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The design of revenue-maximizing combinatorial auctions, i.e. multi-item auctions over bundles of goods, is one of the most fundamental problems in computational economics, unsolved even for two bidders and two items for sale. In the traditional economic models, it is assumed that the bidders’ valuations are drawn from an underlying distribution and that… (More)

- Maria-Florina Balcan, Ellen Vitercik, Colin White
- COLT
- 2016

A large body of work in machine learning has focused on the problem of learning a close approximation to an underlying combinatorial function, given a small set of labeled examples. However, for real-valued functions, cardinal labels might not be accessible, or it may be difficult for an expert to consistently assign real-valued labels over the entire set… (More)

- Bernhard Haeupler, Amirbehshad Shahrasbi, Ellen Vitercik
- ArXiv
- 2017

We present many new results related to reliable (interactive) communication over insertiondeletion channels. Synchronization errors, such as insertions and deletions, strictly generalize the usual symbol corruption errors and are much harder to protect against. We show how to hide the complications of synchronization errors in many applications by… (More)

- Allison Bishop, Ellen Vitercik
- ArXiv
- 2015

We consider interactive coding in a setting where n parties wish to compute a joint function of their inputs via an interactive protocol over imperfect channels. We assume that adversarial errors can comprise a O( 1 n ) fraction of the total communication, occurring anywhere on the communication network. Our goal is to maintain a constant multiplicative… (More)

- Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
- ArXiv
- 2017

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… (More)

- Christine M Evangelista, Jonathan Katz, +4 authors Ellen Vitercik
- 2015

De, Trevisan, and Tulsiani [?] show that for every permutation f : [N ] → [N ], any algorithm using time T and space S that inverts f on an ε fraction of inputs must satisfy the lower bound TS = Ω̃(εN). We show a TS = Ω̃(εN/k) trade-off lower bound for the complexity of an inverter for k-to-1 functions. This implies that salt is an effective countermeasure.… (More)

Max-cut, clustering, and many other partitioning problems that are of significant importance to machine learning and other scientific fields are NP-hard, a reality that has motivated researchers to develop a wealth of approximation algorithms and heuristics. Although the best algorithm to use typically depends on the specific application domain, a… (More)

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