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The dramatic growth of big datasets presents a new challenge to data storage and analysis. Data reduction, or subsampling, that extracts useful information from datasets is a crucial step in big data analysis. We propose an orthogonal…

Methodology · Statistics 2021-06-01 Lin Wang , Jake Elmstedt , Weng Kee Wong , Hongquan Xu

Efficient spatial reasoning requires world models that remain reliable under tight precision budgets. We study whether low-bit planning behavior is determined mostly by total bitwidth or by where bits are allocated across modules. Using…

Machine Learning · Computer Science 2026-02-13 Suraj Ranganath , Anish Patnaik , Vaishak Menon

In this paper, we consider the problem of partitioning a small data sample of size $n$ drawn from a mixture of $2$ sub-gaussian distributions. Our work is motivated by the application of clustering individuals according to their population…

Statistics Theory · Mathematics 2023-01-05 Shuheng Zhou

Recently, we presented a new Two-Bar Charts Packing Problem (2-BCPP), in which it is necessary to pack two-bar charts (2-BCs) in a unit-height strip of minimum length. The problem is a generalization of the Bin Packing Problem and 2-D…

Computational Geometry · Computer Science 2021-01-05 Adil Erzin , Vladimir Shenmaier

This paper studies empirical risk minimization (ERM) problems for large-scale datasets and incorporates the idea of adaptive sample size methods to improve the guaranteed convergence bounds for first-order stochastic and deterministic…

Machine Learning · Computer Science 2017-09-05 Aryan Mokhtari , Alejandro Ribeiro

We give a simple and efficient algorithm for adaptively counting inversions in a sequence of $n$ integers. Our algorithm runs in $O(n + n \sqrt{\lg{(Inv/n)}})$ time in the word-RAM model of computation, where $Inv$ is the number of…

Data Structures and Algorithms · Computer Science 2015-03-05 Amr Elmasry

A major application for atomic ensembles consists of a quantum memory for light, in which an optical state can be reversibly converted to a collective atomic excitation on demand. There exists a well-known fundamental bound on the storage…

This paper presents a multiscale approach to efficiently compute approximate optimal transport plans between point sets. It is particularly well-suited for point sets that are in high-dimensions, but are close to being intrinsically…

Machine Learning · Computer Science 2021-04-13 Samuel Gerber , Mauro Maggioni

Let A={a_1,a_2,...,a_s} and {b_1,b_2,...,b_t} with s+r=n, the many to many point matching with demands and capacities matches each point a_i in A to at least alpha_i and at most alpha_i points in B, and each point b_j in B to at least…

Data Structures and Algorithms · Computer Science 2013-02-19 Fatemeh Rajabi-Alni

Span programs are a model of computation that have been used to design quantum algorithms, mainly in the query model. For any decision problem, there exists a span program that leads to an algorithm with optimal quantum query complexity,…

Quantum Physics · Physics 2015-07-03 Tsuyoshi Ito , Stacey Jeffery

The "Subset Sum problem" is a very well-known NP-complete problem. In this work, a top-k variation of the "Subset Sum problem" is considered. This problem has wide application in recommendation systems, where instead of k best objects the k…

Data Structures and Algorithms · Computer Science 2021-08-27 Biswajit Sanyal , Subhashis Majumder , Priya Ranjan Sinha Mahapatra

Active learning is a subfield of machine learning, in which the learning algorithm is allowed to choose the data from which it learns. In some cases, it has been shown that active learning can yield an exponential gain in the number of…

Machine Learning · Computer Science 2020-12-22 Ori Kelner

We study high-dimensional two-sample mean comparison and address the curse of dimensionality through data-adaptive projections. Leveraging the low-dimensional and localized signal structures commonly seen in single-cell genomics data, our…

Methodology · Statistics 2025-06-12 Tianyu Zhang , Jing Lei , Kathryn Roeder

Successful applications of sparse models in computer vision and machine learning imply that in many real-world applications, high dimensional data is distributed in a union of low dimensional subspaces. Nevertheless, the underlying…

Computer Vision and Pattern Recognition · Computer Science 2014-04-22 Xiao Bian , Hamid Krim

We investigate how embedding dimension affects the emergence of an internal "world model" in a transformer trained with reinforcement learning to perform bubble-sort-style adjacent swaps. Models achieve high accuracy even with very small…

Machine Learning · Computer Science 2025-10-22 Brady Bhalla , Honglu Fan , Nancy Chen , Tony Yue YU

We show that any embedding of a planar graph can be encoded succinctly while efficiently answering a number of topological queries near-optimally. More precisely, we build on a succinct representation that encodes an embedding of $m$ edges…

Data Structures and Algorithms · Computer Science 2021-12-14 José Fuentes-Sepúlveda , Gonzalo Navarro , Diego Seco

We show how to store good approximations of probability distributions in small space.

Information Theory · Computer Science 2007-07-16 Travis Gagie

The theory of two projections is utilized to study two-component Gibbs samplers. Through this theory, previously intractable problems regarding the asymptotic variances of two-component Gibbs samplers are reduced to elementary matrix…

Statistics Theory · Mathematics 2024-03-11 Qian Qin

In this paper we propose and prove that cyclic quorum sets can efficiently manage all-pairs computations and data replication. The quorums are O(N/sqrt(P)) in size, up to 50% smaller than the dual N/sqrt(P) array implementations, and…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-08-19 Cory J. Kleinheksel , Arun K. Somani

Permutation and multipermutation codes in the Ulam metric have been suggested for use in non-volatile memory storage systems such as flash memory devices. In this paper we introduce a new method to calculate permutation sphere sizes in the…

Information Theory · Computer Science 2019-01-10 Justin Kong