Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
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TLDR
This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.Abstract:
Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the pre...read more
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Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach
Hamidreza Taghvaee,Akshay Jain,Xavier Timoneda,Christos Liaskos,Sergi Abadal,Eduard Alarcon,Albert Cabellos-Aparicio +6 more
TL;DR: In this article, a neural network-based approach is proposed to enable a fast and accurate characterization of the metasurface response to reflected wave radiation with an accuracy of 98.8-99.8%.
Posted Content
Natural quantum reservoir computing for temporal information processing
TL;DR: In this article, the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits, is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on a temporal sensor data.
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Machine Labor
TL;DR: In this paper , the utility of machine learning for regression-based causal inference is illustrated by using lasso to select control variables for estimates of college characteristics' wage effects, which is a path to data-driven sensitivity analysis.
Journal ArticleDOI
Fintech for the Poor: Financial Intermediation Without Discrimination
TL;DR: In this paper, a machine learning algorithm was used to improve the efficiency in lending without compromising on equity in a credit environment where soft information dominates, and the efficiency was maintained even when the algorithm is explicitly prevented from discriminating against disadvantaged social classes.
Journal ArticleDOI
Optimizing User Engagement through Adaptive Ad Sequencing
TL;DR: A unified dynamic framework for adaptive ad sequencing that optimizes user engagement in the session, e.g., the number of clicks or length of stay is proposed and it is demonstrated that adaptive forward-looking ad sequencing is most effective when users are new to the platform.
References
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
Journal ArticleDOI
Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak
TL;DR: In this article, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation.
Journal Article
On Model Selection Consistency of Lasso
Peng Zhao,Bin Yu +1 more
TL;DR: It is proved that a single condition, which is called the Irrepresentable Condition, is almost necessary and sufficient for Lasso to select the true model both in the classical fixed p setting and in the large p setting as the sample size n gets large.
Journal ArticleDOI
Clinical versus actuarial judgment
TL;DR: Research comparing these two approaches to decision-making shows the actuarial method to be superior, factors underlying the greater accuracy of actuarial methods, sources of resistance to the scientific findings, and the benefits of increased reliance on actuarial approaches are discussed.
Book
A Distribution-Free Theory of Nonparametric Regression
TL;DR: How to Construct Nonparametric Regression Estimates * Lower Bounds * Partitioning Estimates * Kernel Estimates * k-NN Estimates * Splitting the Sample * Cross Validation * Uniform Laws of Large Numbers