Maximization of Approximately Submodular Functions. Lior Seeman , Yaron Singer: Pricing Tasks in Online Labor Markets. Posting Prices with Unknown Distributions. As it turns out, however, implementing incentive compatible protocols as advocated in classical mechanism design theory often necessitates solving intractable problems. Robust Influence Maximization for Hyperparametric Models. Distributed Computation of Complex Contagion in Networks.

Avinatan Hassidim , Yaron Singer: Approximation Guarantees for Adaptive Sampling. Limitations and Possibilities of Algorithmic Mechanism Design. Inapproximability of Combinatorial Public Projects. Learnability of Influence in Networks. Pricing Tasks in Online Labor Markets. Robust Classification of Financial Risk.

Distributed Computation of Complex Contagion in Networks. Incentives, Computation, and Networks: Christos Papadimitriou BibTeX citation: The Limitations of Optimization from Samples. Incentives, Computation, and Networks: PapadimitriouGeorge PierrakosYaron Singer: Shahar DobzinskiChristos H.

We introduce a novel class of problems where the bottleneck for implementation is yafon constraint on payments.

Thibaut HorelYaron Singer: Skip to main content. In the first part of this thesiis we show the limitations of algorithmic mechanism design.

# dblp: Yaron Singer

Yaron SingerManas Mittal: Influence maximization through adaptive seeding. We show that for a broad class of these problems, there are incentive compatible mechanisms with desirable approximation guarantees that do not require overpayments. Mechanisms for complement-free procurement. The theory, known as algorithmic mechanism design, builds on the foundations of classical mechanism design from microeconomics and is based on the idea of incentive compatible protocols.

Posting Prices with Unknown Distributions. Submodular Optimization under Noise.

Terms of Use Privacy Policy Imprint. Fast Parallel Algorithms for Feature Selection.

Efficiency-Revenue Trade-Offs in Auctions. Information-theoretic lower bounds for convex optimization with erroneous oracles.

## Shaddin Dughmi

The Importance of Communities for Learning to Influence. Yuval ShavittYaron Singer: Mechanisms for Fair Attribution.

Computation and incentives in combinatorial public projects.

Uaron Optimization for Non-Convex Objectives. This settles the central open question in algorithmic mechanism design which, since its inception, has been focused on trying to show the hardness of polynomial time incentive compatibility.

Learning to Optimize Combinatorial Functions. How to win friends and influence people, truthfully: Robust Influence Maximization for Hyperparametric Models.

Efficiency-Revenue Trade-offs in Auctions. Approximability of Adaptive Seeding under Knapsack Constraints.

In the past decade, a theory of manipulation-robust algorithms has been emerging to address the challenges that frequently occur in strategic environments such as the internet.

Approximation Guarantees for Adaptive Sampling. Ashwinkumar BadanidiyuruChristos H.