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Modern technologies are generating ever-increasing amounts of data. Making use of these data requires methods that are both statistically sound and computationally efficient. Typically, the statistical and computational aspects are treated…

Methodology · Statistics 2022-09-15 Mahsa Taheri , Néhémy Lim , Johannes Lederer

Distributed algorithms and theories are called for in this era of big data. Under weaker local signal-to-noise ratios, we improve upon the celebrated one-round distributed principal component analysis (PCA) algorithm designed in the spirit…

Methodology · Statistics 2025-07-01 ZeYu Li , Xinsheng Zhang , Wang Zhou

Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of real-world testing is prohibitive, we propose a…

Robotics · Computer Science 2023-09-18 Charles Dawson , Chuchu Fan

It is often convenient to separate a state estimation task into smaller "local" tasks, where each local estimator estimates a subset of the overall system state. However, neglecting cross-covariance terms between state estimates can result…

Robotics · Computer Science 2023-02-14 Mohammed Shalaby , Charles Champagne Cossette , Jerome Le Ny , James Richard Forbes

We propose a restricted win probability estimand for comparing treatments in a randomized trial with a time-to-event outcome. We also propose Bayesian estimators for this summary measure as well as the unrestricted win probability. Bayesian…

Methodology · Statistics 2024-11-06 Michelle Leeberg , Xianghua Luo , Thomas A. Murray

Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems. In general, this has a cubic cost in dataset size and is sensitive to…

Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional input parameters and/or an intractable likelihood. Performing Bayesian parameter inference in this context can be challenging. We present a neural…

Machine Learning · Statistics 2021-10-27 Benjamin Kurt Miller , Alex Cole , Patrick Forré , Gilles Louppe , Christoph Weniger

Operations research practitioners frequently want to model complicated functions that are are difficult to encode in their underlying optimisation framework. A common approach is to solve an approximate model, and to use a simulation to…

Optimization and Control · Mathematics 2022-07-06 Michael Forbes , Mitchell Harris , Marijn Jansen , Femke van der Schoot , Thomas Taimre

In randomized benchmarking of quantum logical gates, partial twirling can be used for simpler implementation, better scaling, and higher accuracy and reliability. For instance, for two-qubit gates, single-qubit twirling is easier to realize…

Quantum Physics · Physics 2022-06-22 Kirill Dubovitskii , Yuriy Makhlin

Analysis of parliamentary speeches and political-party manifestos has become an integral area of computational study of political texts. While speeches have been overwhelmingly analysed using unsupervised methods, a large corpus of…

Computation and Language · Computer Science 2025-03-11 Dmitry Nikolaev , Sean Papay

In recent decades, state legislatures have often drawn U.S. Congressional voting districts that look---to the human eye---to be rather twisted. In this paper, we propose a method to measure how much districts "meander" via a computation of…

History and Overview · Mathematics 2018-05-23 Eion Blanchard , Kevin Knudson

The paper illustrates an application of the Resampling approach [2] for the estimation of the aircraft circulation plan reliability. Resampling is an intensive computer statistical method, which can be used effectively in the case of small…

Applications · Statistics 2013-05-14 Maxim Fioshin

Gerrymandering, the deliberate manipulation of electoral district boundaries for political advantage, is a persistent issue in U.S. redistricting cycles. This paper introduces and analyzes a new phenomenon, 'votemandering'- a strategic…

Computer Science and Game Theory · Computer Science 2023-08-17 Sanyukta Deshpande , Ian G Ludden , Sheldon H Jacobson

The standard mathematical approach to fourth-down decision making in American football is to make the decision that maximizes estimated win probability. Win probability estimates arise from machine learning models fit from historical data.…

Applications · Statistics 2025-02-03 Ryan S. Brill , Ronald Yurko , Abraham J. Wyner

Clustering is the technique to partition data according to their characteristics. Data that are similar in nature belong to the same cluster [1]. There are two types of evaluation methods to evaluate clustering quality. One is an external…

Machine Learning · Computer Science 2024-09-05 Anupriya Vysala , Joseph Gomes

We analyze how numerical experiments regarding elections were conducted within the computational social choice literature (focusing on papers published in the IJCAI, AAAI, and AAMAS conferences). We analyze the sizes of the studied…

Risk-limiting audits (RLAs) are techniques for verifying the outcomes of large elections. While they provide rigorous guarantees of correctness, widespread adoption has been impeded by both efficiency concerns and the fact they offer…

Cryptography and Security · Computer Science 2024-06-19 Benjamin Fuller , Rashmi Pai , Alexander Russell

This article introduces a concept and measure of graph compartmentalization. This new measure allows for principled comparison between graphs of arbitrary structure, unlike existing measures such as graph modularity. The proposed measure is…

Social and Information Networks · Computer Science 2014-08-29 Matthew J. Denny

We developed a method for measuring the similarity between materials, focusing on specific physical properties. The obtained information can be utilized to understand the underlying mechanisms and to support the prediction of the physical…

Democratic societies increasingly rely on communication networks to aggregate citizen preferences and information, yet these same networks can systematically mislead voters under certain conditions. We introduce an agent-based model that…

Physics and Society · Physics 2025-06-30 Hsuan-Wei Lee , Po-Kang Hsiao