# functional_analysis

Stein’s method
2021-03-12
– 2021-05-12
Fourier transforms
2021-01-29
– 2021-04-26
Path continuity of stochastic processes
2020-02-26
– 2021-04-19
Differentiable model selection
Differentiable hyperparameter search, and architecture search, and optimisation optimisation by optimisation and so on
2020-09-25
– 2021-04-13
Randomized low dimensional projections
2021-03-12
– 2021-04-12
High dimensional statistics
2015-03-12
– 2021-03-23
Generically approximating probability distributions
2021-03-12
– 2021-03-22
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 empirical density in minimal transport cost
2021-03-16
Optimal transport metrics
Wasserstein distances, Monge-Kantorovich metrics, Earthmover distances
2019-05-30
– 2021-03-16
ML scaling laws in the massive model limit
2021-01-14
– 2021-03-15
Numerical PDE solvers
2016-03-01
– 2021-03-15
Radial functions
2021-01-29
– 2021-03-12
Stochastic processes which represent measures over the reals
2020-10-16
– 2021-03-08
Convolutional subordinator processes
2021-03-08
Automatic differentiation
2016-07-27
– 2021-03-08
Matrix measure concentration inequalities and bounds
2014-11-25
– 2021-03-08
Measure concentration inequalities
On being 80% sure you are only 20% wrong
2014-11-25
– 2021-03-04
Learning on manifolds
Finding the lowest bit of a krazy straw, from the inside
2011-10-21
– 2021-03-03
Hyperparameter optimization in ML
Replacing a hyperparameter problem with a hyperhyperparameter problem which feels like progress I guess
2020-09-25
– 2021-03-01
Frames and Riesz bases
Generalisations of orthogonal bases
2017-06-12
– 2021-02-24
Jax
2020-09-15
– 2021-02-18
Causal inference in the continuous limit
2021-02-17
Polynomial bases
2021-01-29
– 2021-02-17
Stability in linear dynamical systems
This Bodes well
2019-07-19
– 2021-02-16
Partial differential equations
2021-01-27
– 2021-02-01
Integral transforms
2021-01-29
– 2021-01-30
Neural nets for “implicit representations”
2021-01-21
– 2021-01-21
Wiener-Khintchine representation
Now with bonus Bochner!
2019-05-08
– 2021-01-03
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
Gradient descent
First order of business
2014-10-04
– 2020-10-27
Automatic design of experiments
Minesweeper++
2017-04-11
– 2020-10-13
Inverse problems for complex models
a.k.a. Bayesian calibration, model uncertainty
2020-10-13
Sparse model selection
2016-09-05
– 2020-10-02
AutoML
2017-07-17
– 2020-10-02
Data summarization
On maps drawn at smaller than 1:1 scale
2019-01-14
– 2020-09-18
Independence, conditional, statistical
2016-04-21
– 2020-09-13
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
(Outlier) robust statistics
2014-11-25
– 2020-07-14
(Approximate) matrix factorisation
2014-08-05
– 2020-07-03
Learning of manifolds
Also topological data analysis; other hip names to follow
2014-08-19
– 2020-06-23
Automatic differentiation in Julia
2016-07-27
– 2020-06-05
Functional regression
2016-01-05
– 2020-05-28
Natural gradient descent
Climbing slower on the tricky bits
2019-07-18
– 2020-05-26
Matrix calculus
2018-07-09
– 2020-05-19
Limit Theorems
Asymptotic distributions of random processes
2014-11-25
– 2020-05-06
Learning summary statistics
2020-04-22
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
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
Gradient descent, first-order, stochastic
a.k.a. SGD, as seen in deep learning
2020-01-30
– 2020-02-07
Conditional expectation and probability
2020-02-04
Gradient descent, Newton-like, stochastic
2020-01-23
(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
Time frequency analysis
Multiplying your exposure to uncertainty principles
2018-01-06
– 2019-11-26
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
Exponential families
2016-04-19
– 2019-10-12
Representer theorems
2019-09-16
Wirtinger calculus
It’s not complicated / It’s complex
2019-05-08
– 2019-09-10
Gradient descent, Newton-like
2019-02-05
– 2019-09-03
Probability
2011-07-29
– 2019-07-15
Semidefinite proramming
2019-06-29
Optimisation
2014-10-04
– 2019-06-27
Fourier interpolation
2019-06-19
Stability in dynamical systems
Lyapunov exponents and ilk
2019-05-21
Wiener theorem
Now with bonus Bochner!
2019-05-08
Wacky regression
2015-09-23
– 2019-05-02
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
Integral probability metrics
2016-08-21
– 2017-10-31
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 / compressed sampling
The fanciest 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
Inverse problems
2016-03-30
– 2017-02-14
Metric entropy
2017-02-13
Penalised/regularised regression
2016-06-23
– 2016-09-15
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
Randomised linear algebra
2016-08-16
Blind deconvolution
2015-03-01
– 2016-07-27
Matrix norms, divergences, metrics
2016-06-03
– 2016-06-15
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