# nonparametric

Neural nets with basis decomposition layers
2021-03-09
Reparameterization tricks in inference
Normalizing flows, invertible density models, inference by measure transport, low-dimensional coupling…
2018-04-04
– 2021-03-08
Polynomial bases
2021-01-29
– 2021-02-17
Random-forest-like methods
Boosting, bagging and other weak-learner ensemble methods
2015-09-23
– 2021-02-11
Bootstrap
Shuffling reality to produce your data
2014-11-26
– 2020-10-16
Functional regression
2016-01-05
– 2020-05-28
Empirical estimation of information
Informing yourself from your data how informative your data was
2011-04-19
– 2020-04-28
Mixture models for density estimation
2016-03-29
– 2020-04-24
Learning Gamelan
2016-04-05
– 2020-04-06
Kernel approximation
2016-07-27
– 2020-03-06
Bias reduction
Estimating the bias of an estimator so as to subtract it off again
2020-02-26
Survival analysis and reliability
Hazard rates, proportional hazard regression, life testing, mean time to failure
2019-03-12
– 2020-02-05
(Reproducing) kernel tricks
2014-08-18
– 2020-01-20
Gaussian processes
2016-08-07
– 2019-12-03
Sparse coding
How to make big things out of lists of small things.
2014-11-17
– 2019-11-05
Discrete time Fourier and related transforms
Also, chirplets, z-transforms, chromatic derivatives…
2019-10-17
– 2019-10-17
Density estimation
Especially non- or semiparametrically
2016-06-06
– 2019-10-16
The interpretation of densities as intensities and vice versa
Point process of observations ↔ observation of a point process
2016-09-13
– 2019-09-23
Wacky regression
2015-09-23
– 2019-05-02
Inner product spaces
The most highly developed theory of squaring things
2019-01-01
– 2019-02-11
Normed spaces
2019-01-01
– 2019-01-04
Integral probability metrics
2016-08-21
– 2017-10-31
Kernel density estimators
2016-03-05
– 2016-08-18
Function approximation and interpolation
2016-06-09
Deconvolution
2015-04-19
– 2016-04-11