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Loss function
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#	Item
71	RTGDiscrete stochastic optimization with continuous auxiliary variables Rodolphe Le Riche* (speaker), Alexis Lasseigne, François-Xavier Irisarri * CNRS LIMOS and Ecole des Mines de St-Etienne, France ONERA Add to Reading List Source URL: grk1855.tu-dortmund.de Language: English - Date: 2015-02-13 13:31:49** Mathematical optimization Statistics Mathematical analysis Numerical analysis Stochastic optimization Estimation theory Probability theory Simulated annealing Loss function Bayesian optimization Global optimization
72	Turning Down the Noise in the Blogosphere Khalid El-Arini∗ Gaurav Veda∗ Carlos Guestrin∗ Add to Reading List Source URL: select.cs.cmu.edu Language: English - Date: 2009-08-13 03:47:05 Information science Submodular set function World Wide Web Personalization Loss function Algorithm Statistics Information
73	NDN, Technical Report NDN-0008, 2012. http://named-data.net/techreports.html Revision 1: October 10, Chronos: Serverless Multi-User Chat Over NDN Add to Reading List Source URL: www.named-data.net Language: English - Date: 2013-08-09 21:32:20 Computing Technology Computer networking Named data networking Virtual communities Internet Relay Chat Chronos Cryptographic hash function Transmission Control Protocol Packet loss Internet Communications protocol
74	JMLR: Workshop and Conference Proceedings vol 40:1–13, 2015 Bandit Convex Optimization: √ Add to Reading List Source URL: jmlr.org Language: English - Date: 2015-07-20 20:08:35 Mathematical optimization Mathematical analysis Convex analysis Convex function Analysis Regret Loss function Expected value Lipschitz continuity
75	Overview Regression Definition, Loss function, Quadratic Loss, Linear Loss, Robustness Risk Functionals Expected, Empirical, and Regularized Risk Maximum Likelihood Estimation Add to Reading List Source URL: alex.smola.org Language: English - Date: 2013-09-09 02:28:47
76	Collective Noise Contrastive Estimation for Policy Transfer Learning Weinan Zhang† , Ulrich Paquet‡ , Katja Hofmann‡ † University College London, ‡ Microsoft Research Add to Reading List Source URL: www.ulrichpaquet.com Language: English - Date: 2015-12-29 02:57:44 Maximum likelihood estimation Loss function Likelihood function Supervised learning Machine learning Applied mathematics Cognition Cognitive science
77	SHIV: Reducing Supervisor Burden in DAgger using Support Vectors for Efficient Learning from Demonstrations in High Dimensional State Spaces Michael Laskey1 , Sam Staszak1 , Wesley Yu-Shu Hsieh1 , Jeffrey Mahler1 , Flori Add to Reading List Source URL: goldberg.berkeley.edu Language: English - Date: 2016-02-18 10:52:35 Machine learning Artificial intelligence Statistics Learning Statistical classification Support vector machine Apprenticeship learning Artificial neural network Loss function Motion planning Reinforcement learning Robotics
78	On Iterative Hard Thresholding Methods for High-dimensional M-Estimation arXiv:1410.5137v2 [cs.LG] 21 OctPrateek Jain∗ Add to Reading List Source URL: arxiv.org Language: English - Date: 2014-10-21 20:16:25 Statistics Estimation theory Statistical theory Consistent estimator M-estimator Loss function Linear regression Sufficient statistic
79	arXiv:1307.3400v1 [stat.ML] 12 JulThompson Sampling for 1-Dimensional Exponential Family Bandits Nathaniel Korda, Emilie Kaufmann, and R´emi Munos Telecom Paristech UMR CNRS 5141 & INRIA Lille - Nord Europe Add to Reading List Source URL: arxiv.org Language: English - Date: 2013-07-14 23:21:30 Statistics Statistical theory Estimation theory Econometrics Bayesian statistics Design of experiments Fisher information Sufficient statistic Loss function Exponential family Bayes estimator Completeness
80	Linear Programming for Large-Scale Markov Decision Problems Yasin Abbasi-Yadkori Queensland University of Technology, Brisbane, QLD, Australia 4000 Peter L. Bartlett University of California, Berkeley, CA 94720 Add to Reading List Source URL: webdocs.cs.ualberta.ca Language: English - Date: 2014-07-10 15:18:52 Statistics Markov processes Statistical theory Estimation theory Dynamic programming Markov decision process Stochastic control Loss function Markov chain M-estimator Bearing pressure Jet bundle

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