Jax 2020-09-15 – 2020-11-17

Weighted data in statistics 2020-11-04 – 2020-11-06

Gradient descent First order of business 2014-10-04 – 2020-10-27

Automatic design of experiments Minesweeper++ 2017-04-11 – 2020-10-13

Inverse problems for complex models a.k.a. Bayesian calibration, model uncertainty 2020-10-13

Hyperparameter optimization in ML Replacing a hyperparameter problem with a hyperhyperparameter problem which feels like progress I guess 2020-09-25 – 2020-10-06

Sparse model selection 2016-09-05 – 2020-10-02

AutoML 2017-07-17 – 2020-10-02

Automatic differentiation 2016-07-27 – 2020-09-30

Data summarization On maps drawn at smaller than 1:1 scale 2019-01-14 – 2020-09-18

Independence, conditional, statistical 2016-04-21 – 2020-09-13

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

(Outlier) robust statistics 2014-11-25 – 2020-07-14

(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

Boosting, bagging and other weak-learner ensemble methods A selection of randomly stopped clocks is never far from wrong 2015-09-23 – 2020-06-07

Automatic differentiation in Julia 2016-07-27 – 2020-06-05

Functional regression 2016-01-05 – 2020-05-28

Functional regression 2016-01-05 – 2020-05-28

Natural gradient descent Climbing slower on the tricky bits 2019-07-18 – 2020-05-26

Natural gradient descent Climbing slower on the tricky bits 2019-07-18 – 2020-05-26

Matrix calculus 2018-07-09 – 2020-05-19

Measure concentration inequalities On being 80% sure you are only 20% wrong 2014-11-25 – 2020-05-06

Learning summary statistics 2020-04-22

Learning Gamelan 2016-04-05 – 2020-04-06

Restricted isometry properties Plus incoherence, irrepresentability, and other uncertainty bounds for a sparse world, and maybe frame theory, what’s that now? 2017-06-12 – 2020-03-09

Optimal transport metrics Wasserstein distances, Monge-Kantorovich metrics, Earthmover distances 2019-05-30 – 2020-03-06

Kernel approximation 2016-07-27 – 2020-03-06

Random change of time Stochastic processes derived by varying the rate of time’s passage, which is more convenient than I imagined 2015-08-05 – 2020-02-10

Branching processes 2014-08-18 – 2020-02-07

Gradient descent, first-order, stochastic a.k.a. SGD, as seen in deep learning 2020-01-30 – 2020-02-07

Conditional expectation and probability 2020-02-04

Gradient descent, Newton-like, stochastic 2020-01-23

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

Information geometry 2011-10-21 – 2019-12-27

Information geometry 2011-10-21 – 2019-12-27

Hawkes processes 2019-12-22

Convergence of random variables 2019-12-03

Time frequency analysis Multiplying your exposure to uncertainty principles 2018-01-06 – 2019-11-26

Gradient descent, Higher order 2019-10-26

Sparse regression 2016-06-23 – 2019-10-24

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

Non-negative matrix factorisation 2019-10-14

Zeros of random trigonometric polynomials 2019-05-20 – 2019-10-14

Exponential families 2016-04-19 – 2019-10-12

Wiener-Khintchine representation Now with bonus Bochner! 2019-05-08 – 2019-09-23

Representer theorems 2019-09-16

Wirtinger calculus It’s not complicated / It’s complex 2019-05-08 – 2019-09-10

Gradient descent, Newton-like 2019-02-05 – 2019-09-03

Stability in linear dynamical systems This Bodes well 2019-07-19

Probability 2011-07-29 – 2019-07-15

Semidefinite proramming 2019-06-29

Optimisation 2014-10-04 – 2019-06-27

Fourier interpolation 2019-06-19

Stability in dynamical systems Lyapunov exponents and ilk 2019-05-21

Wacky regression 2015-09-23 – 2019-05-02

Differential equations Thou, silent form, dost tease us out of thought / As doth eternity 2019-03-28

Inner product spaces The most highly developed theory of squaring things 2019-01-01 – 2019-02-11

Nearly sufficient statistics How about “Sufficient sufficiency”? — is that taken? 2018-03-13 – 2019-01-14

Decaying sinusoid dictionaries 2019-01-07

Normed spaces 2019-01-01 – 2019-01-04

Learning on manifolds Finding the lowest bit of a krazy straw, from the inside 2011-10-21 – 2018-11-16

Learning on manifolds Finding the lowest bit of a krazy straw, from the inside 2011-10-21 – 2018-11-16

Optimisation, combinatorial 2018-08-11

Linear algebra If the thing is twice as big, the transformed version of the thing is also twice as big. {End} 2011-04-06 – 2018-08-07

Integral probability metrics 2016-08-21 – 2017-10-31

Gradient descent, continuous, primal/dual formulations. 2017-08-07

Warping and registration problems Matching up of bumps and wibbles in stretchy things 2016-08-17 – 2017-07-23

Lagrangian mechanics 2015-02-11 – 2017-06-18

Compressed sensing / compressed sampling The fanciest ways of counting zero 2014-08-18 – 2017-06-14

Uncertainty principles 2017-05-14

Randomised regression 2017-01-13 – 2017-02-22

Functional equations Putting the funk in functions 2017-02-06 – 2017-02-19

Inverse problems 2016-03-30 – 2017-02-14

Metric entropy 2017-02-13

Random embeddings and hashing 2016-12-05 – 2017-02-13

Penalised/regularised regression 2016-06-23 – 2016-09-15

Statistical learning theory Eventually including structural risk minimisation, risk bounds, hopefully-uniform convergence rates, VC-dimension, generalisation-and-stability framings etc 2016-07-06 – 2016-08-16

Blind deconvolution 2015-03-01 – 2016-07-27

Matrix norms, divergences, metrics 2016-06-03 – 2016-06-15

Function approximation and interpolation 2016-06-09

Iterated function systems 2014-08-14 – 2016-06-06

Deconvolution 2015-04-19 – 2016-04-11

Sigma algebras, probability spaces, measure theory The scaffolding of randomness 2015-06-20