Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Xavier, Jo��o"'
Expectation Maximization (EM) is the standard method to learn Gaussian mixtures. Yet its classic, centralized form is often infeasible, due to privacy concerns and computational and communication bottlenecks. Prior work dealt with data distributed by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8e9546c5b1291e0b387fd3520f19dc73
First-order methods for stochastic optimization have undeniable relevance, in part due to their pivotal role in machine learning. Variance reduction for these algorithms has become an important research topic. In contrast to common approaches, which
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c2219d245f9de2838c29471dfe80b914
http://arxiv.org/abs/2109.03207
http://arxiv.org/abs/2109.03207
In recent work, we proposed a distributed Banach-Picard iteration (DBPI) that allows a set of agents, linked by a communication network, to find a fixed point of a locally contractive (LC) map that is the average of individual maps held by said agent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b4a60a00a77366544bba330c854e250
The Banach-Picard iteration is widely used to find fixed points of locally contractive (LC) maps. This paper extends the Banach-Picard iteration to distributed settings; specifically, we assume the map of which the fixed point is sought to be the ave
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24c5c92ae9e0fa02e1bc2c5a27ce34d5
Autor:
Almeida, In��s, Xavier, Jo��o
We present the Stochastic alternate Linearization Method (StochaLM), a token-based method for distributed optimization. This algorithm finds the solution of a consensus optimization problem by solving a sequence of subproblems where some components o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2bf58313205f157ea5d8a4004264dafe
We present an algorithm for the problem of linear distributed estimation of a parameter in a network where a set of agents are successively taking measurements. The approach considers a roaming token in a network that carries the estimate, and jumps
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10893e218c541e6aded48baaa86a14f0
http://arxiv.org/abs/1807.01533
http://arxiv.org/abs/1807.01533
Autor:
Almeida, In��s, Xavier, Jo��o
Processing data collected by a network of agents often boils down to solving an optimization problem. The distributed nature of these problems calls for methods that are, themselves, distributed. While most collaborative learning problems require age
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::19822d74cede574319907cbb0050c692
Autor:
Sim��es, Ant��nio, Xavier, Jo��o
Consider a set of agents that wish to estimate a vector of parameters of their mutual interest. For this estimation goal, agents can sense and communicate. When sensing, an agent measures (in additive gaussian noise) linear combinations of the unknow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6fdc54a625047a61dc8b411e7e60eb20
We address the sensor network localization problem given noisy range measurements between pairs of nodes. We approach the non-convex maximum-likelihood formulation via a known simple convex relaxation. We exploit its favorable optimization properties
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4d5604252f3abd7b0125870e13b5560d
We give a general proof of convergence for the Alternating Direction Method of Multipliers (ADMM). ADMM is an optimization algorithm that has recently become very popular due to its capabilities to solve large-scale and/or distributed problems. We pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::762a1411995ce07923926648593293b4
http://arxiv.org/abs/1112.2295
http://arxiv.org/abs/1112.2295