# approximation

Neural denoising diffusion models
Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models…
2021-11-11
– 2022-09-24Score matching
2021-11-11
– 2022-09-23Transforms of Gaussian noise
Delta method, error propagation, unscented transform, Taylor expansion…
2014-11-25
– 2022-09-01Gaussian belief propagation
Least squares at maximal elaboration
2014-11-25
– 2022-09-01Probability divergences
Metrics, contrasts and divergences and other ways of quantifying how similar are two randomnesses
2014-11-25
– 2022-08-30Elliptical belief propagation
Generalized least generalized squares
2022-08-22
– 2022-08-23Recommender systems
2020-11-30
– 2022-08-15ELBO
Evidence lower bound, variational free energy etc
2020-10-02
– 2022-08-03Bayes linear regression and basis-functions in Gaussian process regression
a.k.a Fixed Rank Kriging, weight space GPs
2022-02-22
– 2022-07-27Expectation propagation
Generalized moment matching
2015-10-26
– 2022-05-04Generalized Bayesian Computation
2019-10-03
– 2022-04-28Inference without KL divergence
2019-10-03
– 2022-04-28SLAM
Simultaneous Location and Mapping
2014-11-25
– 2022-04-28Vecchia factoring of GP likelihoods
Ignore some conditioning in the dependencies and attain a sparse cholesky factor for the precision matrix
2022-04-27Particle belief propagation
Graphical inference using empirical distribution estimates
2014-07-25
– 2022-04-08Particle Markov Chain Monte Carlo
Particle systems as MCMC proposals
2014-07-25
– 2022-04-08Belief propagation
2014-11-25
– 2022-03-31Generative flow
2022-03-07Message-passing algorithms in graphical models
Cleaving reality at the joint, then summing it at the marginal
2014-11-25
– 2022-02-17Variational inference
On fitting something not too far from a pretty good model that is not too hard
2016-03-22
– 2022-02-10Reparameterization tricks in inference
Pathwise gradient estimation, nNormalizing flows, invertible density models, inference by measure transport, low-dimensional coupling…
2018-04-04
– 2021-12-21Gaussian Processes as stochastic differential equations
Imposing time on things
2019-09-18
– 2021-11-25Deep generative models
2020-12-10
– 2021-11-11Learning on tabular data
2020-11-30
– 2021-06-21Optimal transport metrics
Wasserstein distances, Monge-Kantorovich metrics, Earthmover distances
2019-05-30
– 2021-06-08Energy based models
Inference with kinda-tractable un-normalized densities
2021-06-07Stein’s method
2021-03-12
– 2021-06-01Randomized low dimensional projections
2021-03-12
– 2021-05-24Generically approximating probability distributions
2021-03-12
– 2021-03-22Measure concentration inequalities
On being 80% sure I am only 20% wrong
2014-11-25
– 2021-03-04Efficient factoring of GP likelihoods
2020-10-16
– 2020-10-26Data summarization
On maps drawn at smaller than 1:1 scale
2019-01-14
– 2020-09-18Variational autoencoders
2019-11-04
– 2020-09-10Variational inference
On fitting the best model one can be bothered to
2016-03-22
– 2020-05-24Likelihood free inference
2020-04-22Nearly sufficient statistics
How about “Sufficient sufficiency”? — is that taken?
2018-03-13
– 2019-01-14Metric entropy
2017-02-13Statistical 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