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Related papers: Discrepancy Minimization via a Self-Balancing Walk

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We consider random walks in a uniformly elliptic, balanced, i.i.d. random environment in the integer lattice $Z^d$ for $d\geq 2$ and the corresponding problem of stochastic homogenization of non-divergence form difference operators. We…

Probability · Mathematics 2025-12-08 Xiaoqin Guo , Hung V. Tran

High-dimensional limit theorems have been shown useful to derive tuning rules for finding the optimal scaling in random-walk Metropolis algorithms. The assumptions under which weak convergence results are proved are however restrictive: the…

Methodology · Statistics 2022-02-16 Sebastian M Schmon , Philippe Gagnon

We consider the problem of robustly predicting as well as the best linear combination of $d$ given functions in least squares regression, and variants of this problem including constraints on the parameters of the linear combination. For…

Statistics Theory · Mathematics 2012-02-24 Jean-Yves Audibert , Olivier Catoni

This paper is about the construction of displacement interpolations on a discrete metric graph. Our approach is based on the approximation of any optimal transport problem whose cost function is a distance on a discrete graph by a sequence…

Metric Geometry · Mathematics 2022-09-05 Christian Léonard

We study the matrix discrepancy problem in the average-case setting. Given a sequence of $m \times m$ symmetric matrices $A_1,\ldots,A_n$, its discrepancy is defined as the minimal spectral norm over all signed sums $\sum_{i=1}^n x_iA_i$…

Probability · Mathematics 2025-10-07 Dmitriy Kunisky , Timm Oertel , Nicola Wengiel , Peiyuan Zhang

When maximum likelihood estimation is infeasible, one often turns to score matching, contrastive divergence, or minimum probability flow to obtain tractable parameter estimates. We provide a unifying perspective of these techniques as…

Statistics Theory · Mathematics 2022-10-07 Alessandro Barp , Francois-Xavier Briol , Andrew B. Duncan , Mark Girolami , Lester Mackey

Variational inequalities are a formalism that includes games, minimization, saddle point, and equilibrium problems as special cases. Methods for variational inequalities are therefore universal approaches for many applied tasks, including…

We systematically explore a class of constrained optimization problems with linear objective function and constraints that are linear combinations of logarithms of the optimization variables. Such problems can be viewed as a generalization…

Classical Analysis and ODEs · Mathematics 2021-01-01 Sergey Sadov

In this article, we study a branching random walk in an environment which depends on the time. This time-inhomogeneous environment consists of a sequence of macroscopic time intervals, in each of which the law of reproduction remains…

Probability · Mathematics 2017-06-13 Bastien Mallein

We prove the non-equilibrium fluctuations for the one-dimensional symmetric simple exclusion process with a slow bond. This generalizes a result of T. Franco, A. Neumann and P. Gon\c{c}alves (2013), which dealt with the equilibrium…

Probability · Mathematics 2018-09-13 Dirk Erhard , Tertuliano Franco , Patrícia Gonçalves , Adriana Neumann , Mariana Tavares

We derive logarithmic asymptotics of probabilities of small deviations for iterated processes in the space of trajectories. We find conditions under which these asymptotics coincide with those of processes generating iterated processes.…

Probability · Mathematics 2015-02-17 Andrei N. Frolov

The main result of this paper is a general central limit theorem for distributions defined by certain renewal type equations. We apply this to weakly self-avoiding random walks. We give good error estimates and Gaussian tail estimates which…

Probability · Mathematics 2007-05-23 Erwin Bolthausen , Christine Ritzmann

A key principle in assessing textual similarity is measuring the degree of semantic overlap between two texts by considering the word alignment. Such alignment-based approaches are intuitive and interpretable; however, they are empirically…

Computation and Language · Computer Science 2020-11-17 Sho Yokoi , Ryo Takahashi , Reina Akama , Jun Suzuki , Kentaro Inui

This report will be a literature review on a result in algorithmic discrepancy theory. We will begin by providing a quick overview on discrepancy theory and some major results in the field, and then focus on an important result by Shachar…

Data Structures and Algorithms · Computer Science 2019-11-19 Michael Whitmeyer , Jonathan Liu

Numerous problems of both theoretical and practical interest are related to finding shortest (or otherwise optimal) paths in networks, frequently in the presence of some obstacles or constraints. A somewhat related class of problems focuses…

Statistical Mechanics · Physics 2021-03-01 Ricardo Gutiérrez , Carlos Pérez-Espigares

We study an online linear optimization (OLO) problem in which the learner is provided access to $K$ "hint" vectors in each round prior to making a decision. In this setting, we devise an algorithm that obtains logarithmic regret whenever…

Machine Learning · Computer Science 2020-10-08 Aditya Bhaskara , Ashok Cutkosky , Ravi Kumar , Manish Purohit

The goal of this work is to fill a gap in [Yang, SIAM J. Matrix Anal. Appl, 41 (2020), 1797--1825]. In that work, an approximation procedure was proposed for orthogonal low-rank tensor approximation; however, the approximation lower bound…

Optimization and Control · Mathematics 2021-01-01 Yuning Yang

Consider the boundary case in a one-dimensional super-critical branching random walk. It is known that upon the survival of the system, the minimal position after $n$ steps behaves in probability like ${3\over 2} \log n$ when $n\to \infty$.…

Probability · Mathematics 2011-02-02 Elie Aidekon , Zhan Shi

Let B_n be the number of self-intersections of a symmetric random walk with finite second moments in the integer planar lattice. We obtain moderate deviation estimates for B_n - E B_n and E B_n- B_n, which are given in terms of the best…

Probability · Mathematics 2007-05-23 Richard F. Bass , Xia Chen , Jay Rosen

We consider the problem of forecasting multiple values of the future of a vector time series, using some past values. This problem, and related ones such as one-step-ahead prediction, have a very long history, and there are a number of…

Machine Learning · Statistics 2021-02-01 Shane Barratt , Yining Dong , Stephen Boyd