Designing an incentive compatible auction that maximizes expected revenue is a central
problem in Auction Design. While theoretical approaches to the problem have hit some …

Given ap-coin that lands heads with unknown probability p, we wish to produce an f (p)-coin
for a given function f:(0, 1)→(0, 1). This problem is commonly known as the Bernoulli factory …

In this paper, we provide a transform from an ε-BIC mechanism into an exactly BIC
mechanism without any loss of social welfare and with additive and negligible revenue loss …

Increasingly, copious amounts of consumer data are used to learn high-revenue
mechanisms via machine learning. Existing research on mechanism design via machine …

A Bernoulli factory is an algorithmic procedure for exact sampling of certain random
variables having only Bernoulli access to their parameters. Bernoulli access to a parameter …

Abstract The Empirical Revenue Maximization (ERM) is one of the most important price
learning algorithms in auction design: as the literature shows it can learn approximately …

We consider the problem of computing with many coins of unknown bias. We are given
access to samples of n coins with unknown biases p 1,…, pn and are asked to sample from …

We construct explicit combinatorial Bernoulli factories for the following class of flow-based
polytopes: integral 0/1-polytopes defined by a set of network flow constraints. This …