functional_analysis

Bayesian model calibration 2017-04-11 – 2023-06-01

Low-rank-plus-diagonal matrix representations 2014-08-05 – 2023-05-30

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

Bayes functional regression 2019-12-03 – 2023-05-25

Neural process regression 2019-12-03 – 2023-05-25

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

Gradient descent, first-order, stochastic a.k.a. SGD, as seen in deep learning 2020-01-30 – 2023-05-19

Canonical correlation 2014-08-23 – 2023-05-17

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

Hyperparameter optimization Replacing a hyperparameter problem with a hyperhyperparameter problem which feels like progress 2020-09-25 – 2023-05-12

Optimal transport inference I feel the earth mover under my feet, I feel the ψ tumbling down, I feel my heart start to trembling, whenever you’re around (my barycentre) 2021-03-16 – 2023-05-03

Maximum Mean Discrepancy 2016-08-21 – 2023-05-03

Polynomial bases 2021-01-29 – 2023-04-28

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

Gradient descent First order of business 2014-10-04 – 2023-04-27

Neural implicit representations Neural nets as coordinate mappings 2021-01-21 – 2023-04-26

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

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

Approximate matrix factorisation Sometimes even exact 2014-08-05 – 2023-04-18

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

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

(Nearly-)Convex relation of nonconvex problems 2018-04-04 – 2023-03-17

Matrix calculus 2018-07-09 – 2023-03-07

Scaling laws for very large neural nets Compute/size/data tradeoffs 2021-01-14 – 2023-02-16

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

(Kernelized) Stein variational gradient descent KSVD, SVGD 2022-11-02 – 2023-01-09

Graph neural nets 2020-09-16 – 2022-12-19

Gradient flows infinitesimal optimization 2020-01-30 – 2022-11-02

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

Neural tangent kernel 2020-12-09 – 2022-10-14

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

Automatic differentiation 2016-07-27 – 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

Gaussian process inference by partial updates 2020-12-03 – 2022-09-22

Conditional expectation and probability 2020-02-04 – 2022-09-21

Posterior Gaussian process samples by updating prior samples Matheron’s other weird trick 2020-12-03 – 2022-09-20

Gaussian process regression And classification. And extensions. 2019-12-03 – 2022-09-20

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

Distributional robustness in inference 2019-07-12 – 2022-08-30

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

Automatic differentiation in Julia 2016-07-27 – 2022-08-09

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

Gaussian process regression software And classification. 2019-12-03 – 2022-07-29

Gradient descent, Newton-like 2019-02-05 – 2022-07-25

Partial differential equations 2021-01-27 – 2022-07-23

Fun tricks in non-convex optimisation 2014-10-04 – 2022-07-14

Empirical mode decomposition Multiplying your exposure to uncertainty principles 2018-01-06 – 2022-07-12

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

Signal sampling Discrete representation of continuous signals and converse 2017-05-30 – 2022-06-24

Emergent spacetime What are qubits again? 2022-05-26

Exponential families 2016-04-19 – 2022-05-13

Measure-valued stochastic processes Moving masses 2020-10-16 – 2022-05-03

Numerical PDE solvers 2016-03-01 – 2022-05-02

Generalized Bayesian Computation 2019-10-03 – 2022-04-28

Bayesian nonparametric statistics Updating more dimensions than datapoints 2016-05-30 – 2022-04-07

Orthonormal and unitary matrices Energy preserving operators, generalized rotations 2019-10-22 – 2022-04-05

Measure-valued random variates Including completely random measures and many generalizations 2020-10-16 – 2022-03-30

Wiener-Khintchine representations Spectral representations of stochastic processes 2019-05-08 – 2022-03-11

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

Stability in dynamical systems Lyapunov exponents and ilk 2019-05-21 – 2022-02-22

Karhunen-Loève expansions 2019-09-16 – 2022-02-01

Hypothesis tests, statistical 2014-08-23 – 2022-01-27

Learning on manifolds Finding the lowest bit of a krazy straw, from the inside 2011-10-21 – 2022-01-26

(Outlier) robust statistics 2014-11-25 – 2022-01-21

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

Time frequency analysis Multiplying your exposure to uncertainty principles 2018-01-06 – 2021-12-21

Gradient descent, Newton-like, stochastic 2020-01-23 – 2021-12-09

Convolutional subordinator processes 2021-03-08 – 2021-12-01

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

Neural nets for “implicit representations” 2021-01-21 – 2021-11-16

Optimisation 2014-10-04 – 2021-11-11

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

Fun with determinants Especially Jacobian determinants 2011-04-06 – 2021-10-12

Multilinear algebra Outer products, tensors, einstein summation 2021-10-08

Gradient descent at scale Practical implementation of large optimisations 2021-07-14 – 2021-09-28

Meta learning Few-shot learning, learning fast weights, learning to learn 2021-09-16

Fractional differential equations 2016-03-22 – 2021-09-13

Wirtinger calculus It’s not complicated / It’s complex 2019-05-08 – 2021-09-07

Path smoothness properties of stochastic processes Continuity, differentiability and other smoothness properties 2020-02-26 – 2021-09-02

Sequential experiments Especially multiple sequential experiments 2021-08-04

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

Optimal transport metrics Wasserstein distances, Monge-Kantorovich metrics, Earthmover distances 2019-05-30 – 2021-06-08

Stein’s method His eyes are like angels but his heart is cold / No need to ask / He’s a Stein operator 2021-03-12 – 2021-06-01

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

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

Infinite width limits of neural networks 2020-12-09 – 2021-05-11

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

Differentiable model selection Differentiable hyperparameter search, and architecture search, and optimisation optimisation by optimisation and so on 2020-09-25 – 2021-04-13

Generically approximating probability distributions 2021-03-12 – 2021-03-22

Matrix measure concentration inequalities and bounds 2014-11-25 – 2021-03-08

Measure concentration inequalities On being 80% sure I am only 20% wrong 2014-11-25 – 2021-03-04

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

Stability in linear dynamical systems This Bodes well 2019-07-19 – 2021-02-16

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

Distribution regression 2020-12-01

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

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

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

AutoML 2017-07-17 – 2020-10-02

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

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

Learning of manifolds Also topological data analysis; other hip names to follow 2014-08-19 – 2020-06-23

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

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

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

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

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

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

Hawkes processes 2019-12-22

Convergence of random variables 2019-12-03

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

Representer theorems 2019-09-16

Probability 2011-07-29 – 2019-07-15

Semidefinite proramming 2019-06-29

Fourier interpolation 2019-06-19

Wiener theorem Now with bonus Bochner! 2019-05-08

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

Ordinary 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

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

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

Uncertainty principles 2017-05-14

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

Metric entropy 2017-02-13

Differential geometry, geometric algebra etc 2014-11-27 – 2016-10-13

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

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