sparser_than_thou

Random rotations 2021-05-18 – 2021-12-01

Probabilistic neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2021-11-03

High dimensional statistics 2015-03-12 – 2021-10-28

Fun with rotational symmetries 2021-01-29 – 2021-10-14

Regularising neural networks Generalisation for street fighters 2017-02-12 – 2021-09-24

Neural nets with implicit layers Also, declarative networks 2020-12-08 – 2021-09-07

Inverse problems 2016-03-30 – 2021-08-23

Rough path theory Also, signatures. 2021-04-02 – 2021-08-05

Recommender systems 2020-11-30 – 2021-07-19

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

Multi-task ML 2021-07-14

ML on small devices Putting intelligence on chips small enough to be in disconcerting places 2016-10-14 – 2021-07-13

Learning on tabular data 2020-11-30 – 2021-06-21

Isotropic random vectors 2011-08-10 – 2021-05-24

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

Compressing neural nets pruning, compacting and otherwise fitting a good estimate into fewer parameters 2016-10-14 – 2021-05-07

Fourier transforms 2021-01-29 – 2021-04-26

Colour 2015-04-07 – 2021-03-24

Model interpretation and explanation Colorizing black boxes 2016-09-01 – 2021-03-15

Neural nets with basis decomposition layers 2021-03-09

Frames and Riesz bases Generalisations of orthogonal bases 2017-06-12 – 2021-02-24

Integral transforms 2021-01-29 – 2021-01-30

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

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

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

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

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

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

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

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

Lévy stochastic differential equations 2020-05-23

Mixture models for density estimation 2016-03-29 – 2020-04-24

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

Sparse coding How to make big things out of lists of small things. 2014-11-17 – 2019-11-05

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

Non-negative matrix factorisation 2019-10-14

Covariance estimation for stochastic processes 2014-11-16 – 2019-09-21

Fourier interpolation 2019-06-19

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

Sparse stochastic processes and sampling Discrete sample representation of sparse continuous stochastic processes 2018-11-22 – 2018-10-29

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

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

Randomised linear algebra 2016-08-16

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

Kernel approximation 2016-07-27

Function approximation and interpolation 2016-06-09

Clustering 2015-05-22 – 2016-06-07

Deconvolution 2015-04-19 – 2016-04-11

Sparse regression for inhomogeneous Hawkes processes My MSc thesis with Professors Didier Sornette and Sara van de Geer 2015-04-28 – 2015-05-12