scholarly journals Stochastic trust region inexact Newton method for large-scale machine learning

2020 ◽  
Vol 11 (7) ◽  
pp. 1541-1555
Author(s):  
Vinod Kumar Chauhan ◽  
Anuj Sharma ◽  
Kalpana Dahiya
Keyword(s):  
Machine Learning ◽  
Newton Method ◽  
Large Scale ◽  
Trust Region ◽  
Inexact Newton Method

Stochastic sub-sampled Newton method with variance reduction

2019 ◽  
Vol 17 (06) ◽  
pp. 1950041
Author(s):  
Zhijian Luo ◽  
Yuntao Qian
Keyword(s):  
Machine Learning ◽  
Newton Method ◽  
Large Scale ◽  
Variance Reduction ◽  
Linear Time ◽  
Computational Cost ◽  
Gradient Methods ◽  
Nonlinear Problems ◽  
Learning Problems ◽  
Reduced Gradient

Stochastic optimization on large-scale machine learning problems has been developed dramatically since stochastic gradient methods with variance reduction technique were introduced. Several stochastic second-order methods, which approximate curvature information by the Hessian in stochastic setting, have been proposed for improvements. In this paper, we introduce a Stochastic Sub-Sampled Newton method with Variance Reduction (S2NMVR), which incorporates the sub-sampled Newton method and stochastic variance-reduced gradient. For many machine learning problems, the linear time Hessian-vector production provides evidence to the computational efficiency of S2NMVR. We then develop two variations of S2NMVR that preserve the estimation of Hessian inverse and decrease the computational cost of Hessian-vector product for nonlinear problems.

scholarly journals Inexact Nonconvex Newton-Type Methods

2021 ◽  
pp. ijoo.2019.0043
Author(s):  
Zhewei Yao ◽  
Peng Xu ◽  
Fred Roosta ◽  
Michael W. Mahoney
Keyword(s):  
Machine Learning ◽  
Research Program ◽  
Large Scale ◽  
Trust Region ◽  
Optimization Methods ◽  
Worst Case ◽  
The Third ◽  
Cubic Regularization ◽  
Machine Learning Applications ◽  
Fourth Author

The paper aims to extend the theory and application of nonconvex Newton-type methods, namely trust region and cubic regularization, to the settings in which, in addition to the solution of subproblems, the gradient and the Hessian of the objective function are approximated. Using certain conditions on such approximations, the paper establishes optimal worst-case iteration complexities as the exact counterparts. This paper is part of a broader research program on designing, analyzing, and implementing efficient second-order optimization methods for large-scale machine learning applications. The authors were based at UC Berkeley when the idea of the project was conceived. The first two authors were PhD students, the third author was a postdoc, all supervised by the fourth author.

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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