Zobrazeno 1 - 10
of 196
pro vyhledávání: '"NOWICKI, TOMASZ"'
Monte Carlo matrix trace estimation is a popular randomized technique to estimate the trace of implicitly-defined matrices via averaging quadratic forms across several observations of a random vector. The most common approach to analyze the quality o
Externí odkaz:
http://arxiv.org/abs/2410.16455
Autor:
Lu, Yingdong, Nowicki, Tomasz
For the iterations of $x\mapsto |x-\theta|$ random functions with Lipschitz number one, we represent the dynamics as a Markov chain and prove its convergence under mild conditions. We also demonstrate that the Wasserstein metric of any two measures w
Externí odkaz:
http://arxiv.org/abs/2409.06003
This paper presents an extension of the classical Ulan-von Neumann Markov chain Monte-Carlo algorithm for the computation of the matrix inverse. The algorithm presented in this paper, termed as \emph{regenerative Ulam-von Neumann algorithm}, utilizes
Externí odkaz:
http://arxiv.org/abs/2407.16661
We study convergence rates of Hamiltonian Monte Carlo (HMC) algorithms with leapfrog integration under mild conditions on stochastic gradient oracle for the target distribution (SGHMC). Our method extends standard HMC by allowing the use of general a
Externí odkaz:
http://arxiv.org/abs/2405.13140
Autor:
Kalantzis, Vassilis, Squillante, Mark S., Ubaru, Shashanka, Gokmen, Tayfun, Wu, Chai Wah, Gupta, Anshul, Avron, Haim, Nowicki, Tomasz, Rasch, Malte, Onen, Murat, Marrero, Vanessa Lopez, Leobandung, Effendi, Kohda, Yasuteru, Haensch, Wilfried, Horesh, Lior
Numerical computation is essential to many areas of artificial intelligence (AI), whose computing demands continue to grow dramatically, yet their continued scaling is jeopardized by the slowdown in Moore's law. Multi-function multi-way analog (MFMWA
Externí odkaz:
http://arxiv.org/abs/2401.13754
The mean field variational inference (MFVI) formulation restricts the general Bayesian inference problem to the subspace of product measures. We present a framework to analyze MFVI algorithms, which is inspired by a similar development for general va
Externí odkaz:
http://arxiv.org/abs/2210.11385
We study the convergence of a random iterative sequence of a family of operators on infinite dimensional Hilbert spaces, inspired by the Stochastic Gradient Descent (SGD) algorithm in the case of the noiseless regression, as studied in [1]. We identi
Externí odkaz:
http://arxiv.org/abs/2202.02266
Autor:
Onen, Murat, Gokmen, Tayfun, Todorov, Teodor K., Nowicki, Tomasz, del Alamo, Jesus A., Rozen, John, Haensch, Wilfried, Kim, Seyoung
Analog crossbar arrays comprising programmable nonvolatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymmetric conductance modulation of practical resistive devices critically
Externí odkaz:
http://arxiv.org/abs/2201.13377
Existing rigorous convergence guarantees for the Hamiltonian Monte Carlo (HMC) algorithm use Gaussian auxiliary momentum variables, which are crucially symmetrically distributed. We present a novel convergence analysis for HMC utilizing new analytic
Externí odkaz:
http://arxiv.org/abs/2110.12907