# sparser_than_thou

System identification in continuous time
Learning in continuous ODEs, SDEs and CDEs
2016-08-01
– 2023-11-30Quantization
2016-03-29
– 2023-11-02Non-negative matrix factorisation
2019-10-14
– 2023-10-26Low-rank matrices
2014-08-05
– 2023-09-29Matrix inverses
2014-08-05
– 2023-09-29Nearly-low-rank Hermitian matrices
a.k.a. perturbations of the identity, low-rank-plus-diagonal matrices
2014-08-05
– 2023-09-13Approximate matrix factorisations and decompositions
Sometimes even exact
2014-08-05
– 2023-08-23ML on small devices
Putting intelligence on chips small enough to be in disconcerting places
2016-10-14
– 2023-08-14Bayes neural nets via subsetting weights
2017-01-11
– 2023-07-03Rough path theory and signature methods
2021-04-02
– 2023-06-29Neural nets with implicit layers
Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem
2020-12-08
– 2023-06-28Tabular data processing in python
CSV to Data lake
2020-11-30
– 2023-05-29Matrix norms, divergences, metrics
2016-06-03
– 2023-05-29Recurrent / convolutional / state-space
Translating between means of approximating time series dynamics
2016-04-05
– 2023-05-24Singular Value Decomposition
The ML workhorse
2014-08-05
– 2023-05-23Matrix square roots
Whitening, preconditioning etc
2014-08-05
– 2023-05-13Multi-objective optimisation
2021-07-14
– 2023-05-04Data summarization
On maps drawn at smaller than 1:1 scale
2019-01-14
– 2023-04-27Probabilistic neural nets
Bayesian and other probabilistic inference in overparameterized ML
2017-01-11
– 2023-04-27Covariance estimation
Esp Gaussian
2014-11-16
– 2023-04-26Model order reduction
2015-03-22
– 2023-04-21Optimal rotations
2021-05-18
– 2023-04-06Deep sets
invariant and equivariant functions
2022-11-24
– 2023-03-21Sparse coding with learnable dictionaries
2014-11-17
– 2023-03-02Last-layer Bayes neural nets
Bayesian and other probabilistic inference in overparameterized ML
2017-01-11
– 2023-02-09Last-layer Bayes neural nets
Bayesian and other probabilistic inference in overparameterized ML
2017-01-11
– 2023-02-09Bayesian sparsity
2019-01-08
– 2022-10-25Randomised linear algebra
2016-08-16
– 2022-10-22Precision matrix estimation
Especially Gaussain
2014-11-16
– 2022-10-04Bayesian inverse problems in function space
a.k.a. Bayesian calibration, model uncertainty for PDEs and other wibbly, blobby things
2020-10-13
– 2022-09-26Ensemble Kalman methods for training neural networks
Data assimilation for network weights
2022-09-20Penalised/regularised regression
2016-06-23
– 2022-09-19Scattering transforms
2022-03-16
– 2022-08-31Mellin transforms
2021-01-29
– 2022-08-28Recommender systems
2020-11-30
– 2022-08-15Neural nets with implicit layers
Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem
2020-12-08
– 2022-08-09Learning Gamelan
2016-04-05
– 2022-08-05Inverse problems
2016-03-30
– 2022-06-30Model interpretation and explanation
Colorizing black boxes
2016-09-01
– 2022-05-06Beta Processes
2019-10-14
– 2022-04-08Stationary Gamma processes
2019-10-14
– 2022-04-08Probabilistic neural nets
Inferring distributions in neural nets
2017-01-11
– 2022-04-07Multivariate Gamma distributions
2019-10-14
– 2022-03-14Sparse coding
Wavelets, matching pursuit, overcomplete dictionaries…
2014-11-17
– 2022-03-07Fun with rotational symmetries
2021-01-29
– 2022-03-03Neural nets with basis decomposition layers
2021-03-09
– 2022-02-01Running neural nets backwards
2022-01-29Bayesian inverse problems
2016-03-30
– 2022-01-13Random rotations
2021-05-18
– 2021-12-01High dimensional statistics
2015-03-12
– 2021-10-28Regularising neural networks
Generalisation for street fighters
2017-02-12
– 2021-09-24Essays in stochastic processes
My PhD thesis with Zdravko I. Botev
2021-08-13Learning summary statistics
2020-04-22
– 2021-07-15Multi-task ML
2021-07-14Learning on tabular data
2020-11-30
– 2021-06-21Isotropic random vectors
2011-08-10
– 2021-05-24Randomized low dimensional projections
2021-03-12
– 2021-05-24Compressing neural nets
pruning, compacting and otherwise fitting a good estimate into fewer parameters
2016-10-14
– 2021-05-07Fourier transforms
2021-01-29
– 2021-04-26Colour
2015-04-07
– 2021-03-24Frames and Riesz bases
Generalisations of orthogonal bases
2017-06-12
– 2021-02-24Integral transforms
2021-01-29
– 2021-01-30Random embeddings and hashing
2016-12-05
– 2020-12-01Randomised regression
2017-01-13
– 2020-12-01Weighted data in statistics
2020-11-04
– 2020-11-06Sparse model selection
2016-09-05
– 2020-10-02Data 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
– 2020-09-11Emulators and surrogate models via ML
Shortcuts in scientific simulation using ML
2020-08-12
– 2020-08-26Functional regression
2016-01-05
– 2020-05-28Lévy stochastic differential equations
2020-05-23Mixture models for density estimation
2016-03-29
– 2020-04-24Restricted isometry properties
Plus incoherence, irrepresentability, and other uncertainty bounds for a sparse world, and maybe frame theory, what’s that now?
2017-06-12
– 2020-03-09Kernel approximation
2016-07-27
– 2020-03-06Sparse regression
2016-06-23
– 2019-10-24Discrete time Fourier and related transforms
Also, chirplets, z-transforms, chromatic derivatives…
2019-10-17
– 2019-10-17Random (element) matrix theory
2014-11-09
– 2019-10-10Fourier interpolation
2019-06-19Nearly sufficient statistics
How about “Sufficient sufficiency”? — is that taken?
2018-03-13
– 2019-01-14Compressed sensing and sampling
A fancy ways of counting zero
2014-08-18
– 2017-06-14Blind deconvolution
2015-03-01
– 2016-07-27Function approximation and interpolation
2016-06-09Function approximation and interpolation
2016-06-09Clustering
2015-05-22
– 2016-06-07Deconvolution
2015-04-19
– 2016-04-11Sparse regression for inhomogeneous Hawkes processes
My MSc thesis with Professors Didier Sornette and Sara van de Geer
2015-04-28
– 2015-05-12