An Empirical Study on Distributed Bayesian Approximation Inference of Piecewise Sparse Linear Models

Asahara, Masato; Fujimaki, Ryohei

doi:10.1109/tpds.2019.2892972

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…More details are provided in Supplementary Methods. The interpretable machine learning can solve data-classification and data-regression problems simultaneously, by maximizing a novel information criterion (factorized information criterion, which is referred to as FIC) with an Expectation-Maximization-like algorithm (factorized asymptotic Bayesian inference, referred to as FAB), thus constructing a piecewise sparse linear model 12,13 . Figure 4a, b shows the visualization of the model constructed by the interpretable machine learning.…”

Section: Machine Learning Modeling By the Interpretable Machine Learningmentioning

confidence: 99%

“…The factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs) constructs a piecewise sparse linear model that assigns sparse linear experts to individual partitions in feature space and expresses whole models as patches of local experts 12,13 . By maximizing the factorized information criterion including two L 0 -regularizations for partitionstructure determinations and feature selection for individual experts, the FAB/HMEs performs the partition-structure determination and feature selection at the same time.…”

Section: Fab/hmesmentioning

confidence: 99%

“…Here, we show an actual material development by using stateof-the-art interpretable machine learning called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/ HMEs) 12,13 . The development demonstrated the synergy between the FAB/HMEs and the materials scientists.…”

Section: Introductionmentioning

confidence: 99%

“…Interpretable machine learning FAB/HMEs constructs a piecewise sparse linear modeling 12 that meets the three requirements of "sparse modeling", "prediction accuracy", and "interpretability". Therefore, the actionable information from the data-driven model provided by the FAB/HMEs can leads us to discoveries of novel materials.…”

Section: Introductionmentioning

confidence: 99%

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Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

et al. 2019

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Machine learning is becoming a valuable tool for scientific discovery. Particularly attractive is the application of machine learning methods to the field of materials development, which enables innovations by discovering new and better functional materials. To apply machine learning to actual materials development, close collaboration between scientists and machine learning tools is necessary. However, such collaboration has been so far impeded by the black box nature of many machine learning algorithms. It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics. Here, we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs). Based on prior knowledge of material science and physics, we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials. Guided by this, we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material. This material shows the largest thermopower to date.

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Section: Machine Learning Modeling By the Interpretable Machine Learningmentioning

confidence: 99%

Section: Fab/hmesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

et al. 2019

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“…In addition, there have been other reports on the interpretation of models with the aim of discovering new mechanisms [12,13]. Factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs) were developed [14,15] and were applied to extract knowledge on spin-driven thermoelectric materials [16]. Although FAB/ HMEs have interpretability, sparsity, and predictive ability of models, nonlinearity between X and y dividing samples into components is limited to squared terms and cross terms of X.…”

Section: Introductionmentioning

confidence: 99%

Constructing Regression Models with High Prediction Accuracy and Interpretability Based on Decision Tree and Random Forests

Shimizu

Kaneko

2021

J. Comput. Chem. Jpn.

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Models for predicting properties/activities of materials based on machine learning can lead to the discovery of new mechanisms underlying properties/activities of materials. However, methods for constructing models that exhibit both high prediction accuracy and interpretability remain a work in progress because the prediction accuracy and interpretability exhibit a trade-off relationship. In this study, we propose a new model-construction method that combines decision tree (DT) with random forests (RF); which we therefore call DT-RF. In DT-RF, the datasets to be analyzed are divided by a DT model, and RF models are constructed for each subdataset. This enables global interpretation of the data based on the DT model, while the RT models improve the prediction accuracy and enable local interpretations. Case studies were performed using three datasets, namely, those containing data on the boiling point of compounds, their water solubility, and the transition temperature of inorganic superconductors. We examined the proposed method in terms of its validity, prediction accuracy, and interpretability.

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DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions

2022

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An Empirical Study on Distributed Bayesian Approximation Inference of Piecewise Sparse Linear Models

Cited by 6 publications

References 21 publications

Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

Constructing Regression Models with High Prediction Accuracy and Interpretability Based on Decision Tree and Random Forests

DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions

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