Search Results - Approximate sample error minimization algorithm

Learning from non-irreducible Markov chains by Sandrić, Nikola, Šebek, Stjepan

“…Most of the existing literature on supervised machine learning problems focuses on the case when the training data set is drawn from an i.i.d. sample…”
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Optimal Subsampling for Large Sample Logistic Regression by Wang, HaiYing, Zhu, Rong, Ma, Ping

“… In this article, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression…”
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Phase Transition of Total Variation Based on Approximate Message Passing Algorithm by Cheng, Xiang, Lei, Hong

“…In compressed sensing (CS), one seeks to down-sample some high-dimensional signals and recover them accurately by exploiting the sparsity of the signals…”
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Rademacher learning rates for iterated random functions by Sandrić, Nikola

“…Most supervised learning methods assume training data is drawn from an i.i.d. sample. However, real-world problems often exhibit temporal dependence and strong correlations between marginals of the data-generating process, rendering…”
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A Canonical Form for Weighted Automata and Applications to Approximate Minimization by Balle, Borja, Panangaden, Prakash, Precup, Doina

“…We study the problem of constructing approximations to a weighted automaton. Weighted finite automata (WFA) are closely related to the theory of rational…”
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Learning from non-irreducible Markov chains by Sandrić, Nikola, Šebek, Stjepan

“…Mostof the existing literature on supervised machine learning problems focuses on the case when the training data set is drawn from an i.i.d. sample…”
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A Randomized Algorithm for Nonconvex Minimization With Inexact Evaluations and Complexity Guarantees by Li, Shuyao, Wright, Stephen J.

“…We consider minimization of a smooth nonconvex function with inexact oracle access to gradient and Hessian…”
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Efficient Data Placement and Replication for QoS-Aware Approximate Query Evaluation of Big Data Analytics by Xia, Qiufen, Xu, Zichuan, Liang, Weifa, Yu, Shui, Guo, Song, Zomaya, Albert Y.

“… Instead, sometimes users may only be interested in timely approximate rather than exact query results…”
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Minimization for conditional simulation: Relationship to optimal transport by Oliver, Dean S.

“…In this paper, we consider the problem of generating independent samples from a conditional distribution when independent samples from the prior distribution are available…”
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Approximation of frame based missing data recovery by Cai, Jian-Feng, Shen, Zuowei, Ye, Gui-Bo

“… While many such algorithms have been developed recently, there are very few papers available on their error estimations…”
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A deep neural network-based method for solving obstacle problems by Cheng, Xiaoliang, Shen, Xing, Wang, Xilu, Liang, Kewei

“… The approximate error is bounded by the depth and width of the network, the statistical error is estimated by the number of samples, and the optimization error is reflected in the empirical loss term…”
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On optimal selection of summary statistics for approximate Bayesian computation by Nunes, Matthew A, Balding, David J

“… We approach it from the point of view of seeking data summaries that minimize the average squared error of the posterior distribution for a parameter of interest under approximate Bayesian computation (ABC…”
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Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black-Scholes Partial Differential Equations by Berner, Julius, Grohs, Philipp, Jentzen, Arnulf

“…The development of new classification and regression algorithms based on empirical risk minimization (ERM…”
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Near-optimality of greedy set selection in the sampling of graph signals by Chamon, Luiz F. O., Ribeiro, Alejandro

“… Still, sampling set selection remains an open issue. Indeed, although conditions for graph signal reconstruction from noiseless samples were derived, the presence of noise makes sampling set selection combinatorial and NP-hard in general…”
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Machine learning based energy-free structure predictions of molecules, transition states, and solids by Lemm, Dominik, von Rudorff, Guido Falk, von Lilienfeld, O. Anatole

“… Conventionally, force-fields or ab initio methods determine structure through energy minimization, which is either approximate or computationally demanding…”
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Statistical learning problem of artificial neural network to control roofing process by Lapidus, Azariy, Makarov, Aleksandr

“… ANN learning is the main stage of its development. A key question for supervised learning is how many number of training examples we need to approximate the true relationship between network inputs and output with the desired accuracy…”
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Algorithm for sparse representation minimizing mean square error of power spectrograms by Tanaka, Yuma, Ogawa, Takahiro, Haseyama, Miki

“…Sparse representation is an idea to approximate a target signal by a linear combination of a small number of sample signals, and it is utilized in various research fields…”
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Efficient and accurate approximate Bayesian inference with an application to insurance data by Streftaris, George, Worton, Bruce J.

“… An approximate Gibbs sampling method and an exact independence-type Metropolis–Hastings algorithm are derived, based on a log-normal/gamma mixture density that closely approximates the conditional distribution of the Poisson parameters…”
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Group Distributionally Robust Dataset Distillation with Risk Minimization by Vahidian, Saeed, Wang, Mingyu, Gu, Jianyang, Kungurtsev, Vyacheslav, Jiang, Wei, Chen, Yiran

“… However, targeting the training dataset must be thought of as auxiliary in the same sense that the training set is an approximate substitute for the population distribution, and the latter is the data…”
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An optimal control framework for adaptive neural ODEs by Aghili, Joubine, Mula, Olga

“… The learning task consists in finding the ODE parameters as the optimal values of a sampled loss minimization problem…”
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