time_series

Audio/music corpora Smells like Team Audioset 2014-08-08 – 2023-12-06

Voice transcriptions and speech recognition 2019-01-08 – 2023-12-05

System identification in continuous time Learning in continuous ODEs, SDEs and CDEs 2016-08-01 – 2023-11-30

Neural process regression 2019-12-03 – 2023-11-28

Simulation-based inference 2014-12-23 – 2023-11-23

Machine learning for partial differential equations 2017-05-15 – 2023-11-17

Calibration of probabilistic forecasts Proper scoring rules, skill scores etc 2015-06-16 – 2023-11-15

Feedback system identification, not necessarily linear Learning dynamics from data 2016-08-01 – 2023-11-15

Physics-informed neural networks 2019-10-15 – 2023-10-19

Neural PDE operator learning Especially forward operators. Image-to-image regression, where the images encode a physical process. 2019-10-15 – 2023-10-19

Probabilistic numerics 2023-07-13 – 2023-09-25

Machine learning for climate systems 2020-04-02 – 2023-09-25

Orthonormal and unitary matrices Energy preserving operators, generalized rotations 2019-10-22 – 2023-09-19

Code generation, programming assistants 2021-10-14 – 2023-08-28

Generalised autoregressive processes 2022-01-10 – 2023-08-11

Materials informatics Machine learning in condensed matter physics, chemistry and materials science 2023-08-01 – 2023-08-08

Non-uniform signal sampling Discrete sample representation of continuous signals without a grid 2019-01-08 – 2023-08-08

Gaussian process regression And classification. And extensions. 2019-12-03 – 2023-07-28

Python spatial statistics 2020-09-11 – 2023-07-26

Geoscience 2020-09-11 – 2023-07-19

Rough path theory and signature methods 2021-04-02 – 2023-06-29

Decaying sinusoid dictionaries 2019-01-07 – 2023-06-15

Noise contrastive estimation Also “negative sampling”. 2020-04-22 – 2023-06-06

Bayes functional regression 2019-12-03 – 2023-05-25

Non-Gaussian Bayesian functional regression 2019-10-10 – 2023-05-25

Recurrent / convolutional / state-space Translating between means of approximating time series dynamics 2016-04-05 – 2023-05-24

State filtering for hidden Markov models Kalman and friends 2015-06-22 – 2023-05-24

Neural learning dynamical systems 2018-08-13 – 2023-05-23

Belief propagation 2014-11-25 – 2023-05-17

Method of Adjoints for differentiating through ODEs 2017-09-15 – 2023-05-15

Multi-objective optimisation 2021-07-14 – 2023-05-04

Recursive identification Learning forward dynamics by looking at time series. 2017-09-15 – 2023-04-16

Particle filters incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names 2014-07-25 – 2023-03-24

Transformer networks The transformer-powered subtitle for this article is “Our most terrifyingly effective weapon against the forces of evil is our ability to laugh at them.” 2017-12-20 – 2023-03-22

Ensemble Kalman methods Data Assimilation; Data fusion; Sloppy filters for over-ambitious models 2015-06-22 – 2023-03-18

Variational message-passing algorithms in graphical models Cleaving reality at the joint, then summing it at the marginal 2014-11-25 – 2023-01-12

Transforms of Gaussian noise Delta method, error propagation, unscented transform, Taylor expansion… 2014-11-25 – 2022-12-23

Machine learning for physical sciences Turbulent mixing at the boundary between disciplines with differing inertia and viscosity 2017-05-15 – 2022-12-07

COVID-19 in practice SARS-CoV-2 to its friends 2020-11-25 – 2022-12-07

Density ratio tricks 2022-12-06

Online learning 2018-09-30 – 2022-10-20

Forecasting Vegan haruspicy 2015-06-16 – 2022-10-08

The interpretation of RV densities as point process intensities and vice versa Point process of observations ↔ observation of a point process 2016-09-13 – 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

Conditional expectation and probability 2020-02-04 – 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 belief propagation Least squares at maximal elaboration 2014-11-25 – 2022-09-01

State space reconstruction 2014-10-13 – 2022-08-30

Factorial hidden Markov models 2022-08-29

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

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

Stochastic signal sampling Discrete sample representation of continuous stochastic processes 2017-05-30 – 2022-06-24

Signal sampling Discrete representation of continuous signals and converse 2017-05-30 – 2022-06-24

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

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

Nested sampling 2022-05-16

System identification using particle filters A.k.a. parameter estimation in data assimilation 2014-07-25 – 2022-05-04

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

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

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

Beta Processes 2019-10-14 – 2022-04-08

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

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

Simulating Gaussian processes 2022-03-17

Survival analysis and reliability Hazard rates, proportional hazard regression, life testing, mean time to failure 2019-03-12 – 2022-03-07

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

Gaussian processes on lattices 2019-10-30 – 2022-02-28

OODA loops 2021-05-01 – 2022-02-28

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

Ergodicity and mixing Things that probably happen eventually on average 2011-10-17 – 2022-02-13

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

Feedback system identification, linear 2016-07-27 – 2022-01-21

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

Variational state filtering 2018-03-19 – 2021-12-08

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

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

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

t-processes, t-distributions 2021-11-10 – 2021-11-24

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

Approximate Bayesian Computation Posterior updates without likelihood 2020-08-25 – 2021-09-20

Recurrent neural networks 2016-06-16 – 2021-09-06

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

Essays in stochastic processes My PhD thesis with Zdravko I. Botev 2021-08-13

Cascade models a.k.a. cluster distributions, Galton-Watson models 2019-10-11 – 2021-08-04

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

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

Voice fakes 2018-09-06 – 2021-06-17

Stochastic Taylor expansion Polynomial approximations of small randomnesses, Itô’s lemma 2020-10-15 – 2021-05-14

Deep Gaussian process regression 2021-05-13

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

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

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

Dynamical systems via Koopman operators Composition operators, Dynamic Extended Mode decompositions… 2020-10-13 – 2021-04-09

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

Feynman-Kac formulae 2021-01-27

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

Observability and sensitivity in learning dynamical systems Parameter identifiability in dynamical models 2020-11-09

Time Certain quirks of entropy 2020-10-20

Splitting simulation 2017-05-29 – 2020-09-28

Filter design, linear Especially digital 2017-07-24 – 2020-09-18

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

Functional regression 2016-01-05 – 2020-05-28

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

Queueing The mathematical field whose major result is enraging you about call centres 2015-06-03 – 2020-04-06

Epidemics 2020-03-10 – 2020-04-03

Effective sample size 2016-11-21 – 2020-03-03

Cepstral transforms and harmonic identification 2017-09-12 – 2020-02-13

Random change of time Stochastic processes derived by varying the rate of time’s passage, which is more convenient than I imagined 2015-08-05 – 2020-02-10

Branching processes 2014-08-18 – 2020-02-07

Hawkes processes 2019-12-22

Cherchez la martingale Stuff about probability and orthogonality 2019-11-25 – 2019-11-30

Optimal control 2015-06-22 – 2019-11-01

Delays and reverbs for audio processing …ing …ing …ing 2015-11-11 – 2019-10-22

Generalized Galton-Watson processes 2014-12-28 – 2019-10-11

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

Correlograms Also covariances 2018-08-08 – 2019-09-22

Defining dynamics via Gaussian processes 2019-09-18

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

Signal processing That which you study for 4 years in order to design trippy music visualisers 2015-03-18 – 2018-01-05

Warping and registration problems Matching up of bumps and wibbles in stretchy things 2016-08-17 – 2017-07-23

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

Thermodynamics of life 2014-09-23 – 2017-05-30

Granger causation/Transfer Entropy 2012-07-26 – 2017-05-04

Fractional Brownian motion 2017-02-18

Change of probability measure 2015-08-05 – 2016-08-16

Dynamical systems 2016-04-26 – 2016-07-27

Count time series models 2015-06-03 – 2015-12-09

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

Sigma algebras, probability spaces, measure theory The scaffolding of randomness 2015-06-20

Artificial chemistry 2011-12-13 – 2015-05-31

Sparse regression for inhomogeneous Hawkes processes My MSc thesis with Professors Didier Sornette and Sara van de Geer 2015-04-28 – 2015-05-12