papers

Publications (23)

cs.DS2020

On the Approximability of Time Disjoint Walks

Alexandre Bayen, Jesse Goodman, Eugene Vinitsky

We introduce the combinatorial optimization problem Time Disjoint Walks (TDW), which has applications in collision-free routing of discrete objects (e.g., autonomous vehicles) over…

math.PR2008

Invasion percolation on regular trees

Omer Angel, Jesse Goodman, Frank den Hollander +1

We consider invasion percolation on a rooted regular tree. For the infinite cluster invaded from the root, we identify the scaling behavior of its -point function for any $r\geq…

stat.CO2026

A general framework for computation and estimation using the saddlepoint approximation

Godrick Oketch, Rachel M. Fewster, Jesse Goodman

The saddlepoint approximation provides highly accurate approximations to probability density and mass functions using only the corresponding moment generating functions (MGFs). Rec…

math.FA2025

An atomic decomposition of one-dimensional metric currents without boundary

You-Wei Benson Chen, Jesse Goodman, Felipe Hernandez +1

This paper proves an atomic decomposition of the space of -dimensional metric currents without boundary, in which the atoms are specified by closed Lipschitz curves with uniform…

cs.CC2025

Leakage-Resilient Extractors against Number-on-Forehead Protocols

Eshan Chattopadhyay, Jesse Goodman

Given a sequence of independent sources , how many of them must be good (i.e., contain some min-entropy) in order to…

stat.ME2025

What is the price of approximation? The saddlepoint approximation to a likelihood function

Godrick Oketch, Rachel M. Fewster, Jesse Goodman

The saddlepoint approximation to the likelihood, and its corresponding maximum likelihood estimate (MLE), offer an alternative estimation method when the true likelihood is intract…

math.AP2026

Potential Estimates and Hodge Systems with data on compact manifolds

Jesse Goodman, Felipe Hernández, Daniel Spector

In this paper we establish optimal Lorentz estimates for the Riesz potentials acting on closed or co-closed -forms of finite mass on a smooth, compact Riemannian manifold of dim…

cs.CC2022

Low-Degree Polynomials Extract from Local Sources

Omar Alrabiah, Eshan Chattopadhyay, Jesse Goodman +2

We continue a line of work on extracting random bits from weak sources that are generated by simple processes. We focus on the model of locally samplable sources, where each bit in…

cs.CC2024

Extractors for Polynomial Sources over

Eshan Chattopadhyay, Jesse Goodman, Mohit Gurumukhani

We explicitly construct the first nontrivial extractors for degree polynomial sources over . Our extractor requires min-entropy $k\geq n - \tildeΩ(\sqrt{…

math.PR2013

Scaling limit of the invasion percolation cluster on a regular tree

Omer Angel, Jesse Goodman, Mathieu Merle

We prove existence of the scaling limit of the invasion percolation cluster (IPC) on a regular tree. The limit is a random real tree with a single end. The contour and height funct…

math.PR2009

Exponential growth of ponds in invasion percolation on regular trees

Jesse Goodman

In invasion percolation, the edges of successively maximal weight (the outlets) divide the invasion cluster into a chain of ponds separated by outlets. On the regular tree, the pon…

cs.CC2024

Improved Condensers for Chor-Goldreich Sources

Jesse Goodman, Xin Li, David Zuckerman

One of the earliest models of weak randomness is the Chor-Goldreich (CG) source. A -CG source is a sequence of random variables , where…

stat.CO2017

Properties of the Affine Invariant Ensemble Sampler in high dimensions

David Huijser, Jesse Goodman, Brendon J. Brewer

We present theoretical and practical properties of the affine-invariant ensemble sampler Markov chain Monte Carlo method. In high dimensions the affine-invariant ensemble sampler s…

math.PR2012

Short paths for first passage percolation on the complete graph

Maren Eckhoff, Jesse Goodman, Remco van der Hofstad +1

We study the complete graph equipped with a topology induced by independent and identically distributed edge weights. The focus of our analysis is on the weight W_n and the number…

cs.CC2021

Improved Extractors for Small-Space Sources

Eshan Chattopadhyay, Jesse Goodman

We study the problem of extracting random bits from weak sources that are sampled by algorithms with limited memory. This model of small-space sources was introduced by Kamp, Rao,…

math.PR2012

Lectures on Self-Avoiding Walks

Roland Bauerschmidt, Hugo Duminil-Copin, Jesse Goodman +1

These lecture notes provide a rapid introduction to a number of rigorous results on self-avoiding walks, with emphasis on the critical behaviour. Following an introductory overview…

math.AP2024

Two Approximation Results for Divergence Free Measures

Jesse Goodman, Felipe Hernandez, Daniel Spector

In this paper we prove two approximation results for divergence free measures. The first is a form of an assertion of J. Bourgain and H. Brezis concerning the approximation of sole…

math.PR2013

Extremal geometry of a Brownian porous medium

Jesse Goodman, Frank den Hollander

The path W[0,t] of a Brownian motion on a d-dimensional torus T^d run for time t is a random compact subset of T^d. We study the geometric properties of the complement T^d \ W[0,t]…

math.PR2024

The saddlepoint approximation factors over sample paths of recursively compounded processes

Jesse Goodman

This paper presents an identity between the multivariate and univariate saddlepoint approximations applied to sample path probabilities for a certain class of stochastic processes.…

cs.CC2025

Low-Degree Polynomials Are Good Extractors

Omar Alrabiah, Jesse Goodman, Jonathan Mosheiff +1

We prove that random low-degree polynomials (over ) are unbiased, in an extremely general sense. That is, we show that random low-degree polynomials are good randomne…

math.ST2022

Asymptotic accuracy of the saddlepoint approximation for maximum likelihood estimation

Jesse Goodman

The saddlepoint approximation gives an approximation to the density of a random variable in terms of its moment generating function. When the underlying random variable is itself t…

math.PR2015

Degree distribution of shortest path trees and bias of network sampling algorithms

Shankar Bhamidi, Jesse Goodman, Remco van der Hofstad +1

In this article, we explicitly derive the limiting degree distribution of the shortest path tree from a single source on various random network models with edge weights. We determi…

math.AP2017

Some remarks on boundary operators of Bessel extensions

Jesse Goodman, Daniel Spector

In this paper we study some boundary operators of a class of Bessel-type Littlewood-Paley extensions whose prototype is \[Δ_x u(x,y) +\frac{1-2s}{y} \frac{\partial u}{\partial y}(…