Haipeng Luo
Haipeng Luo
確認したメール アドレス: usc.edu - ホームページ
タイトル
引用先
引用先
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
1862013
Variance-reduced and projection-free stochastic optimization
E Hazan, H Luo
International Conference on Machine Learning, 1263-1271, 2016
882016
Automatic scaling of internet applications for cloud computing services
Z Xiao, Q Chen, H Luo
IEEE transactions on computers 63 (5), 1111-1123, 2012
752012
Fast convergence of regularized learning in games
V Syrgkanis, A Agarwal, H Luo, RE Schapire
Advances in Neural Information Processing Systems, 2989-2997, 2015
692015
Achieving all with no parameters: Adanormalhedge
H Luo, RE Schapire
Conference on Learning Theory, 1286-1304, 2015
672015
Optimal and adaptive algorithms for online boosting
A Beygelzimer, S Kale, H Luo
International Conference on Machine Learning, 2323-2331, 2015
652015
Efficient second order online learning by sketching
H Luo, A Agarwal, N Cesa-Bianchi, J Langford
Advances in Neural Information Processing Systems, 902-910, 2016
622016
Corralling a band of bandit algorithms
A Agarwal, H Luo, B Neyshabur, RE Schapire
Conference on Learning Theory, 12-38, 2017
412017
Online gradient boosting
A Beygelzimer, E Hazan, S Kale, H Luo
Advances in neural information processing systems, 2458-2466, 2015
372015
Efficient Contextual Bandits in Non-stationary Worlds
H Luo, CY Wei, A Agarwal, J Langford
arXiv preprint arXiv:1708.01799, 2017
342017
More adaptive algorithms for adversarial bandits
CY Wei, H Luo
arXiv preprint arXiv:1801.03265, 2018
332018
Oracle-efficient online learning and auction design
M Dudik, N Haghtalab, H Luo, RE Schapire, V Syrgkanis, JW Vaughan
2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS …, 2017
282017
Improved regret bounds for oracle-based adversarial contextual bandits
V Syrgkanis, H Luo, A Krishnamurthy, RE Schapire
Advances in Neural Information Processing Systems, 3135-3143, 2016
272016
A new algorithm for non-stationary contextual bandits: Efficient, optimal, and parameter-free
Y Chen, CW Lee, H Luo, CY Wei
arXiv preprint arXiv:1902.00980, 2019
222019
Practical contextual bandits with regression oracles
DJ Foster, A Agarwal, M Dudík, H Luo, RE Schapire
arXiv preprint arXiv:1803.01088, 2018
212018
A drifting-games analysis for online learning and applications to boosting
H Luo, RE Schapire
Advances in Neural Information Processing Systems, 1368-1376, 2014
212014
Logistic regression: The importance of being improper
DJ Foster, S Kale, H Luo, M Mohri, K Sridharan
arXiv preprint arXiv:1803.09349, 2018
202018
Beating stochastic and adversarial semi-bandits optimally and simultaneously
J Zimmert, H Luo, CY Wei
arXiv preprint arXiv:1901.08779, 2019
162019
Improved path-length regret bounds for bandits
S Bubeck, Y Li, H Luo, CY Wei
arXiv preprint arXiv:1901.10604, 2019
152019
Open problem: First-order regret bounds for contextual bandits
A Agarwal, A Krishnamurthy, J Langford, H Luo
Conference on Learning Theory, 4-7, 2017
152017
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