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no code implementations • 4 Nov 2021 • Eric Neyman, Tim Roughgarden

We show that by averaging the experts' forecasts and then \emph{extremizing} the average by moving it away from the prior by a constant factor, the aggregator's performance guarantee is substantially better than is possible without knowledge of the prior.

no code implementations • 16 Feb 2021 • Nika Haghtalab, Tim Roughgarden, Abhishek Shetty

-Online discrepancy minimization: We consider the online Koml\'os problem, where the input is generated from an adaptive sequence of $\sigma$-smooth and isotropic distributions on the $\ell_2$ unit ball.

no code implementations • 18 Jan 2021 • Andrew Lewis-Pye, Tim Roughgarden

What differentiates Bitcoin from these previously studied protocols is that it operates in a permissionless setting, i. e. it is a protocol for establishing consensus over an unknown network of participants that anybody can join, with as many identities as they like in any role.

Distributed Computing Distributed, Parallel, and Cluster Computing

no code implementations • 26 Jul 2020 • Tim Roughgarden

One of the primary goals of the mathematical analysis of algorithms is to provide guidance about which algorithm is the "best" for solving a given computational problem.

no code implementations • NeurIPS 2020 • Nika Haghtalab, Tim Roughgarden, Abhishek Shetty

Practical and pervasive needs for robustness and privacy in algorithms have inspired the design of online adversarial and differentially private learning algorithms.

no code implementations • 3 Jul 2018 • Vaggos Chatziafratis, Tim Roughgarden, Joshua R. Wang

We prove that the evolution of weight vectors in online gradient descent can encode arbitrary polynomial-space computations, even in very simple learning settings.

no code implementations • 8 Jun 2018 • Tim Roughgarden, Joshua R. Wang

The goal is to design a computationally efficient online algorithm, which chooses a subset of $[n]$ at each time step as a function only of the past, such that the accumulated value of the chosen subsets is as close as possible to the maximum total value of a fixed subset in hindsight.

no code implementations • NeurIPS 2018 • Rad Niazadeh, Tim Roughgarden, Joshua R. Wang

Our main result is the first $\frac{1}{2}$-approximation algorithm for continuous submodular function maximization; this approximation factor of $\frac{1}{2}$ is the best possible for algorithms that only query the objective function at polynomially many points.

1 code implementation • 30 Apr 2018 • George Barmpalias, Neng Huang, Andrew Lewis-Pye, Angsheng Li, Xuechen Li, YiCheng Pan, Tim Roughgarden

We introduce the \emph{idemetric} property, which formalises the idea that most nodes in a graph have similar distances between them, and which turns out to be quite standard amongst small-world network models.

Social and Information Networks Discrete Mathematics

no code implementations • 26 Jul 2016 • Tim Roughgarden, Vasilis Syrgkanis, Eva Tardos

This survey outlines a general and modular theory for proving approximation guarantees for equilibria of auctions in complex settings.

no code implementations • 11 Apr 2016 • Jamie Morgenstern, Tim Roughgarden

We present a general framework for proving polynomial sample complexity bounds for the problem of learning from samples the best auction in a class of "simple" auctions.

no code implementations • NeurIPS 2015 • Jamie H. Morgenstern, Tim Roughgarden

This paper develops a general approach, rooted in statistical learning theory, to learning an approximately revenue-maximizing auction from data.

no code implementations • 23 Nov 2015 • Rishi Gupta, Tim Roughgarden

While there is a large literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of the problem.

no code implementations • 19 Sep 2014 • Amir Globerson, Tim Roughgarden, David Sontag, Cafer Yildirim

We show that the prospects for achieving low expected Hamming error depend on the structure of the graph $G$ in interesting ways.

no code implementations • 15 Feb 2014 • Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan Ullman

In this paper, we initiate the systematic study of solving linear programs under differential privacy.

no code implementations • NeurIPS 2013 • Tim Roughgarden, Michael Kearns

We consider a number of classical and new computational problems regarding marginal distributions, and inference in models specifying a full joint distribution.

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