nonparametric

Sparse coding with learnable dictionaries 2014-11-17 – 2023-03-02

Transport maps Inference by measure transport, low-dimensional coupling… 2018-04-04 – 2023-02-21

Bayes neural nets via subsetting weights 2017-01-11 – 2023-02-09

Last-layer Bayes neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2023-02-09

Reparameterization tricks in inference Pathwise gradient estimation, normalizing flows, invertible density models 2018-04-04 – 2023-01-19

Maximum Mean Discrepancy 2016-08-21 – 2023-01-13

Neural tangent kernel 2020-12-09 – 2022-10-14

Precision matrix estimation Especially Gaussain 2014-11-16 – 2022-10-04

The interpretation of RV densities as point process intensities and vice versa Point process of observations ↔ observation of a point process 2016-09-13 – 2022-09-24

Ensemble Kalman methods for training neural networks Data assimilation for network weights 2022-09-20

Gaussian process regression And classification. And extensions. 2019-12-03 – 2022-09-20

Neural process regression 2019-12-03 – 2022-09-20

Learning Gamelan 2016-04-05 – 2022-08-05

Gaussian process regression software And classification. 2019-12-03 – 2022-07-29

Simulating Gaussian processes on a lattice 2022-03-17 – 2022-07-26

(Discrete-measure)-valued stochastic processes 2019-10-10 – 2022-05-04

Measure-valued stochastic processes Moving masses 2020-10-16 – 2022-05-03

Generalized Bayesian Computation 2019-10-03 – 2022-04-28

Bayesian nonparametric statistics Updating more dimensions than datapoints 2016-05-30 – 2022-04-07

Probabilistic neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2022-04-07

Probabilistic neural nets Inferring distributions in neural nets 2017-01-11 – 2022-04-07

Measure-valued random variates Including completely random measures and many generalizations 2020-10-16 – 2022-03-30

Simulating Gaussian processes 2022-03-17

Sparse coding Wavelets, matching pursuit, overcomplete dictionaries… 2014-11-17 – 2022-03-07

Survival analysis and reliability Hazard rates, proportional hazard regression, life testing, mean time to failure 2019-03-12 – 2022-03-07

Self-supervised learning I just wanna be meeeeee / with high probabilityyy ♬♪ 2022-03-04

Neural nets with basis decomposition layers 2021-03-09 – 2022-02-01

Karhunen-Loève expansions 2019-09-16 – 2022-02-01

Bootstrap Shuffling reality to produce your data 2014-11-26 – 2022-01-27

t-processes, t-distributions 2021-11-10 – 2021-11-24

Polynomial bases 2021-01-29 – 2021-10-06

Gaussian processes 2016-08-07 – 2021-06-23

Random-forest-like methods A selection of randomly stopped clocks is never far from wrong. 2015-09-23 – 2021-06-17

Infinite width limits of neural networks 2020-12-09 – 2021-05-11

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

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

(Reproducing) kernel tricks 2014-08-18 – 2020-01-20

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

Covariance estimation Esp Gaussian 2014-11-16 – 2019-09-21

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

Kernel density estimators 2016-03-05 – 2016-08-18

Function approximation and interpolation 2016-06-09

Deconvolution 2015-04-19 – 2016-04-11