Relevant bibliographies by topics / Randomized sketches

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Randomized sketches'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Randomized sketches"

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Chen, Ziling, and Shaoxu Song. "Randomized Sketches for Quantile in LSM-tree based Store." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–26. https://doi.org/10.1145/3709717.

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Quantiles are costly to compute exactly but can be efficiently estimated by quantile sketches. Extensive works on summarizing streaming data, such as KLL sketch, focus on minimizing the cost in memory to provide certain error guarantees. For the problem of quantile estimation of values in LSM-tree based stores, streaming methods have an expensive I/O cost linear to data size N. Since disk components (chunks and SSTables) in the LSM-tree are immutable once flushed, quantile sketches can be pre-computed as a type of statistics to reduce I/O cost and accelerate queries. Unfortunately, to provide
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Lian, Heng, Fode Zhang, and Wenqi Lu. "Randomized sketches for kernel CCA." Neural Networks 127 (July 2020): 29–37. http://dx.doi.org/10.1016/j.neunet.2020.04.006.

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Chen, Ziling, Haoquan Guan, Shaoxu Song, Xiangdong Huang, Chen Wang, and Jianmin Wang. "Determining Exact Quantiles with Randomized Summaries." Proceedings of the ACM on Management of Data 2, no. 1 (2024): 1–26. http://dx.doi.org/10.1145/3639280.

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Quantiles are fundamental statistics in various data science tasks, but costly to compute, e.g., by loading the entire data in memory for ranking. With limited memory space, prevalent in end devices or databases with heavy loads, it needs to scan the data in multiple passes. The idea is to gradually shrink the range of the queried quantile till it is small enough to fit in memory for ranking the result. Existing methods use deterministic sketches to determine the exact range of quantile, known as deterministic filter, which could be inefficient in range shrinking. In this study, we propose to
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Zhang, Fode, Xuejun Wang, Rui Li, and Heng Lian. "Randomized sketches for sparse additive models." Neurocomputing 385 (April 2020): 80–87. http://dx.doi.org/10.1016/j.neucom.2019.12.012.

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Pilanci, Mert, and Martin J. Wainwright. "Randomized Sketches of Convex Programs With Sharp Guarantees." IEEE Transactions on Information Theory 61, no. 9 (2015): 5096–115. http://dx.doi.org/10.1109/tit.2015.2450722.

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Yang, Yun, Mert Pilanci, and Martin J. Wainwright. "Randomized sketches for kernels: Fast and optimal nonparametric regression." Annals of Statistics 45, no. 3 (2017): 991–1023. http://dx.doi.org/10.1214/16-aos1472.

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Xiong, Xianzhu, Rui Li, and Heng Lian. "On nonparametric randomized sketches for kernels with further smoothness." Statistics & Probability Letters 153 (October 2019): 139–42. http://dx.doi.org/10.1016/j.spl.2019.06.001.

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Chen, Yang, Huishu Wu, and Xuhao Ren. "Efficient and Secure Traffic Scheduling Based on Private Sketch." Mathematics 13, no. 2 (2025): 288. https://doi.org/10.3390/math13020288.

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In today’s data–driven world, the explosive growth of network traffic often leads to network congestion, which seriously affects service performance and user experience. Network traffic scheduling is one of the key technologies to deal with congestion problems. Traditional traffic scheduling methods often rely on static rules or pre–defined policies, which make it difficult to cope with dynamically changing network traffic patterns. Additionally, the inability to efficiently manage tail contributors that disproportionately contribute to traffic can further exacerbate congestion issues. In this
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Chen, Yuantao, Weihong Xu, Fangjun Kuang, and Shangbing Gao. "The Study of Randomized Visual Saliency Detection Algorithm." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/380245.

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Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scalin
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姜, 昊辰. "A Count Sketch Randomized Average Block Kaczmarz Method for Solving Highly Overdetermined Linear Systems." Advances in Applied Mathematics 14, no. 02 (2025): 244–50. https://doi.org/10.12677/aam.2025.142067.

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Dissertations / Theses on the topic "Randomized sketches"

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Wacker, Jonas. "Random features for dot product kernels and beyond." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS241.

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Les noyaux de produit scalaire, tels que les noyaux polynomiaux et exponentiels (softmax), sont parmi les noyaux les plus utilisés en apprentissage automatique, car ils permettent de modéliser les interactions entre les composantes des vecteurs d'entrée, ce qui est crucial dans des applications telles que la vision par ordinateur, le traitement du langage naturel et les systèmes de recommandation. Cependant, un inconvénient fondamental des modèles statistiques basés sur les noyaux est leur évolutivité limitée à un grand nombre de données d'entrée, ce qui nécessite de recourir à des approximati
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Gower, Robert Mansel. "Sketch and project : randomized iterative methods for linear systems and inverting matrices." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20989.

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Probabilistic ideas and tools have recently begun to permeate into several fields where they had traditionally not played a major role, including fields such as numerical linear algebra and optimization. One of the key ways in which these ideas influence these fields is via the development and analysis of randomized algorithms for solving standard and new problems of these fields. Such methods are typically easier to analyze, and often lead to faster and/or more scalable and versatile methods in practice. This thesis explores the design and analysis of new randomized iterative methods for solv
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Gazagnadou, Nidham. "Expected smoothness for stochastic variance-reduced methods and sketch-and-project methods for structured linear systems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT035.

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L'augmentation considérable du volume de données ainsi que de la taille des échantillons complexifie la phase d'optimisation des algorithmes d'apprentissage, nécessitant la minimisation d'une fonction de perte. La descente de gradient stochastique (SGD) et ses variantes à réduction de variance (SAGA, SVRG, MISO) sont largement utilisées pour résoudre ces problèmes. En pratique, ces méthodes sont accélérées en calculant des gradients stochastiques sur un "mini-batch" : un petit groupe d'échantillons tiré aléatoirement. En effet, les récentes améliorations technologiques permettant la parallélis
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Book chapters on the topic "Randomized sketches"

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Roy, Subhro, Rahul Chatterjee, Partha Bhowmick, and Reinhard Klette. "MAESTRO: Making Art-Enabled Sketches through Randomized Operations." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23672-3_39.

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Andriushchenko, Roman, Milan Češka, Sebastian Junges, Joost-Pieter Katoen, and Šimon Stupinský. "PAYNT: A Tool for Inductive Synthesis of Probabilistic Programs." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_40.

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AbstractThis paper presents PAYNT, a tool to automatically synthesise probabilistic programs. PAYNT enables the synthesis of finite-state probabilistic programs from a program sketch representing a finite family of program candidates. A tight interaction between inductive oracle-guided methods with state-of-the-art probabilistic model checking is at the heart of PAYNT. These oracle-guided methods effectively reason about all possible candidates and synthesise programs that meet a given specification formulated as a conjunction of temporal logic constraints and possibly including an optimising
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Inchausti, Pablo. "The Generalized Linear Model." In Statistical Modeling With R. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192859013.003.0008.

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Abstract This chapter introduces the three components of a generalized linear model (GLM): the linear predictor, the link function, and the probability function. It discusses the exponential dispersion family as a generator model for GLMs in a large sense. It sketches the fitting of a GLM with the iteratively weighted least squares algorithm for maximum likelihood in the frequentist framework. It introduces the main methods for assessing the effects of explanatory variables in frequentist GLMs (the Wald and likelihood ratio tests), the use of deviance as a measure of lack of model fit in GLMs,
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Bevington, Dickon, Peter Fuggle, Liz Cracknell, and Peter Fonagy. "Future ambitions for the AMBIT project." In Adaptive Mentalization-Based Integrative Treatment. Oxford University Press, 2017. http://dx.doi.org/10.1093/med-psych/9780198718673.003.0011.

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The evidence for AMBIT is described, together with our intentions regarding future development and dissemination. Existing evidence, and its limits, is sketched out. Challenges for future data collection/analysis are highlighted in relation to a “model” that is, by design, broad and adaptable, rendering “fidelity” difficult to define in conventional terms. Opportunities for meaningful randomized trials are limited, but possible. A description of AMBIT’s strategy for future model development (referencing open-source computing, learning organizations, and quality improvement models) concludes wi
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Conference papers on the topic "Randomized sketches"

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Pilanci, Mert, and Martin J. Wainwright. "Randomized sketches of convex programs with sharp guarantees." In 2014 IEEE International Symposium on Information Theory (ISIT). IEEE, 2014. http://dx.doi.org/10.1109/isit.2014.6874967.

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Chen, Hongwei, Jie Zhao, Qixing Luo, and Yajun Hou. "Distributed randomized singular value decomposition using count sketch." In 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 2017. http://dx.doi.org/10.1109/spac.2017.8304273.

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Aghazade, K., H. Aghamiry, A. Gholami, and S. Operto. "Sketched Waveform Inversion (Swi): an Efficient Augmented Lagrangian Based Full-Waveform Inversion with Randomized Source Sketching." In 83rd EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, 2022. http://dx.doi.org/10.3997/2214-4609.202210284.

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