# probabilistic_algorithms

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
– 2022-09-23Bayesian posterior sampling via SGD
One of those times when the easy thing can also be the smart thing
2020-08-17
– 2022-09-21Langevin dynamcs MCMC
2020-08-17
– 2022-09-21Laplace approximations in inference
Lightweight uncertainties, especially for heavy neural nets
2021-07-28
– 2022-09-06Generic variance reduction in Monte Carlo samplers
2022-08-25Monte Carlo gradient estimation
Especially stochastic automatic differentiation
2020-09-30
– 2022-08-18Recommender systems
2020-11-30
– 2022-08-15Integrated Nested Laplace Approximation
2021-07-28
– 2022-07-26Markov Chain Monte Carlo methods
2017-08-28
– 2022-06-08Overparameterization in large models
Improper learning, benign overfitting, double descent
2018-04-04
– 2022-05-27Nested sampling
2022-05-16System identification using particle filters
A.k.a. parameter estimation in data assimilation
2014-07-25
– 2022-05-04Expectation propagation
Generalized moment matching
2015-10-26
– 2022-05-04Particle filters
incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names
2014-07-25
– 2022-04-10Particle 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
– 2022-04-08Change points
Looking for regime changes in stochastic processes. a.k.a. Switching state space models
2021-11-29
– 2022-04-01Pólya-Gamma augmentation trick
2017-02-20
– 2022-04-01Reservoir Computing
2022-03-28Genetic programming
2015-12-22
– 2022-03-09Generative flow
2022-03-07Variational inference
On fitting something not too far from a pretty good model that is not too hard
2016-03-22
– 2022-02-10Bootstrap
Shuffling reality to produce your data
2014-11-26
– 2022-01-27Reparameterization tricks in inference
Pathwise gradient estimation, nNormalizing flows, invertible density models, inference by measure transport, low-dimensional coupling…
2018-04-04
– 2021-12-21Deep generative models
2020-12-10
– 2021-11-11Random neural networks
2017-02-17
– 2021-10-12Approximate Bayesian Computation
Posterior updates without likelihood
2020-08-25
– 2021-09-20Monte Carlo methods
2014-11-16
– 2021-09-02Learning on tabular data
2020-11-30
– 2021-06-21Energy based models
Inference with kinda-tractable un-normalized densities
2021-06-07Neural net kernels
2019-09-16
– 2021-05-24Infinite width limits of neural networks
2020-12-09
– 2021-05-11Log concave distributions
associated tools
2017-08-28
– 2021-03-11Feynman-Kac formulae
2021-01-27Random embeddings and hashing
2016-12-05
– 2020-12-01Randomised regression
2017-01-13
– 2020-12-01Markov Chain Monte Carlo methods
2020-06-28
– 2020-10-28Monte Carlo optimisation
2020-09-30Splitting simulation
2017-05-29
– 2020-09-28Data summarization
On maps drawn at smaller than 1:1 scale
2019-01-14
– 2020-09-18Variational autoencoders
2019-11-04
– 2020-09-10Combinatorics of note
2020-07-18Variational inference
On fitting the best model one can be bothered to
2016-03-22
– 2020-05-24Adaptive Markov Chain Monte Carlo samplers
2020-04-28
– 2020-04-30Tuning an MCMC sampler
2020-04-30
– 2020-04-30The cross entropy method
2020-04-24Kernel approximation
2016-07-27
– 2020-03-06Bias reduction
Estimating the bias of an estimator so as to subtract it off again
2020-02-26Random (element) matrix theory
2014-11-09
– 2019-10-10Nearly sufficient statistics
How about “Sufficient sufficiency”? — is that taken?
2018-03-13
– 2019-01-14Rare-event-conditional estimation
2017-05-29
– 2017-11-10Compressed sensing and sampling
A fancy ways of counting zero
2014-08-18
– 2017-06-14Quasi Monte Carlo
2015-09-01
– 2017-02-14Randomised linear algebra
2016-08-16Expectation maximisation
2014-08-17
– 2016-04-17Biomimetic algorithms
2015-12-22Random number generation
2015-05-14
– 2015-10-13