feature_construction

Laplace approximations in inference Lightweight uncertainties, especially for heavy neural nets 2021-07-28 – 2021-11-03

Random neural networks 2017-02-17 – 2021-10-12

Approximate Bayesian Computation Posterior updates without likelihood 2020-08-25 – 2021-09-20

Learning summary statistics 2020-04-22 – 2021-07-15

Overparameterization a.k.a. improper learning 2018-04-04 – 2021-07-08

Randomized low dimensional projections 2021-03-12 – 2021-05-24

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

Random embeddings and hashing 2016-12-05 – 2020-12-01

Randomised regression 2017-01-13 – 2020-12-01

Dimensionality reduction Wherein I teach myself, amongst other things, feature selection, how a sparse PCA works, and decide where to file multidimensional scaling 2015-03-22 – 2020-09-11

(Approximate) matrix factorisation 2014-08-05 – 2020-07-03

Learning of manifolds Also topological data analysis; other hip names to follow 2014-08-19 – 2020-06-23

Likelihood free inference 2020-04-22

Learnable indexes and hashes 2018-01-12 – 2020-02-18

Non-negative matrix factorisation 2019-10-14

Fourier interpolation 2019-06-19

Entity embeddings 2017-04-01

Clustering 2015-05-22 – 2016-06-07

Indirect inference 2014-12-23 – 2015-12-15