An introduction to computational learning theory
An introduction to computational learning theory
Bidding and allocation in combinatorial auctions
Proceedings of the 2nd ACM conference on Electronic commerce
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning from revealed preference
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Generalized Robust Conjoint Estimation
Marketing Science
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In classical revealed preference analysis we are given a sequence of linear prices (i.e., additive over goods) and an agent's demand at each of the prices. The problem is to determine whether the observed demands are consistent with utility-maximizing behavior, and if so, recover a representation of the agent's utility function. In this work, we consider a setting where an agent responds to non-linear prices and also allow for incomplete price information over the consumption set. We develop two different kernel methods to fit linear and concave utilities to such observations. The methods allow one to incorporate prior information about the utility function into the estimation procedure, and represent semi-parametric alternatives to the classical non-parametric approach. An empirical evaluation exhibits the relative merits of the two methods in terms of generalization ability, solution sparsity, and runtime performance.