# 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