Some notes concerning a generalized KMM-type optimization method for density ratio estimation
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Abstract (arXiv)
In the present paper we introduce new optimization algorithms for the task of density ratio estimation. More precisely, we consider extending the well-known KMM method using the construction of a suitable loss function, in order to encompass more general situations involving the estimation of density ratio with respect to subsets of the training data and test data, respectively. The associated codes can be found at https://github.com/CDAlecsa/Generalized-KMM.
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References · 9
- 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
- 2017A Rewriting System for Convex Optimization Problemscited by 179
- 2020Rethinking Importance Weighting for Deep Learning under Distribution Shiftcited by 93
- 2019Two-sample Testing Using Deep Learningcited by 27
- 2021ADAPT : Awesome Domain Adaptation Python Toolboxcited by 13
- 2020Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimationcited by 12
- 2021Meta-Learning for Relative Density-Ratio Estimationcited by 6
- 2021A Unified Framework for Multi-distribution Density Ratio Estimationcited by 5
- 2022Fast and Accurate Importance Weighting for Correcting Sample Biascited by 5
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