Ariel D. Procaccia

Publications247
h-index58
Citations9,707
Highly Influential Citations805
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Variational Dropout and the Local Reparameterization Trick
TLDR
The Variational dropout method is proposed, a generalization of Gaussian dropout, but with a more flexibly parameterized posterior, often leading to better generalization in stochastic gradient variational Bayes.
The Unreasonable Fairness of Maximum Nash Welfare
TLDR
It is proved that the maximum Nash welfare solution selects allocations that are envy free up to one good --- a compelling notion that is quite elusive when coupled with economic efficiency.
Approximate mechanism design without money
TLDR
This paper establishes tight upper and lower bounds for the approximation ratio given by strategyproof mechanisms without payments, with respect to both deterministic and randomized mechanisms, under two objective functions: the social cost, and the maximum cost.
Fair enough: guaranteeing approximate maximin shares
TLDR
This work considers the problem of fairly allocating indivisible goods, focusing on a recently- introduced notion of fairness called maximin share guarantee, and shows that allocations guaranteeing each player 2/3 of the above value always exist, and can be computed in polynomial time when the number of players is constant.
On the complexity of achieving proportional representation
TLDR
It is demonstrated that winner selection in two prominent proportional representation voting systems is a computationally intractable problem—implying that these systems are impractical when the assembly is large, and in settings where the size of the Assembly is constant, the problem can be solved in polynomial time.
Truth, justice, and cake cutting
Approximate Mechanism Design without Money
TLDR
This article argues for the first time that approximation can be leveraged to obtain truthfulness without resorting to payments in highly structured optimization problems to which standard economic money-based mechanisms cannot be applied efficiently.
A note on competitive diffusion through social networks
No agent left behind: dynamic fair division of multiple resources
TLDR
This work develops a dynamic model of fair division, and proposes desirable axiomatic properties for dynamic resource allocation mechanisms, and construct two novel mechanisms that provably satisfy some of these properties, and analyze their performance using real data.
Optimal social choice functions: A utilitarian view
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