English
Related papers

Related papers: Regularized Dikin Walks for Sampling Truncated Log…

200 papers

Via a Dirichlet form extension theorem and making full use of two-sided heat kernel estimates, we establish quenched invariance principles for random walks in random environments with a boundary. In particular, we prove that the random walk…

Probability · Mathematics 2015-09-10 Zhen-Qing Chen , David A. Croydon , Takashi Kumagai

We introduce Reflective Hamiltonian Monte Carlo (ReHMC), an HMC-based algorithm, to sample from a log-concave distribution restricted to a convex body. We prove that, starting from a warm start, the walk mixes to a log-concave target…

Machine Learning · Computer Science 2023-03-30 Apostolos Chalkis , Vissarion Fisikopoulos , Marios Papachristou , Elias Tsigaridas

We give refined estimates for the discrete time and continuous time versions of some basic random walks on the symmetric and alternating groups $S_n$ and $A_n$. We consider the following models: random transposition, transpose top with…

Probability · Mathematics 2008-09-04 L. Saloff-Coste , J. Zuniga

Random walk algorithms are crucial for sampling and approximation problems in statistical physics and theoretical computer science. The mixing property is necessary for Markov chains to approach stationary distributions and is facilitated…

Quantum Physics · Physics 2024-12-02 Shyam Dhamapurkar , Yuhang Dang , Saniya Wagh , Xiu-Hao Deng

A key task in Bayesian statistics is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). However, without any assumptions, sampling (even approximately) can be #P-hard, and few…

Machine Learning · Computer Science 2018-12-03 Rong Ge , Holden Lee , Andrej Risteski

In this paper we study random walks on dynamical random environments in $1 + 1$ dimensions. Assuming that the environment is invariant under space-time shifts and fulfills a mild mixing hypothesis, we establish a law of large numbers and a…

Probability · Mathematics 2018-05-25 Oriane Blondel , Marcelo R. Hilario , Augusto Teixeira

It is natural to expect that nonbacktracking random walk will mix faster than simple random walks, but so far this has only been proved in regular graphs. To analyze typical irregular graphs, let $G$ be a random graph on $n$ vertices with…

Probability · Mathematics 2018-05-07 Anna Ben-Hamou , Eyal Lubetzky , Yuval Peres

Search patterns of randomly oriented steps of different lengths have been observed on all scales of the biological world, ranging from the microscopic to the ecological, including in protein motors, bacteria, T-cells, honeybees, marine…

Quantitative Methods · Quantitative Biology 2020-02-14 Brieuc Guinard , Amos Korman

Counting integer solutions of linear constraints has found interesting applications in various fields. It is equivalent to the problem of counting lattice points inside a polytope. However, state-of-the-art algorithms for this problem…

Data Structures and Algorithms · Computer Science 2023-12-15 Cunjing Ge

We prove that a uniformized variant of both the Rosenthal walk \cite{Rosenthal} and the Kac random walk \cite{Kac} on SO(n) mixes in $\cO(n^3)$ steps in total variation distance. The proof also extends easily to Rosenthal walk with fixed…

Probability · Mathematics 2011-10-26 Yunjiang Jiang

By viewing the $N$-simplex as the set of positions of $N-1$ ordered particles on the unit interval, the adjacent walk is the continuous time Markov chain obtained by updating independently at rate 1 the position of each particle with a…

Probability · Mathematics 2020-11-16 Pietro Caputo , Cyril Labbé , Hubert Lacoin

Performing random walks in networks is a fundamental primitive that has found applications in many areas of computer science, including distributed computing. In this paper, we focus on the problem of sampling random walks efficiently in a…

Distributed, Parallel, and Cluster Computing · Computer Science 2013-02-20 Atish Das Sarma , Danupon Nanongkai , Gopal Pandurangan , Prasad Tetali

In large-data applications, such as the inference process of diffusion models, it is desirable to design sampling algorithms with a high degree of parallelization. In this work, we study the adaptive complexity of sampling, which is the…

Data Structures and Algorithms · Computer Science 2025-05-21 Huanjian Zhou , Baoxiang Wang , Masashi Sugiyama

We show that the gradient norm $\|\nabla f(x)\|$ for $x \sim \exp(-f(x))$, where $f$ is strongly convex and smooth, concentrates tightly around its mean. This removes a barrier in the prior state-of-the-art analysis for the well-studied…

Machine Learning · Computer Science 2020-06-16 Yin Tat Lee , Ruoqi Shen , Kevin Tian

We consider nearest neighbor weighted random walks on the $d$-dimensional box $[n]^d$ that are governed by some function $g:[0,1] \ra [0,\iy)$, by which we mean that standing at $x$, a neighbor $y$ of $x$ is picked at random and the walk…

Probability · Mathematics 2021-01-26 Johan Jonasson , Måns Magnusson

We consider a one-dimensional simple random walk surviving among a field of static soft traps : each time it meets a trap the walk is killed with probability 1--e --$\beta$ , where $\beta$ is a positive and fixed parameter. The positions of…

Probability · Mathematics 2018-10-02 Julien Poisat , François Simenhaus

This thesis is devoted to the study of extreme value statistics in stochastic processes and their applications. In the first part, we obtain exact analytical results on the extreme value statistics of both discrete-time and continuous-time…

Statistical Mechanics · Physics 2023-10-24 Benjamin De Bruyne

We consider dynamical percolation on the $d$-dimensional discrete torus of side length $n$, $\mathbb{Z}_n^d$, where each edge refreshes its status at rate $\mu=\mu_n\le 1/2$ to be open with probability $p$. We study random walk on the…

Probability · Mathematics 2017-07-25 Yuval Peres , Perla Sousi , Jeffrey E. Steif

We introduce a theoretical framework for sampling from unnormalized densities based on a smoothing scheme that uses an isotropic Gaussian kernel with a single fixed noise scale. We prove one can decompose sampling from a density (minimal…

Machine Learning · Statistics 2023-10-02 Saeed Saremi , Ji Won Park , Francis Bach

We study the problem of "isotropically rounding" a polytope $K\subset\mathbb{R}^n$, that is, computing a linear transformation which makes the uniform distribution on the polytope have roughly identity covariance matrix. We assume $K$ is…

Data Structures and Algorithms · Computer Science 2019-09-17 Oren Mangoubi , Nisheeth K. Vishnoi