Haipeng Luo
Haipeng Luo
Verified email at usc.edu - Homepage
Title
Cited by
Cited by
Year
Adaptive resource provisioning for the cloud using online bin packing
W Song, Z Xiao, Q Chen, H Luo
IEEE Transactions on Computers 63 (11), 2647-2660, 2013
2232013
Variance-reduced and projection-free stochastic optimization
E Hazan, H Luo
International Conference on Machine Learning, 1263-1271, 2016
1242016
Fast convergence of regularized learning in games
V Syrgkanis, A Agarwal, H Luo, RE Schapire
arXiv preprint arXiv:1507.00407, 2015
1062015
Achieving all with no parameters: Adanormalhedge
H Luo, RE Schapire
Conference on Learning Theory, 1286-1304, 2015
832015
Automatic scaling of internet applications for cloud computing services
Z Xiao, Q Chen, H Luo
IEEE Transactions on Computers 63 (5), 1111-1123, 2012
802012
Efficient second order online learning by sketching
H Luo, A Agarwal, N Cesa-Bianchi, J Langford
arXiv preprint arXiv:1602.02202, 2016
782016
Optimal and adaptive algorithms for online boosting
A Beygelzimer, S Kale, H Luo
International Conference on Machine Learning, 2323-2331, 2015
712015
Corralling a band of bandit algorithms
A Agarwal, H Luo, B Neyshabur, RE Schapire
Conference on Learning Theory, 12-38, 2017
642017
More adaptive algorithms for adversarial bandits
CY Wei, H Luo
Conference On Learning Theory, 1263-1291, 2018
572018
Efficient Contextual Bandits in Non-stationary Worlds
H Luo, CY Wei, A Agarwal, J Langford
arXiv preprint arXiv:1708.01799, 2017
552017
Online gradient boosting
A Beygelzimer, E Hazan, S Kale, H Luo
arXiv preprint arXiv:1506.04820, 2015
512015
A new algorithm for non-stationary contextual bandits: Efficient, optimal and parameter-free
Y Chen, CW Lee, H Luo, CY Wei
Conference on Learning Theory, 696-726, 2019
412019
Practical contextual bandits with regression oracles
D Foster, A Agarwal, M Dudik, H Luo, R Schapire
International Conference on Machine Learning, 1539-1548, 2018
362018
Oracle-efficient online learning and auction design
M Dudík, N Haghtalab, H Luo, RE Schapire, V Syrgkanis, JW Vaughan
2017 ieee 58th annual symposium on foundations of computer science (focs …, 2017
332017
Learning adversarial mdps with bandit feedback and unknown transition
C Jin, T Jin, H Luo, S Sra, T Yu
arXiv preprint arXiv:1912.01192, 2019
32*2019
Beating stochastic and adversarial semi-bandits optimally and simultaneously
J Zimmert, H Luo, CY Wei
International Conference on Machine Learning, 7683-7692, 2019
322019
Logistic regression: The importance of being improper
DJ Foster, S Kale, H Luo, M Mohri, K Sridharan
Conference On Learning Theory, 167-208, 2018
322018
Improved regret bounds for oracle-based adversarial contextual bandits
V Syrgkanis, H Luo, A Krishnamurthy, RE Schapire
arXiv preprint arXiv:1606.00313, 2016
322016
Model selection for contextual bandits
DJ Foster, A Krishnamurthy, H Luo
arXiv preprint arXiv:1906.00531, 2019
262019
A drifting-games analysis for online learning and applications to boosting
H Luo, RE Schapire
arXiv preprint arXiv:1406.1856, 2014
252014
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