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Related papers: Partition Decomposition for Roll Call Data

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The dominant paradigm in computational materials discovery relies on heavily parameterized deep architectures, including message-passing graph networks and equivariant models, that require millions of DFT-labeled training structures and…

Materials Science · Physics 2026-05-19 Pranoy Ray , Surya R. Kalidindi

The Hotelling-Downs model considers parties changing policy to maximise their vote-share. Where policy position lies on a left-right axis, it describes a tendency for political parties to move towards centrist platforms. This is in contrast…

Physics and Society · Physics 2026-05-12 Christopher Campbell , Graeme J. Ackland

Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-06-11 Salman Salloum , Yulin He , Joshua Zhexue Huang , Xiaoliang Zhang , Tamer Z. Emara , Chenghao Wei , Heping He

We consider a two-round election model involving $m$ voters and $n$ candidates. Each voter is endowed with a strict preference list ranking the candidates. In the first round, the candidates are partitioned into two subsets, $A$ and $B$,…

Computer Science and Game Theory · Computer Science 2026-03-17 Emilio De Santis , Antonio Di Crescenzo , Verdiana Mustaro

Recently developed theoretical framework for analysis of structured population dynamics in the spaces of nonnegative Radon measures with a suitable metric provides a rigorous tool to study numerical schemes based on particle methods. The…

Analysis of PDEs · Mathematics 2013-09-11 P. Gwiazda , J. Jabłoński , A. Marciniak-Czochra , A. Ulikowska

Data collection has become an increasingly important problem in robotic manipulation, yet there still lacks much understanding of how to effectively collect data to facilitate broad generalization. Recent works on large-scale robotic data…

Robotics · Computer Science 2024-05-22 Jensen Gao , Annie Xie , Ted Xiao , Chelsea Finn , Dorsa Sadigh

Anticipating and recognizing surgical workflows are critical for intelligent surgical assistance systems. However, existing methods rely on deterministic decision-making, struggling to generalize across the large anatomical and procedural…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Kaixiang Yang , Xin Li , Qiang Li , Zhiwei Wang

This paper establishes a precise high-dimensional asymptotic theory for boosting on separable data, taking statistical and computational perspectives. We consider a high-dimensional setting where the number of features (weak learners) $p$…

Statistics Theory · Mathematics 2022-11-21 Tengyuan Liang , Pragya Sur

We describe and analyze algorithms for shape-constrained symbolic regression, which allows the inclusion of prior knowledge about the shape of the regression function. This is relevant in many areas of engineering -- in particular whenever…

Neural and Evolutionary Computing · Computer Science 2021-07-21 Christian Haider , Fabricio Olivetti de França , Bogdan Burlacu , Gabriel Kronberger

In this article the most fundamental decomposition-based optimization method - block coordinate search, based on the sequential decomposition of problems in subproblems - and building performance simulation programs are used to reason about…

Optimization and Control · Mathematics 2016-09-12 Gian Luca Brunetti

Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state variables (a few equations), a trait that reduces complexity and facilitates exploration of behavioral…

In this paper, we propose a novel distributed data-driven optimization scheme. In detail, we focus on the so-called aggregative framework, a scenario in which a set of agents aim to cooperatively minimize the sum of local costs, each…

Optimization and Control · Mathematics 2026-01-27 Riccardo Brumali , Guido Carnevale , Giuseppe Notarstefano

This thesis develops exact analytical tools to study strongly correlated stochastic systems, with a focus on extreme value statistics, gap statistics, and full counting statistics in multi-particle processes. A central contribution is the…

Statistical Mechanics · Physics 2025-08-19 Marco Biroli

The autonomous systems need to decide how to react to the changes at runtime efficiently. The ability to rigorously analyze the environment and the system together is theoretically possible by the model-driven approaches; however, the model…

Software Engineering · Computer Science 2021-10-28 Melika Dastranj , Mehran Alidoost Nia , Mehdi Kargahi

A key step in reverse engineering neural networks is to decompose them into simpler parts that can be studied in relative isolation. Linear parameter decomposition -- a framework that has been proposed to resolve several issues with current…

Machine Learning · Computer Science 2025-09-05 Lucius Bushnaq , Dan Braun , Lee Sharkey

Analyzing signals arising from dynamical systems typically requires many modeling assumptions and parameter estimation. In high dimensions, this modeling is particularly difficult due to the "curse of dimensionality". In this paper, we…

Systems and Control · Computer Science 2016-12-21 Tal Shnitzer , Ronen Talmon , Jean-Jacques Slotine

Dirichlet Process Mixture (DPM) models have been increasingly employed to specify random partition models that take into account possible patterns within the covariates. Furthermore, to deal with large numbers of covariates, methods for…

Applications · Statistics 2016-11-01 William Barcella , Maria De Iorio , Gianluca Baio

The roll calls of the Italian Parliament in the XVI legislature are studied by employing multidimensional scaling, hierarchical clustering, and network analysis. In order to detect changes in voting behavior, the roll calls have been…

Social and Information Networks · Computer Science 2015-08-07 A. Amelio , C. Pizzuti

Policy learning using historical observational data is an important problem that has found widespread applications. Examples include selecting offers, prices, advertisements to send to customers, as well as selecting which medication to…

Machine Learning · Computer Science 2023-09-13 Nian Si , Fan Zhang , Zhengyuan Zhou , Jose Blanchet

This paper is about how to partition decision variables while decomposing a large-scale optimization problem for the best performance of distributed solution methods. Solving a large-scale optimization problem sequen- tially can be…

Optimization and Control · Mathematics 2017-10-26 Yuchen Zheng , Ilbin Lee , Nicoleta Serban
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