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Motivated by a $2$-dimensional (unsupervised) image segmentation task whereby local regions of pixels are clustered via edge detection methods, a more general probabilistic mathematical framework is devised. Critical thresholds are…

Machine Learning · Computer Science 2021-09-07 Robert A. Murphy

Let $G$ be a group with a non-elementary action on a proper CAT(0) space $X$, and let $\mu$ be a measure on $G$ such that the random walk $(Z_n)_n$ generated by $\mu$ has finite second moment on $X$. Let $o$ be a basepoint in $X$, and…

Group Theory · Mathematics 2024-07-31 Corentin Le Bars

Trajectory planning tasks for non-holonomic or collaborative systems are naturally modeled by state spaces with non-Euclidean metrics. However, existing proofs of convergence for sample-based motion planners only consider the setting of…

Robotics · Computer Science 2023-06-29 Anton Lukyanenko , Damoon Soudbakhsh

The paper presents a novel learning-based sampling strategy that guarantees rejection-free sampling of the free space under both biased and approximately uniform conditions, leveraging multivariate kernel densities. Historical data from a…

Robotics · Computer Science 2025-05-15 Thomas T. Enevoldsen , Roberto Galeazzi

Over the last 20 years significant effort has been dedicated to the development of sampling-based motion planning algorithms such as the Rapidly-exploring Random Trees (RRT) and its asymptotically optimal version (e.g. RRT*). However,…

Robotics · Computer Science 2014-05-13 Georgios Papadopoulos , Hanna Kurniawati , Nicholas M. Patrikalakis

Suppose we are given access to $n$ independent samples from distribution $\mu$ and we wish to output one of them with the goal of making the output distributed as close as possible to a target distribution $\nu$. In this work we show that…

Machine Learning · Statistics 2024-02-27 Adam Block , Yury Polyanskiy

We prove a new lower bound on the critical density $\rho_c$ of the hard disk model, i.e., the density below which it is possible to efficiently sample random configurations of $n$ non-overlapping disks in a unit torus. We use a classic…

Computational Complexity · Computer Science 2014-07-09 Thomas P. Hayes , Cristopher Moore

The Gap-Hamming-Distance problem arose in the context of proving space lower bounds for a number of key problems in the data stream model. In this problem, Alice and Bob have to decide whether the Hamming distance between their $n$-bit…

Computational Complexity · Computer Science 2009-02-17 Joshua Brody , Amit Chakrabarti

We consider the problem of performing a random walk in a distributed network. Given bandwidth constraints, the goal of the problem is to minimize the number of rounds required to obtain a random walk sample. Das Sarma et al. [PODC'10] show…

Distributed, Parallel, and Cluster Computing · Computer Science 2011-10-18 Danupon Nanongkai , Atish Das Sarma , Gopal Pandurangan

In this paper, we present a new algorithm that extends RRT* and RT-RRT* for online path planning in complex, dynamic environments. Sampling-based approaches often perform poorly in environments with narrow passages, a feature common to many…

Robotics · Computer Science 2021-09-10 Daniel Armstrong , André Jonasson

In this paper, we consider the motion planning problem in Gaussian belief space for minimum sensing navigation. Despite the extensive use of sampling-based algorithms and their rigorous analysis in the deterministic setting, there has been…

Robotics · Computer Science 2023-06-02 Vrushabh Zinage , Ali Reza Pedram , Takashi Tanaka

It is often asserted in the literature that one should expect positive autocorrelation for random walk Metropolis-Hastings (RWMH), especially if the typical proposal step-size is small relative to the variability in the target density. In…

Probability · Mathematics 2026-01-28 James Allen Fill , Svante Janson

In the negative perceptron problem we are given $n$ data points $({\boldsymbol x}_i,y_i)$, where ${\boldsymbol x}_i$ is a $d$-dimensional vector and $y_i\in\{+1,-1\}$ is a binary label. The data are not linearly separable and hence we…

Machine Learning · Computer Science 2025-03-25 Andrea Montanari , Yiqiao Zhong , Kangjie Zhou

We first consider {\it deterministic} immersions of the $d$-dimensional sphere into high dimensional Euclidean spaces, where the immersion is via spherical harmonics of level $n$. The main result of the article is the, a priori unexpected,…

Probability · Mathematics 2019-08-06 Renjie Feng , Robert J. Adler

Many real-world networks exhibit the so-called small-world phenomenon: their typical distances are much smaller than their sizes. One mathematical model for this phenomenon is a long-range percolation graph on a $d$-dimensional box $\{0, 1,…

Probability · Mathematics 2022-11-30 Tianqi Wu

Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found, their performance can be dramatically improved by…

Robotics · Computer Science 2017-10-18 Daqing Yi , Rohan Thakker , Cole Gulino , Oren Salzman , Siddhartha Srinivasa

We study percolation on the hierarchical lattice of order $N$ where the probability of connection between two points separated by distance $k$ is of the form $c_k/N^{k(1+\delta)},\; \delta >-1$. Since the distance is an ultrametric, there…

Probability · Mathematics 2012-05-25 Donald Dawson , Luis Gorostiza

We consider percolation of the vacant set of random interlacements at intensity $u$ in dimensions three and higher, and derive lower bounds on the truncated two-point function for all values of $u>0$. These bounds are sharp up to principal…

Probability · Mathematics 2025-04-04 Subhajit Goswami , Pierre-François Rodriguez , Yuriy Shulzhenko

Finding asymptotically-optimal paths in multi-robot motion planning problems could be achieved, in principle, using sampling-based planners in the composite configuration space of all of the robots in the space. The dimensionality of this…

Multiagent Systems · Computer Science 2017-07-05 Andrew Dobson , Kiril Solovey , Rahul Shome , Dan Halperin , Kostas E. Bekris

We study the complexity of geometric problems on spaces of low fractal dimension. It was recently shown by [Sidiropoulos & Sridhar, SoCG 2017] that several problems admit improved solutions when the input is a pointset in Euclidean space…

Computational Complexity · Computer Science 2017-12-14 Anastasios Sidiropoulos , Kritika Singhal , Vijay Sridhar