stochastic_processes

Conformal prediction 2016-12-26 – 2022-09-24

Gaussian process inference by partial updates 2020-12-03 – 2022-09-22

Generalised Ornstein-Uhlenbeck processes Markov/AR(1)-like processes 2022-01-10 – 2022-09-21

Posterior Gaussian process samples by updating prior samples Matheron’s other weird trick 2020-12-03 – 2022-09-20

Ensemble Kalman methods for training neural networks Data assimilation for network weights 2022-09-20

Gaussian process regression And classification. And extensions. 2019-12-03 – 2022-09-20

Neural process regression 2019-12-03 – 2022-09-20

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

Elliptical belief propagation Generalized least generalized squares 2022-08-22 – 2022-08-23

Neural nets with implicit layers Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem 2020-12-08 – 2022-08-09

Nonparametrically learning dynamical systems 2018-08-13 – 2022-08-06

Gaussian process regression software And classification. 2019-12-03 – 2022-07-29

Neural learning for spatiotemporal systems 2020-09-16 – 2022-07-28

Gamma distributions 2019-10-14 – 2022-07-27

Simulating Gaussian processes on a lattice 2022-03-17 – 2022-07-26

Markov decision problems 2014-11-27 – 2022-06-07

Overparameterization in large models Improper learning, benign overfitting, double descent 2018-04-04 – 2022-05-27

Stochastic partial differential equations SDEs taking values in some function space 2021-01-27 – 2022-05-25

Forecasting Vegan haruspicy 2015-06-16 – 2022-05-11

(Discrete-measure)-valued stochastic processes 2019-10-10 – 2022-05-04

Forecasting with model averaging Mixture of experts, ensembles and time series 2022-05-04

Generalized Bayesian Computation 2019-10-03 – 2022-04-28

SLAM Simultaneous Location and Mapping 2014-11-25 – 2022-04-28

Markov bridge processes Especially Lévy bridges 2019-10-15 – 2022-04-20

Divisible, decomposable and stable distributions Ways of slicing randomness 2017-06-15 – 2022-04-18

Stationary Gamma processes 2019-10-14 – 2022-04-08

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

Bayesian nonparametric statistics Updating more dimensions than datapoints 2016-05-30 – 2022-04-07

Orthonormal and unitary matrices Energy preserving operators, generalized rotations 2019-10-22 – 2022-04-05

Beta and Dirichlet distributions 2019-10-14 – 2022-04-04

Change points Looking for regime changes in stochastic processes. a.k.a. Switching state space models 2021-11-29 – 2022-04-01

Detecting stationarity in stochastic processes Change-points, trends and transients 2021-11-29 – 2022-04-01

Partition-valued random variates 2022-04-01

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

Measure-valued random variates Including completely random measures and many generalizations 2020-10-16 – 2022-03-30

Reservoir Computing 2022-03-28

Simulating Gaussian processes 2022-03-17

Multivariate Gamma distributions 2019-10-14 – 2022-03-14

COVID-19 in practice SARS-CoV-2 to its friends 2020-11-25 – 2022-03-13

Wiener-Khintchine representations Spectral representations of stochastic processes 2019-05-08 – 2022-03-11

Generative flow 2022-03-07

Matrix- and vector-valued generalizations of Gamma processes 2019-10-14 – 2022-03-03

Lévy Gamma processes 2019-10-14 – 2022-03-03

Learning Gaussian processes which map functions to functions 2020-12-07 – 2022-02-25

Subordinators Non-decreasing Lévy processes with weird branding 2019-10-14 – 2022-02-24

Deep learning as a dynamical system 2018-08-13 – 2022-02-22

System identification in continuous time Learning in continuous ODEs, SDEs and CDEs 2016-08-01 – 2022-02-15

Neural nets with basis decomposition layers 2021-03-09 – 2022-02-01

Karhunen-Loève expansions 2019-09-16 – 2022-02-01

Running neural nets backwards 2022-01-29

Learning on manifolds Finding the lowest bit of a krazy straw, from the inside 2011-10-21 – 2022-01-26

Gamma-Beta algebra 2019-10-14 – 2022-01-12

Generalised autoregressive processes 2022-01-10

Matrix-valued random variates 2021-12-01 – 2022-01-06

Convolutional subordinator processes 2021-03-08 – 2021-12-01

Multi-output Gaussian process regression 2020-12-02 – 2021-11-26

Combining kernels 2019-09-16 – 2021-11-25

Gaussian Processes as stochastic differential equations Imposing time on things 2019-09-18 – 2021-11-25

t-processes 2021-11-10 – 2021-11-24

Lévy processes 2017-05-29 – 2021-11-17

Random neural networks 2017-02-17 – 2021-10-12

Fractals and self-similarity 2011-11-13 – 2021-09-22

Path smoothness properties of stochastic processes Continuity, differentiability and other smoothness properties 2020-02-26 – 2021-09-02

Stochastic calculus Itô and friends 2019-09-19 – 2021-08-31

Vector Gaussian processses 2020-12-02 – 2021-08-16

Convolutional stochastic processes Moving averages of noise 2021-03-01 – 2021-08-16

Rough path theory Also, signatures. 2021-04-02 – 2021-08-05

Contact tracing Reverse engineering social graphs for the control of contagions of pathogens, subversive ideology and other substances of interest 2020-03-21 – 2021-07-28

Contagion processes and their statistics 2016-08-30 – 2021-07-15

Media virality Strategic modelling for content creators 2016-08-30 – 2021-07-15

Multi-task ML 2021-07-14

Uncertainty quantification 2016-12-26 – 2021-07-06

Gaussian processes 2016-08-07 – 2021-06-23

Backward stochastic differential equations 2019-09-19 – 2021-06-22

Stochastic differential equations 2019-09-19 – 2021-06-22

Neural net kernels 2019-09-16 – 2021-05-24

Transforms of random variates 2020-06-04 – 2021-05-14

Stochastic Taylor expansion Polynomial approximations of small randomnesses 2020-10-15 – 2021-05-14

Deep Gaussian process regression 2021-05-13

Infinite width limits of neural networks 2020-12-09 – 2021-05-11

Spectral factorization 2021-05-05 – 2021-05-07

Wiener-Hopf method Righteous hack for certain integral equations 2021-05-05

Path integral formulations Feynman path integrals, esp for stochastic processes 2021-04-19

Kernel zoo 2019-09-16 – 2021-03-30

Matrix measure concentration inequalities and bounds 2014-11-25 – 2021-03-08

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

Convolutional Gaussian processes 2021-03-01

Random fields as stochastic differential equations Precision vs covariance, fight! 2020-10-12 – 2021-03-01

Stochastic processes on manifolds 2021-03-01

Learning covariance functions Learning a family of covariances at once 2019-09-16 – 2021-03-01

Causal inference in the continuous limit 2021-02-17

Chaos expansions Polynomial chaos, generalized polynomial chaos, arbitrary chaos etc 2020-05-21 – 2021-02-15

Miscellaneous nonstationary kernels 2019-09-16 – 2021-01-21

Warping of stationary stochastic processes 2019-09-16 – 2021-01-21

Statistical mechanics of statistics 2016-12-01 – 2021-01-06

Covariance functions Variograms, Mercer kernels, positive definite operators, spare reproducing kernels for that Hilbert space I bought on eBay real cheap 2019-09-16 – 2021-01-05

Probabilistic spectral analysis 2019-11-13 – 2020-11-25

Bandit problems Also reinforcement learning and stochastic control 2014-11-27 – 2020-10-16

Non-Gaussian Bayesian functional regression 2019-10-10 – 2020-09-16

Gaussian process quantile regression 2020-09-16

Statistics of spatio-temporal processes 2020-09-11 – 2020-09-11

Long memory time series 2011-11-13 – 2020-05-28

Malliavin calculus 2020-05-23 – 2020-05-25

Lévy stochastic differential equations 2020-05-23

Epidemics 2020-03-10 – 2020-04-03

Potential theory in probability Something about harmonic functions or whatever 2020-02-12

Infinitesimal generators Generators of the transition semi-group, connection to Kolmogorov forward equations 2017-05-29 – 2020-02-05

Poisson processes 2019-10-14 – 2020-01-29

Convergence of random variables 2019-12-03

Statistical learning theory for time series 2016-11-03 – 2019-10-01

Wiener theorem Now with bonus Bochner! 2019-05-08

Sparse stochastic processes identification and sampling Discrete sample representation of sparse continuous stochastic processes 2018-11-22 – 2018-10-29

Pattern formation 2014-12-23 – 2018-10-09

Stochastic processes and fields Probabilistic structures over index sets and state spaces 2014-08-05 – 2017-06-15

Fractional Brownian motion 2017-02-18

Maximum processes 2016-07-14

High frequency time series estimation 2016-06-12 – 2015-12-02