sparser_than_thou

System identification in continuous time Learning in continuous ODEs, SDEs and CDEs 2016-08-01 – 2023-11-30

Quantization 2016-03-29 – 2023-11-02

Non-negative matrix factorisation 2019-10-14 – 2023-10-26

Low-rank matrices 2014-08-05 – 2023-09-29

Matrix inverses 2014-08-05 – 2023-09-29

Nearly-low-rank Hermitian matrices a.k.a. perturbations of the identity, low-rank-plus-diagonal matrices 2014-08-05 – 2023-09-13

Approximate matrix factorisations and decompositions Sometimes even exact 2014-08-05 – 2023-08-23

ML on small devices Putting intelligence on chips small enough to be in disconcerting places 2016-10-14 – 2023-08-14

Bayes neural nets via subsetting weights 2017-01-11 – 2023-07-03

Rough path theory and signature methods 2021-04-02 – 2023-06-29

Neural nets with implicit layers Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem 2020-12-08 – 2023-06-28

Tabular data processing in python CSV to Data lake 2020-11-30 – 2023-05-29

Matrix norms, divergences, metrics 2016-06-03 – 2023-05-29

Recurrent / convolutional / state-space Translating between means of approximating time series dynamics 2016-04-05 – 2023-05-24

Singular Value Decomposition The ML workhorse 2014-08-05 – 2023-05-23

Matrix square roots Whitening, preconditioning etc 2014-08-05 – 2023-05-13

Multi-objective optimisation 2021-07-14 – 2023-05-04

Data summarization On maps drawn at smaller than 1:1 scale 2019-01-14 – 2023-04-27

Probabilistic neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2023-04-27

Covariance estimation Esp Gaussian 2014-11-16 – 2023-04-26

Model order reduction 2015-03-22 – 2023-04-21

Optimal rotations 2021-05-18 – 2023-04-06

Deep sets invariant and equivariant functions 2022-11-24 – 2023-03-21

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

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

Bayesian sparsity 2019-01-08 – 2022-10-25

Randomised linear algebra 2016-08-16 – 2022-10-22

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

Bayesian inverse problems in function space a.k.a. Bayesian calibration, model uncertainty for PDEs and other wibbly, blobby things 2020-10-13 – 2022-09-26

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

Penalised/regularised regression 2016-06-23 – 2022-09-19

Scattering transforms 2022-03-16 – 2022-08-31

Mellin transforms 2021-01-29 – 2022-08-28

Recommender systems 2020-11-30 – 2022-08-15

Neural nets with implicit layers Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem 2020-12-08 – 2022-08-09

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

Inverse problems 2016-03-30 – 2022-06-30

Model interpretation and explanation Colorizing black boxes 2016-09-01 – 2022-05-06

Beta Processes 2019-10-14 – 2022-04-08

Stationary Gamma processes 2019-10-14 – 2022-04-08

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

Multivariate Gamma distributions 2019-10-14 – 2022-03-14

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

Fun with rotational symmetries 2021-01-29 – 2022-03-03

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

Running neural nets backwards 2022-01-29

Bayesian inverse problems 2016-03-30 – 2022-01-13

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

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

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

Essays in stochastic processes My PhD thesis with Zdravko I. Botev 2021-08-13

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

Multi-task ML 2021-07-14

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

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

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

Data 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

Emulators and surrogate models via ML Shortcuts in scientific simulation using ML 2020-08-12 – 2020-08-26

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

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

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

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

Random (element) matrix theory 2014-11-09 – 2019-10-10

Fourier interpolation 2019-06-19

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

Compressed sensing and sampling A fancy ways of counting zero 2014-08-18 – 2017-06-14

Blind deconvolution 2015-03-01 – 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