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ACM Computing Surveys, 1995 For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in ...
Rajeev Motwani, Prabhakar Raghavan
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Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD) [PDF]
IEEE Access, 2021 Big data analysis has become a crucial part of new emerging technologies such as the internet of things, cyber-physical analysis, deep learning, anomaly detection, etc.
Salman Ahmadi-Asl +6 more
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Novel and Efficient Randomized Algorithms for Feature Selection
Big Data Mining and Analytics, 2020 Feature selection is a crucial problem in efficient machine learning, and it also greatly contributes to the explainability of machine-driven decisions. Methods, like decision trees and Least Absolute Shrinkage and Selection Operator (LASSO), can select ...
Zigeng Wang +2 more
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On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems
Algorithms, 2019 Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory ...
Angel Alejandro Juan +4 more
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PAC–Bayes Guarantees for Data-Adaptive Pairwise Learning [PDF]
EntropyWe study the generalization properties of stochastic optimization methods under adaptive data sampling schemes, focusing on the setting of pairwise learning, which is central to tasks like ranking, metric learning, and AUC maximization.
Sijia Zhou, Yunwen Lei, Ata Kabán
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Randomized algorithms for rounding in the Tensor-Train format [PDF]
SIAM Journal on Scientific Computing, 2021 The Tensor-Train (TT) format is a highly compact low-rank representation for high-dimensional tensors. TT is particularly useful when representing approximations to the solutions of certain types of parametrized partial differential equations.
Hussam Al Daas +7 more
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Randomized Algorithms for Scientific Computing (RASC) [PDF]
arXiv.org, 2021 Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion ...
A. Buluç +18 more
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Complexity of randomized algorithms for underdamped Langevin dynamics [PDF]
Communications in Mathematical Sciences, 2020 We establish an information complexity lower bound of randomized algorithms for simulating underdamped Langevin dynamics. More specifically, we prove that the worst $L^2$ strong error is of order $\Omega(\sqrt{d}\, N^{-3/2})$, for solving a family of $d$-
Yu Cao, Jianfeng Lu, Lihan Wang
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Evaluating dimensionality reduction for genomic prediction
Frontiers in Genetics, 2022 The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials.
Vamsi Manthena +8 more
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Randomized algorithms for generalized singular value decomposition with application to sensitivity analysis [PDF]
Numerical Linear Algebra with Applications, 2020 The generalized singular value decomposition (GSVD) is a valuable tool that has many applications in computational science. However, computing the GSVD for large‐scale problems is challenging.
A. Saibaba +2 more
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