi, D Thiruvenkatachari, CY Wei, C Zhang International Conference on Machine Learning, 624-633, 2019	5	2019
Spectral learning of large structured hmms for comparative epigenomics C Zhang, J Song, KC Chen, K Chaudhuri Neural Information Processing Systems, 2015	5	2015
Stochastic multi-player bandit learning from player-dependent feedback Z Wang, MK Singh, C Zhang, LD Riek, K Chaudhuri ICML Workshop on Real World Experiment Design and Active Learning, 2020	4	2020

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