A Sample Complexity Measure with Applications to Learning Optimal Auctions
نویسنده
چکیده
We introduce a new sample complexity measure, which we refer to as split-sample growth rate. For any hypothesis H and for any sample S of size m, the split-sample growth rate τ̂H(m) counts how many different hypotheses can empirical risk minimization output on any sub-sample of S of size m/2. We show that the expected generalization error is upper bounded by O ( √
منابع مشابه
A Comparative Study of Multi-Attribute Continuous Double Auction Mechanisms
Auctions have been as a competitive method of buying and selling valuable or rare items for a long time. Single-sided auctions in which participants negotiate on a single attribute (e.g. price) are very popular. Double auctions and negotiation on multiple attributes create more advantages compared to single-sided and single-attribute auctions. Nonetheless, this adds the complexity of the auctio...
متن کاملSample Complexity of Multi-Item Profit Maximization
We study the design of pricing mechanisms and auctions when the mechanism designer does not know the distribution of buyers’ values. Instead the mechanism designer receives a set of samples from this distribution and his goal is to use the sample to design a pricing mechanism or auction with high expected profit. We provide generalization guarantees which bound the difference between average pr...
متن کاملLearning Simple Auctions
We present a general framework for proving polynomial sample complexity bounds for the problem of learning from samples the best auction in a class of “simple” auctions. Our framework captures all of the most prominent examples of “simple” auctions, including anonymous and non-anonymous item and bundle pricings, with either a single or multiple buyers. The technique we propose is to break the a...
متن کاملNon-parametric Revenue Optimization for Generalized Second Price auctions
We present an extensive analysis of the key problem of learning optimal reserve prices for generalized second price auctions. We describe two algorithms for this task: one based on density estimation, and a novel algorithm benefiting from solid theoretical guarantees and with a very favorable running-time complexity of O(nS log(nS)), where n is the sample size and S the number of slots. Our the...
متن کاملAn Optimization Framework for Combining the Petroleum Replenishment Problem with the Optimal Bidding in Combinatorial Auctions
We address in this paper a periodic petroleum station replenishment problem (PPSRP) that aims to plan the delivery of petroleum products to a set of geographically dispatched stations. It is assumed that each station is characterized by its weekly demand and by its frequency of service. The main objective of the delivery process is to minimize the total travelled distance by the vailable trucks...
متن کامل