Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets
暂无分享,去创建一个
[1] J. H. Hateren,et al. Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .
[2] Amos J. Storkey,et al. Isoelastic Agents and Wealth Updates in Machine Learning Markets , 2012, ICML.
[3] Mark Rubinstein,et al. Securities Market Efficiency in an Arrow-Debre Economy , 1973 .
[4] ZissermanAndrew,et al. The Pascal Visual Object Classes Challenge , 2015 .
[5] Richard Cole,et al. Fast-converging tatonnement algorithms for one-time and ongoing market problems , 2008, STOC.
[6] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[7] Tom Heskes,et al. Selecting Weighting Factors in Logarithmic Opinion Pools , 1997, NIPS.
[8] Jennifer Wortman Vaughan,et al. A new understanding of prediction markets via no-regret learning , 2010, EC '10.
[9] Nathan Lay,et al. Supervised Aggregation of Classifiers using Artificial Prediction Markets , 2010, ICML.
[10] Amos Storkey,et al. When Training and Test Sets are Different: Characterising Learning Transfer , 2013 .
[11] Cordelia Schmid,et al. The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.
[12] Nathan Lay,et al. An introduction to artificial prediction markets for classification , 2011, J. Mach. Learn. Res..
[13] Pedrito Maynard-Reid,et al. Aggregating Learned Probabilistic Beliefs , 2001, UAI.
[14] R. Cole,et al. Fast-Converging Tatonnement Algorithms for the Market Problem , 2007 .
[15] VP Jim Bennett. The $ 1 Million Netflix Challenge , 2007 .
[16] Anthony Goldbloom,et al. Data Prediction Competitions -- Far More than Just a Bit of Fun , 2010, 2010 IEEE International Conference on Data Mining Workshops.
[17] Mark Rubinstein,et al. THE STRONG CASE FOR THE GENERALIZED LOGARITHMIC UTILITY MODEL AS THE PREMIER MODEL OF FINANCIAL MARKETS , 1976 .
[18] Marco Ottaviani,et al. Aggregation of Information and Beliefs in Prediction Markets , 2007 .
[19] Michael P. Wellman,et al. Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market , 1997, UAI.
[20] David H. Wolpert,et al. Stacked generalization , 1992, Neural Networks.
[21] T. S. Jayram,et al. Generalized Opinion Pooling , 2004, ISAIM.
[22] Yehuda Koren,et al. All Together Now: A Perspective on the Netflix Prize , 2010 .
[23] Joseph M. Kahn,et al. A Generative Bayesian Model for Aggregating Experts' Probabilities , 2004, UAI.
[24] Amos J. Storkey,et al. Machine Learning Markets , 2011, AISTATS.
[25] David M. Pennock,et al. An Empirical Comparison of Algorithms for Aggregating Expert Predictions , 2006, UAI.
[26] Mark Rubinstein,et al. An aggregation theorem for securities markets , 1974 .
[27] Pedro M. Domingos. Why Does Bagging Work? A Bayesian Account and its Implications , 1997, KDD.
[28] Franz Dietrich,et al. Bayesian group belief , 2010, Soc. Choice Welf..
[29] Jacob D. Abernethy,et al. A Collaborative Mechanism for Crowdsourcing Prediction Problems , 2011, NIPS.
[30] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.