Publications associées à E (nombre)

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Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Michael Christoph Gastpar, Alper Köse, Ahmet Arda Atalik

The conditional mean is a fundamental and important quantity whose applications include the theories of estimation and rate-distortion. It is also notoriously difficult to work with. This paper establishes novel bounds on the differential entropy of the co ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Local approximation of the Maximum Cut in regular graphs

Etienne Michel François Bamas

This paper is devoted to the distributed complexity of finding an approximation of the maximum cut (MAXCUT) in graphs. A classical algorithm consists in letting each vertex choose its side of the cut uniformly at random. This does not require any communica ...

ELSEVIER2020

Distributionally Robust Mechanism Design

Daniel Kuhn, Cagil Kocyigit, Wolfram Wiesemann

We study a mechanism design problem where an indivisible good is auctioned to multiple bidders, for each of whom it has a private value that is unknown to the seller and the other bidders. The agents perceive the ensemble of all bidder values as a random v ...

2020

Local Approximation of the Maximum Cut in Regular Graphs

Etienne Michel François Bamas

This paper is devoted to the distributed complexity of finding an approximation of the maximum cut in graphs. A classical algorithm consists in letting each vertex choose its side of the cut uniformly at random. This does not require any communication and ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Hardness Results for Consensus-Halving

Jie Zhang, Aris Filos Ratsikas

The Consensus-halving problem is the problem of dividing an object into two portions, such that each of n agents has equal valuation for the two portions. We study the epsilon-approximate version, which allows each agent to have an epsilon discrepancy on t ...

Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik2018

Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

Jean-Baptiste Francis Marie Juliette Cordonnier

The interest for distributed stochastic optimization has raised to train complex Machine Learning models with more data on distributed systems. Increasing the computation power speeds up the training but it faces a communication bottleneck between workers ...

2018

Using Structural Properties for Integer Programs

Kim-Manuel Klein

Integer programs (IPs) are one of the fundamental tools used to solve combinatorial problems in theory and practice. Understanding the structure of solutions of IPs is thus helpful to argue about the existence of solutions with a certain simple structure, ...

SPRINGER INTERNATIONAL PUBLISHING AG2018

A Framework for the Secretary Problem on the Intersection of Matroids

Ola Nils Anders Svensson, Moran Feldman, Rico Zenklusen

The secretary problem became one of the most prominent online selection problems due to its numerous applications in online mechanism design. The task is to select a maximum weight subset of elements subject to given constraints, where elements arrive one- ...

ASSOC COMPUTING MACHINERY2018

E (nombre)

Graph Chatbot

Chattez avec Graph Search

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Local approximation of the Maximum Cut in regular graphs

Distributionally Robust Mechanism Design

Local Approximation of the Maximum Cut in Regular Graphs

Hardness Results for Consensus-Halving

Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

Using Structural Properties for Integer Programs

A Framework for the Secretary Problem on the Intersection of Matroids

A New Framework for Distributed Submodular Maximization

Performance limits of single-agent and multi-agent sub-gradient stochastic learning

Distributionally Robust Mechanism Design

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Local approximation of the Maximum Cut in regular graphs

Local Approximation of the Maximum Cut in Regular Graphs

Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

A New Framework for Distributed Submodular Maximization

A Framework for the Secretary Problem on the Intersection of Matroids

Using Structural Properties for Integer Programs

Hardness Results for Consensus-Halving

Performance limits of single-agent and multi-agent sub-gradient stochastic learning