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Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
[article]
2018
arXiv
pre-print
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In this way, our work both contributes to the foundations of algorithm configuration and pushes the boundaries of learning theory, since the algorithm classes we analyze consist of multi-stage optimization ...
We address this problem for clustering, max-cut, and other partitioning problems, such as integer quadratic programming, by designing computationally efficient and sample efficient learning algorithms ...
We approach application-specific algorithm configuration via a learning-theoretic framework wherein an application domain is modeled as a distribution over problem instances. ...
arXiv:1611.04535v4
fatcat:tj5j3iyinvd6ffkbwyy3e6didm
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Our algorithm can help compile a configuration portfolio, or it can be used to select the input to a configuration algorithm for finite parameter spaces. ...
We provide an algorithm that learns a finite set of promising parameters from within an infinite set. ...
CCF-1733556, the ARO under award W911NF-17-1-0082, an Amazon Research Award, an AWS Machine Learning Research Award, and a Bloomberg Data Science research grant. ...
doi:10.1609/aaai.v34i04.5721
fatcat:4kliqd5hy5dtvmlidfyoeimc5i
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees
[article]
2020
arXiv
pre-print
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Our algorithm can help compile a configuration portfolio, or it can be used to select the input to a configuration algorithm for finite parameter spaces. ...
We provide an algorithm that learns a finite set of promising parameters from within an infinite set. ...
In this work, we develop the foundations of automated algorithm configuration via machine learning. ...
arXiv:1905.10819v3
fatcat:okbr5tep6nchrhav7nx57eiobm
Tropical Tensor Network for Ground States of Spin Glasses
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2008.06888v1 fatcat:27rwky76rjdydnfsmjeevxqmzu
Our approach provides baselines and benchmarks for exact algorithms for spin glasses and combinatorial optimization problems, and for evaluating heuristic algorithms and mean-field theories. ...
We present a unified exact tensor network approach to compute the ground state energy, identify the optimal configuration, and count the number of solutions for spin glasses. ...
Introduction-Combinatorial optimization problems have fundamental theoretical interests in statistical physics and computer science. ...
arXiv:2008.06888v2
fatcat:jtdiyori7bewfkxsex63ywdhyq
Backbone Analysis and Applications in Heuristic Algorithm Design
2011
ACTA AUTOMATICA SINICA
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A morphing procedure to supplement a simulated annealing heuristic for cost-and coverage-correlated set-covering problems. Annals of Operations Research, 1999, 86(1): 611−627 45 Boese K D. ...
The existing research of backbone consists of three areas, including the basic theoretical research focusing on investigation of the relationship between the backbone and computational complexity, the ...
Searching for backbones -a high-performance parallel algorithm for solving combinatorial optimization problems. ...
doi:10.3724/sp.j.1004.2011.00257
fatcat:dbvrdmoqynaulcpwrfiruateq4
Optimal Segmentation of Directed Graph and the Minimum Number of Feedback Arcs
2017
Journal of statistical physics
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2017 pre-print arXiv:1705.05946v1 fatcat:xad6kmhag5hqjerl7aye3gcery
The minimum feedback arc density of a given random digraph ensemble is then obtained by extrapolating the theoretical results to the limit of large D. ...
We solve the D-segmentation problem by the replica-symmetric mean field theory and belief-propagation heuristic algorithms. ...
One of the authors (HJZ) acknowledges the hospitality of the Asia Pacific Center for Theoretical Physics (APCTP, Pohang, Korea) where the theoretical part of this project was carried out during a short ...
doi:10.1007/s10955-017-1860-5
fatcat:4ybx4jvs3zfnrg5cysvgjdmrtq
Bayesian statistical learning for big data biology
2019
Biophysical Reviews
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We then describe the use of Bayesian learning in single-cell biology for the analysis of high-dimensional, large data sets. ...
This review describes the theoretical foundations underlying Bayesian statistics and outlines the computational frameworks for implementing Bayesian inference in practice. ...
Direct computation is typically intractable, due to the curse of dimensionality for any problem of even moderate dimensionality, which results in a combinatorial explosion in the number of configurations ...
doi:10.1007/s12551-019-00499-1
pmid:30729409
pmcid:PMC6381359
fatcat:26l5tkuawrarbni4vc6srasvd4
Privacy Preserving Linear Programming
[article]
2016
arXiv
pre-print
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In summary, we propose a set of efficient and secure transformation based techniques that create significant value-added benefits of being independent of the specific algorithms used for solving the linear ...
Finally, we extend the technique for securely solving two-party arbitrarily partitioned linear programming problems to a multi-party scenario. ...
Acknowledgments This work is partially supported by the National Science Foundation under Grants No. CNS-0746943 and CNS-1618221. ...
arXiv:1610.02339v1
fatcat:spgrdyeeyreyxh5ik6o5bkaafa
Toward IoT-Friendly Learning Models
2018
2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)
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We argue that knowledge of the composite nature of the learning process, as well as of the adversarial character of the relationship among phases, can help in developing heuristics for improving the learning ...
We demonstrate the application of this principle, by a multiple kernel learning approach, based on the exploration of the partition lattice driven by the natural partitioning of the feature set. ...
Choosing a kernel is itself a problem; one can explore the combinatorial space of dimensions and assess results by crossvalidation. ...
doi:10.1109/icdcs.2018.00128
dblp:conf/icdcs/DamianiGCM18
fatcat:3vym7eppizfc3m4ibephvclafy
From Relational Data to Graphs: Inferring Significant Links Using Generalized Hypergeometric Ensembles
[chapter]
2017
Lecture Notes in Computer Science
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2017 pre-print arXiv:1706.04370v1 fatcat:fvzgcdojcnfzbga6ybshyyraeqOther Versions
The inference of network topologies from relational data is an important problem in data analysis. ...
Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships ...
We thus argue that our work advances the theoretical foundation for the mining of relational data on social systems. ...
doi:10.1007/978-3-319-67256-4_11
fatcat:tdxkugui2zgkfjjgxvdo5athni
Quantum annealing: next-generation computation and how to implement it when information is missing
2018
Nonlinear Theory and Its Applications IEICE
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Recently, several powerful machines dedicated to solving combinatorial optimization problems through the Ising-model formulation have appeared. ...
Furthermore, we analyze the theoretical limitations of our proposed method by employing the replica method, which is a sophisticated tool in statistical mechanics. ...
Acknowledgments One of the authors (M. O.) was partially supported by Inamori Foundation, KAKENHI Nos. 15H03699, 16H04832, and 16K13849, and JST-START. One of the authors (C. ...
doi:10.1587/nolta.9.392
fatcat:5aps5timqne4vn6xejns5cry6y
Local Graph Partitions for Approximation and Testing
2009
2009 50th Annual IEEE Symposium on Foundations of Computer Science
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We introduce a new tool for approximation and testing algorithms called partitioning oracles. ...
For instance: • We give constant-time approximation algorithms for the size of the minimum vertex cover, the minimum dominating set, and the maximum independent set for any class of graphs with an excluded ...
Avinatan and Krzysztof thank Erik Demain for an inspiring early discussion. Avinatan thanks Noga Alon for inspiring discussions. Krzysztof thanks Fabian Kuhn for pointing out [13] . ...
doi:10.1109/focs.2009.77
dblp:conf/focs/HassidimKNO09
fatcat:reo7i3vue5djfglqkexjmdk42m
Data-driven Algorithm Design
[article]
2020
arXiv
pre-print
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The challenge is that for many combinatorial problems of significant importance including partitioning, subset selection, and alignment problems, a small tweak to the parameters can cause a cascade of ...
In this chapter, we survey recent work that helps put data-driven combinatorial algorithm design on firm foundations. ...
of random problem parameters in algorithm configuration instances. ...
arXiv:2011.07177v1
fatcat:xozyiox2pjgtxftntoxoosumt4
Abstract Combinatorial Programs and Efficient Property Testers
2005
SIAM journal on computing (Print)
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Among others, we present efficient property testing algorithms for geometric clustering problems, for the reversal distance problem, for graph and hypergraph coloring problems. ...
We apply our framework to a variety of classical combinatorial problems. ...
graph partitioning problems. ...
doi:10.1137/s009753970444199x
fatcat:vqohwu2vcvealhnej6gfeh7zqq
Thermodynamic-RAM technology stack
2017
International Journal of Parallel, Emergent and Distributed Systems
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2014 pre-print arXiv:1406.5633v1 fatcat:wgj2x4zg5zhdpg2y2contot6seOther Versions
Bringing us closer to brain-like neural computation, kT-RAM will provide a general-purpose adaptive hardware resource to existing computing platforms enabling fast and low-power machine learning capabilities ...
of AHaH Computing and integrate it into today's digital computing systems. ...
Campbell from Boise State University for graciously providing us with memristor device data. ...
doi:10.1080/17445760.2017.1314472
fatcat:e2mm7stwzzhldklms72vywmd6a
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