Adaptive sequential machine learning
TL;DR: A bound is developed to show that the estimate of the change in the minimizers is non trivial provided that the excess risk is small enough and the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer does not exceed a target level.
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Abstract: A framework previously introduced in Wilson et al. (2018) for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to machine learni...
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Stochastic Saddle Point Problems with Decision-Dependent Distributions
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Online Projected Gradient Descent for Stochastic Optimization With Decision-Dependent Distributions
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Online Projected Gradient Descent for Stochastic Optimization with Decision-Dependent Distributions
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Convergence of the Inexact Online Gradient and Proximal-Gradient Under the Polyak-Łojasiewicz Condition.
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References
•Book
The Elements of Statistical Learning
Trevor Hastie,Robert Tibshirani,Jerome H. Friedman +2 more
- 01 Jan 2001
29.4K
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
An introduction to ROC analysis
TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
21.3K
The Elements of Statistical Learning
TL;DR: Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.
15.5K
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.
15.4K