generativeHamiltonian and Langevin Monte Carlo Physics might be on to something 2018-07-31 – 2022-11-14Neural 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 – 2022-09-23Gaussian process inference by partial updates 2020-12-03 – 2022-09-22Posterior Gaussian process samples by updating prior samples Matheron’s other weird trick 2020-12-03 – 2022-09-20Gaussian process regression And classification. And extensions. 2019-12-03 – 2022-09-20Neural process regression 2019-12-03 – 2022-09-20Gaussian belief propagation Least squares at maximal elaboration 2014-11-25 – 2022-09-01Elliptical belief propagation Generalized least generalized squares 2022-08-22 – 2022-08-23Bayes linear methods some kind of approximate Bayes thing 2022-08-17 – 2022-08-18Gaussian process regression software And classification. 2019-12-03 – 2022-07-29Bayes linear regression and basis-functions in Gaussian process regression a.k.a Fixed Rank Kriging, weight space GPs 2022-02-22 – 2022-07-27Bayes for beginners 2016-05-30 – 2022-07-23Markov Chain Monte Carlo methods 2017-08-28 – 2022-06-08Generalized Bayesian Computation 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 – 2022-04-08Bayesian nonparametric statistics Updating more dimensions than datapoints 2016-05-30 – 2022-04-07Belief propagation 2014-11-25 – 2022-03-31Generative flow 2022-03-07Learning Gaussian processes which map functions to functions 2020-12-07 – 2022-02-25Probabilistic programming Doing statistics using the tools of computer science 2019-10-02 – 2022-02-11Pyro Approximate maximum in the density of probabilistic programming effort 2019-10-02 – 2021-11-25Gaussian Processes as stochastic differential equations Imposing time on things 2019-09-18 – 2021-11-25Deep generative models 2020-12-10 – 2021-11-11Energy based models Inference with kinda-tractable un-normalized densities 2021-06-07Deep Gaussian process regression 2021-05-13Generative adversarial networks 2016-10-07 – 2020-12-14Efficient factoring of GP likelihoods 2020-10-16 – 2020-10-26Variational autoencoders 2019-11-04 – 2020-09-10Defining dynamics via Gaussian processes 2019-09-18
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
Posterior Gaussian process samples by updating prior samples Matheron’s other weird trick 2020-12-03 – 2022-09-20
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
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
Probabilistic programming Doing statistics using the tools of computer science 2019-10-02 – 2022-02-11
Gaussian Processes as stochastic differential equations Imposing time on things 2019-09-18 – 2021-11-25