approximation

Neural denoising diffusion models Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models… 2021-11-11 – 2022-09-24

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

Transforms of Gaussian noise Delta method, error propagation, unscented transform, Taylor expansion… 2014-11-25 – 2022-09-01

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

Probability divergences Metrics, contrasts and divergences and other ways of quantifying how similar are two randomnesses 2014-11-25 – 2022-08-30

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

Expectation propagation Generalized moment matching 2015-10-26 – 2022-05-04

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

Belief propagation 2014-11-25 – 2022-03-31

Generative flow 2022-03-07

Message-passing algorithms in graphical models Cleaving reality at the joint, then summing it at the marginal 2014-11-25 – 2022-02-17

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

Reparameterization tricks in inference Pathwise gradient estimation, nNormalizing flows, invertible density models, inference by measure transport, low-dimensional coupling… 2018-04-04 – 2021-12-21

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

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

Data summarization On maps drawn at smaller than 1:1 scale 2019-01-14 – 2020-09-18

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

Likelihood free inference 2020-04-22

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