approaches including follow-the-leader, exponential weighting, Covers algorithm and gradient descent. Pdf ieee Transactions on Computers, 63:1111-1123, 2012. Efficient algorithms for online convex optimization and their applications. Keywords, online learning Online optimization Regret minimization Portfolio management, download to read the full article text. Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo and Robert. The matrix reference manual. Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer, Advances in Neural Information Processing Systems 17, MIT Press, 2005. Author: Satyen Kale, princeton University, publication: Doctoral Dissertation, efficient algorithms using the multiplicative weights update method.
Logarithmic regret algorithms for online convex optimization Haipeng Luo - Machine Learning - University of Southern California Shai Shalev-Shwartz - CS - Huji The End of Solitude The Art of the Paragraph: Internal Monologues in Fiction
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Best Paper Award In Proceedings of the 25th International Joint Conference on Aritifical Intelligence (ijcai 2016, sister conference best paper track) short version Haipeng Luo and Robert. Alekh Agarwal, Haipeng Luo, Behnam Neyshabur and Robert. Corralling a Band of Bandit Algorithms. The main new ideas give rise to an efficient algorithm based on the Newton method for optimization, a new tool in the field. T convex cost functions exchange program essay (of bounded gradients with respect to the best single decision in hindsight. A ShermanMorrisonWoodbury identity for rank augmenting matrices with application to centering. Zbmath, google Scholar, cover,. Towards Minimax Online Learning with Unknown Time Horizon. A technical report with a generalized logarithmic regret and detailed proofs: "Logarithmic Regret Algorithms for Strongly Convex Repeated Games",Technical Report 2007-42, The Hebrew University, May 2007. Zhen Xiao, Qi Chen and Haipeng Luo.
Practical Contextual Bandits with Regression Oracles. Arxiv, to appear at Advances in Neural Information Processing Systems 31 (nips 2018) as a spotlight. Elad Hazan and Haipeng Luo. In this paper, we give algorithms that achieve regret.
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