Goods are said to be combinatorial when the value of a bundle of goods is not equal to the sum of the values of the same goods unbundled. In layman’s terms, the “whole ” can be worth more or less than sum of the “parts”. Examples of goods which are thought to exhibit such a property include airport landing slots, electromagnetic spectrum

Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with many important applications in machine learning and statistics. In this paper we propose a simple and fast algorithm SVP (Singular Value Projection) for rank minimization under affine constraints (ARMP

be able to learn about this information with a delay that grows slowly with d, and is independent of the size of the network. For nodes arranged with uniform density in Euclidean space, we present natural gossip mechanisms, called spatial gossip, that satisfy such a guarantee: new information is spread to

In Milgrom and Weber’s (1982) “general symmetric model, ” under few additional regularity conditions, the English auction maximizes the seller’s expected profit within the class of all posteriorimplementable trading procedures, and fails to do so among all interim incentive-compatible procedures

Abstract We initiate the study of incentives in a general machine learning framework. We focus on a game-theoretic regression learning setting where private information is elicited from multiple agents with different, possibly conflicting, views on how to label the points of an input space

ABSTRACT How should players bid in keyword auctions such as those used by Google, Yahoo! and MSN? We consider greedy bidding strategies for a repeated auction on a single keyword, where in each round, each player chooses some optimal bid for the next round, assuming that the other players merely

three parts. First, it identifies ideal goals for CVAs. One of the criteria identifies an incentive problem that is new to the literature on cryptographic Vickrey auctions: the disincentive of a bidder who has learned that she has lost the auction to complete the protocol. Any auction protocol

We describe a new auction protocol that enjoys the following properties: the biddings are submitted non-interactively and no information beyond the result is disclosed. The protocol is efficient for a logarithmic number of players. Our solution uses a semi-trusted third party T who learns

matrix learning, rank minimization, robust estimation, eigenvalue thresholding I.

We introduce robust learning equilibrium and apply it to the context of auctions. 1