# approximation

Tabular data processing in python
CSV to Data lake
2020-11-30
– 2023-05-29Neural denoising diffusion models
Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models…
2021-11-11
– 2023-05-25Belief propagation
2014-11-25
– 2023-05-17Maximum Mean Discrepancy
2016-08-21
– 2023-05-03Reparameterization methods for MC gradient estimation
Pathwise gradient estimation,
2018-04-04
– 2023-05-02Normalizing flows
Invertible density models, sounding clever by using the word diffeomorphism like a real mathematician
2018-04-04
– 2023-05-02Data summarization
On maps drawn at smaller than 1:1 scale
2019-01-14
– 2023-04-27Neural vector embeddings
2017-12-20
– 2023-04-21Expectation propagation
Generalized moment matching
2015-10-26
– 2023-04-03Transport maps
Inference by measure transport, low-dimensional coupling…
2018-04-04
– 2023-02-21Generative flow nets
Gflownets
2021-11-11
– 2023-02-13Variational 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
– 2023-01-09Probability divergences
Metrics, contrasts and divergences and other ways of quantifying how similar are two randomnesses
2014-11-25
– 2023-01-06Transforms of Gaussian noise
Delta method, error propagation, unscented transform, Taylor expansion…
2014-11-25
– 2022-12-23Density ratio tricks
2022-12-06Randomised linear algebra
2016-08-16
– 2022-10-22Score matching
2021-11-11
– 2022-09-23Gaussian belief propagation
Least squares at maximal elaboration
2014-11-25
– 2022-09-01Elliptical 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
– 2022-07-27Generalized 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
– 2022-04-08Variational inference
On fitting something not too far from a pretty good model that is not too hard
2016-03-22
– 2022-02-10Gaussian 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
His eyes are like angels but his heart is cold / No need to ask / He’s a Stein operator
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
– 2020-10-26Variational 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
2020-04-22Learnable indexes and hashes
2018-01-12
– 2020-02-18Nearly 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