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The Price of Distributed: Rate Loss in the CEO Problem

Related publications (31)

AI-driven Prices for Externalities and Sustainability in Production Markets

Boi Faltings, Panayiotis Danassis, Aris Filos Ratsikas

Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone) ...

Cornell University2023

Hypergraph-Based Fast Distributed AC Power Flow Optimization

Colin Neil Jones, Yingzhao Lian, Yuning Jiang, Xinliang Dai

This paper presents a novel distributed approach for solving AC power flow (PF) problems. The optimization problem is reformulated into a distributed form using a communication structure corresponding to a hypergraph, by which complex relationships between ...

IEEE2023

Optimizing Sales Forecasting, Inventory, Pricing and Sourcing Decisions

Yara Kayyali El Alem

In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

EPFL2023

Resource aware pricing for electric vehicle charging

Nils Gustav Nilsson

Electric vehicle charging facilities offer their capacity constrained electric charge and parking to users for a fee. As electric vehicle adoption grows, so too does the potential for excessive resource utilization. In this paper, we study how prices set b ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Le Nguyen Hoang, John Stephan, Sadegh Farhadkhani, Nirupam Gupta, Rafaël Benjamin Pinot

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

Observation of non-reciprocal harmonic conversion in real sounds

Hervé Lissek, Romain Christophe Rémy Fleury, Xinxin Guo

Reciprocity guarantees that in most media, sound transmission is symmetric between two points of space when the location of the source and receiver are interchanged. This fundamental law can be broken in non-linear media, often at the cost of detrimental i ...

2023

Ultrabroadband sound control with deep-subwavelength plasmacoustic metalayers

Hervé Lissek, Romain Christophe Rémy Fleury, Stanislav Sergeev

Controlling audible sound requires inherently broadband and subwavelength acoustic solutions, which are to date, crucially missing. This includes current noise absorption methods, such as porous materials or acoustic resonators, which are typically ineffic ...

Nature Research2023

Analytical prediction of the final error due to noise in a ϕ-OTDR system

Luc Thévenaz, Li Zhang, Malak Mohamed Hossameldeen Omar Mohamed Galal, Suneetha Sebastian, Yuting Yang

An analytical expression to estimate the uncertainty on the frequency shift in ϕ-OTDR systems is established and confirmed by actual data. ...

Optica Publishing Group2022

An Efficient Signal-to-noise Approximation for Eccentric Inspiraling Binaries

Alexandra Shelest

Eccentricity has emerged as a potentially useful tool for helping to identify the origin of black hole mergers. However, eccentric templates can be computationally very expensive owing to the large number of harmonics, making statistical analyses to distin ...

IOP Publishing Ltd2022

A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers

Alcherio Martinoli, Cyrill Silvan Baumann

The automatic design of well-performing robotic controllers is still an unsolved problem due to the inherently large parameter space and noisy, often hard-to-define performance metrics, especially when sequential tasks need to be accomplished. Distal contr ...

2022

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models

Volkan Cevher, Paul Thierry Yves Rolland

This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal disc ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022