Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Shen Zebang"'
We establish the Poincar\'e inequality (PI) for a class of Gibbs measures of the form $\mu_\epsilon \propto \exp(-V/\epsilon)$, where the potential $V$ satisfies a local Polyak-{\L}ojasiewicz (PL) inequality, and its set of local minima is \emph{conn
Externí odkaz:
http://arxiv.org/abs/2501.00429
Publikováno v:
Dianzi Jishu Yingyong, Vol 45, Iss 6, Pp 28-36 (2019)
With the rapid development of automotive electronics and advanced assisted driving technology, autonomous driving as an advanced stage of assisted driving technology has become an important way to solve traffic travel in the future, and has become a
Externí odkaz:
https://doaj.org/article/ade7acd1bee84e05a2eef183ffb17a37
Designing incentives for an adapting population is a ubiquitous problem in a wide array of economic applications and beyond. In this work, we study how to design additional rewards to steer multi-agent systems towards desired policies \emph{without}
Externí odkaz:
http://arxiv.org/abs/2407.10207
Continuous-time approximation of Stochastic Gradient Descent (SGD) is a crucial tool to study its escaping behaviors from stationary points. However, existing stochastic differential equation (SDE) models fail to fully capture these behaviors, even f
Externí odkaz:
http://arxiv.org/abs/2405.18373
Repeated parameter sharing in federated learning causes significant information leakage about private data, thus defeating its main purpose: data privacy. Mitigating the risk of this information leakage, using state of the art differentially private
Externí odkaz:
http://arxiv.org/abs/2309.05505
Nowadays, algorithms with fast convergence, small memory footprints, and low per-iteration complexity are particularly favorable for artificial intelligence applications. In this paper, we propose a doubly stochastic algorithm with a novel accelerati
Externí odkaz:
http://arxiv.org/abs/2304.11665
Autor:
Shen, Zebang, Wang, Zhenfu
We extend the concept of self-consistency for the Fokker-Planck equation (FPE) to the more general McKean-Vlasov equation (MVE). While FPE describes the macroscopic behavior of particles under drift and diffusion, MVE accounts for the additional inte
Externí odkaz:
http://arxiv.org/abs/2303.11205
Federated Learning is an emerging learning paradigm that allows training models from samples distributed across a large network of clients while respecting privacy and communication restrictions. Despite its success, federated learning faces several
Externí odkaz:
http://arxiv.org/abs/2206.02078
The Fokker-Planck equation (FPE) is the partial differential equation that governs the density evolution of the It\^o process and is of great importance to the literature of statistical physics and machine learning. The FPE can be regarded as a conti
Externí odkaz:
http://arxiv.org/abs/2206.00860
Minimax problems arise in a wide range of important applications including robust adversarial learning and Generative Adversarial Network (GAN) training. Recently, algorithms for minimax problems in the Federated Learning (FL) paradigm have received
Externí odkaz:
http://arxiv.org/abs/2105.14216