approximation

Neural denoising diffusion models Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models… 2021-11-11 – 2023-12-06

Simulation-based inference 2014-12-23 – 2023-11-23

Gaussian process ensembles Bayesian committee machines, product-of-experts 2023-07-12

Position encoding Also Fourier features 2021-01-21 – 2023-06-23

Neural vector embeddings Hyperdimensional Computing, Vector Symbolic Architectures, Holographic Reduced Representations 2017-12-20 – 2023-06-13

Noise contrastive estimation Also “negative sampling”. 2020-04-22 – 2023-06-06

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

Belief propagation 2014-11-25 – 2023-05-17

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

Reparameterization methods for MC gradient estimation Pathwise gradient estimation, 2018-04-04 – 2023-05-02

Normalizing flows Invertible density models, sounding clever by using the word diffeomorphism like a real mathematician 2018-04-04 – 2023-05-02

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

Expectation propagation Generalized moment matching 2015-10-26 – 2023-04-03

Transport maps Inference by measure transport, low-dimensional coupling… 2018-04-04 – 2023-02-21

Generative flow nets Gflownets 2021-11-11 – 2023-02-13

Variational message-passing algorithms in graphical models Cleaving reality at the joint, then summing it at the marginal 2014-11-25 – 2023-01-12

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

Probability divergences Metrics, contrasts and divergences and other ways of quantifying how similar are two randomnesses 2014-11-25 – 2023-01-06

Transforms of Gaussian noise Delta method, error propagation, unscented transform, Taylor expansion… 2014-11-25 – 2022-12-23

Density ratio tricks 2022-12-06

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

Score matching 2021-11-11 – 2022-09-23

Gaussian belief propagation Least squares at maximal elaboration 2014-11-25 – 2022-09-01

Elliptical belief propagation Generalized least generalized squares 2022-08-22 – 2022-08-23

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

ELBO Evidence lower bound, variational free energy etc 2020-10-02 – 2022-08-03

Bayes linear regression and basis-functions in Gaussian process regression a.k.a Fixed Rank Kriging, weight space GPs 2022-02-22 – 2022-07-27

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

Inference without KL divergence 2019-10-03 – 2022-04-28

SLAM Simultaneous Location and Mapping 2014-11-25 – 2022-04-28

Vecchia factoring of GP likelihoods Ignore some conditioning in the dependencies and attain a sparse cholesky factor for the precision matrix 2022-04-27

Particle belief propagation Graphical inference using empirical distribution estimates 2014-07-25 – 2022-04-08

Particle Markov Chain Monte Carlo Particle systems as MCMC proposals 2014-07-25 – 2022-04-08

Variational inference On fitting something not too far from a pretty good model that is not too hard 2016-03-22 – 2022-02-10

Gaussian Processes as stochastic differential equations Imposing time on things 2019-09-18 – 2021-11-25

Deep generative models 2020-12-10 – 2021-11-11

Learning on tabular data 2020-11-30 – 2021-06-21

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

Energy based models Inference with kinda-tractable un-normalized densities 2021-06-07

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

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

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

Measure concentration inequalities The fancy name for probability inequalities 2014-11-25 – 2021-03-04

Efficient factoring of GP likelihoods 2020-10-16 – 2020-10-26

GP inducing features 2020-10-16 – 2020-10-26

GP inducing variables 2020-10-16 – 2020-10-26

Localized Gaussian processes 2020-10-16 – 2020-10-26

Variational autoencoders 2019-11-04 – 2020-09-10

Variational inference On fitting the best model one can be bothered to 2016-03-22 – 2020-05-24

Learnable indexes and hashes 2018-01-12 – 2020-02-18

Representer theorems 2019-09-16

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

Metric entropy 2017-02-13

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