# stochastic_processes

System identification in continuous time
Learning in continuous ODEs, SDEs and CDEs
2016-08-01
– 2023-11-30Neural process regression
2019-12-03
– 2023-11-28Machine learning for partial differential equations
2017-05-15
– 2023-11-17Calibration of probabilistic forecasts
Proper scoring rules, skill scores etc
2015-06-16
– 2023-11-15Feedback system identification, not necessarily linear
Learning dynamics from data
2016-08-01
– 2023-11-15Physics-informed neural networks
2019-10-15
– 2023-10-19Neural PDE operator learning
Especially forward operators. Image-to-image regression, where the images encode a physical process.
2019-10-15
– 2023-10-19Gradient flows
infinitesimal optimization
2020-01-30
– 2023-09-28Probabilistic numerics
2023-07-13
– 2023-09-25Machine learning for climate systems
2020-04-02
– 2023-09-25Orthonormal and unitary matrices
Energy preserving operators, generalized rotations
2019-10-22
– 2023-09-19Annealing in inference
Tempering, cooling, Platt scaling…
2020-09-30
– 2023-09-04Potential theory in probability
Something about harmonic functions or whatever
2020-02-12
– 2023-08-24Generalised autoregressive processes
2022-01-10
– 2023-08-11Materials informatics
Machine learning in condensed matter physics, chemistry and materials science
2023-08-01
– 2023-08-08Gaussian process regression
And classification. And extensions.
2019-12-03
– 2023-07-28Geoscience
2020-09-11
– 2023-07-19Reinforcement learning
2014-11-27
– 2023-07-17Rough path theory and signature methods
2021-04-02
– 2023-06-29Neural nets with implicit layers
Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem
2020-12-08
– 2023-06-28Statistical mechanics of statistics
2016-12-01
– 2023-06-02Bayes functional regression
2019-12-03
– 2023-05-25Non-Gaussian Bayesian functional regression
2019-10-10
– 2023-05-25Neural learning dynamical systems
2018-08-13
– 2023-05-23Gradient descent, first-order, stochastic
a.k.a. SGD, as seen in deep learning
2020-01-30
– 2023-05-19Belief propagation
2014-11-25
– 2023-05-17Differentiable PDE solvers
2017-05-15
– 2023-05-15Conformal prediction
2016-12-26
– 2023-05-05Multi-objective optimisation
2021-07-14
– 2023-05-04Combining kernels
2019-09-16
– 2023-05-02ΦFlow
A modern python computational fluid dynamics library for ML research
2022-06-24
– 2023-04-17Symbolic regression
2023-03-14Variational message-passing algorithms in graphical models
Cleaving reality at the joint, then summing it at the marginal
2014-11-25
– 2023-01-12Transforms of Gaussian noise
Delta method, error propagation, unscented transform, Taylor expansion…
2014-11-25
– 2022-12-23Machine learning for physical sciences
Turbulent mixing at the boundary between disciplines with differing inertia and viscosity
2017-05-15
– 2022-12-07COVID-19 in practice
SARS-CoV-2 to its friends
2020-11-25
– 2022-12-07Deep learning as a dynamical system
2018-08-13
– 2022-10-30The edge of chaos
Computation, evolution, competition and other past-times of faculty
2016-12-01
– 2022-10-30Neural tangent kernel
2020-12-09
– 2022-10-14Forecasting
Vegan haruspicy
2015-06-16
– 2022-10-08Gaussian process inference by partial updates
2020-12-03
– 2022-09-22Generalised Ornstein-Uhlenbeck processes
Markov/AR(1)-like processes
2022-01-10
– 2022-09-21Posterior Gaussian process samples by updating prior samples
Matheron’s other weird trick
2020-12-03
– 2022-09-20Ensemble Kalman methods for training neural networks
Data assimilation for network weights
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
– 2022-08-23Neural nets with implicit layers
Also, declarative networks, bi-level optimization and other ingenious uses of the implicit function theorem
2020-12-08
– 2022-08-09Gaussian process regression software
And classification.
2019-12-03
– 2022-07-29Neural learning for spatiotemporal systems
2020-09-16
– 2022-07-28Gamma distributions
2019-10-14
– 2022-07-27Simulating Gaussian processes on a lattice
2022-03-17
– 2022-07-26Markov decision problems
2014-11-27
– 2022-06-07Overparameterization in large models
Improper learning, benign overfitting, double descent
2018-04-04
– 2022-05-27Stochastic partial differential equations
SDEs taking values in some function space
2021-01-27
– 2022-05-25(Discrete-measure)-valued stochastic processes
2019-10-10
– 2022-05-04Forecasting with model averaging
Mixture of experts, ensembles and time series
2022-05-04Measure-valued stochastic processes
Moving masses
2020-10-16
– 2022-05-03Generalized Bayesian Computation
2019-10-03
– 2022-04-28SLAM
Simultaneous Location and Mapping
2014-11-25
– 2022-04-28Markov bridge processes
Especially Lévy bridges, Doob h-transforms
2019-10-15
– 2022-04-20Divisible, decomposable and stable distributions
Ways of slicing randomness
2017-06-15
– 2022-04-18Beta Processes
2019-10-14
– 2022-04-08Stationary Gamma processes
2019-10-14
– 2022-04-08Particle 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
– 2022-04-07Beta and Dirichlet distributions
2019-10-14
– 2022-04-04Change points
Looking for regime changes in stochastic processes. a.k.a. Switching state space models
2021-11-29
– 2022-04-01Detecting stationarity in stochastic processes
Change-points, trends and transients
2021-11-29
– 2022-04-01Partition-valued random variates
2022-04-01Measure-valued random variates
Including completely random measures and many generalizations
2020-10-16
– 2022-03-30Reservoir Computing
2022-03-28Simulating Gaussian processes
2022-03-17Multivariate Gamma distributions
2019-10-14
– 2022-03-14Wiener-Khintchine representations
Spectral representations of stochastic processes
2019-05-08
– 2022-03-11Matrix- and vector-valued generalizations of Gamma processes
2019-10-14
– 2022-03-03Lévy Gamma processes
2019-10-14
– 2022-03-03Learning Gaussian processes which map functions to functions
2020-12-07
– 2022-02-25Subordinators
Non-decreasing Lévy processes with weird branding
2019-10-14
– 2022-02-24Neural nets with basis decomposition layers
2021-03-09
– 2022-02-01Karhunen-Loève expansions
2019-09-16
– 2022-02-01Running neural nets backwards
2022-01-29Learning on manifolds
Finding the lowest bit of a krazy straw, from the inside
2011-10-21
– 2022-01-26Feedback system identification, linear
2016-07-27
– 2022-01-21Gamma-Beta algebra
2019-10-14
– 2022-01-12Matrix-valued random variates
2021-12-01
– 2022-01-06Gradient descent, Newton-like, stochastic
2020-01-23
– 2021-12-09Convolutional subordinator processes
2021-03-08
– 2021-12-01Multi-output Gaussian process regression
2020-12-02
– 2021-11-26Gaussian Processes as stochastic differential equations
Imposing time on things
2019-09-18
– 2021-11-25t-processes, t-distributions
2021-11-10
– 2021-11-24Lévy processes
2017-05-29
– 2021-11-17Random neural networks
2017-02-17
– 2021-10-12Fractals and self-similarity
2011-11-13
– 2021-09-22Path smoothness properties of stochastic processes
Continuity, differentiability and other smoothness properties
2020-02-26
– 2021-09-02Stochastic calculus
Itô and friends
2019-09-19
– 2021-08-31Vector Gaussian processses
2020-12-02
– 2021-08-16Convolutional stochastic processes
Moving averages of noise
2021-03-01
– 2021-08-16Contact tracing
Reverse engineering social graphs for the control of contagions of pathogens, subversive ideology and other substances of interest
2020-03-21
– 2021-07-28Contagion processes and their statistics
2016-08-30
– 2021-07-15Media virality
Strategic modelling for content creators
2016-08-30
– 2021-07-15Multi-task ML
2021-07-14Uncertainty quantification
2016-12-26
– 2021-07-06Gaussian processes
2016-08-07
– 2021-06-23Backward stochastic differential equations
2019-09-19
– 2021-06-22Stochastic differential equations
2019-09-19
– 2021-06-22Neural net kernels
2019-09-16
– 2021-05-24Transforms of random variates
2020-06-04
– 2021-05-14Stochastic Taylor expansion
Polynomial approximations of small randomnesses, Itô’s lemma
2020-10-15
– 2021-05-14Deep Gaussian process regression
2021-05-13Infinite width limits of neural networks
2020-12-09
– 2021-05-11Spectral factorization
2021-05-05
– 2021-05-07Wiener-Hopf method
Righteous hack for certain integral equations
2021-05-05Path integral formulations of SDEs
Feynman path integrals, esp for stochastic processes
2021-04-19Kernel zoo
2019-09-16
– 2021-03-30Matrix measure concentration inequalities and bounds
2014-11-25
– 2021-03-08Measure concentration inequalities
The fancy name for probability inequalities
2014-11-25
– 2021-03-04Convolutional Gaussian processes
2021-03-01Random fields as stochastic differential equations
Precision vs covariance, fight!
2020-10-12
– 2021-03-01Stochastic processes on manifolds
2021-03-01Learning covariance functions
Learning a family of covariances at once
2019-09-16
– 2021-03-01Chaos expansions
Polynomial chaos, generalized polynomial chaos, arbitrary chaos etc
2020-05-21
– 2021-02-15Miscellaneous nonstationary kernels
2019-09-16
– 2021-01-21Warping of stationary stochastic processes
2019-09-16
– 2021-01-21Covariance functions
Variograms, Mercer kernels, positive definite operators, spare reproducing kernels for that Hilbert space I bought on eBay real cheap
2019-09-16
– 2021-01-05Probabilistic spectral analysis
2019-11-13
– 2020-11-25Bandit problems
Also stochastic control
2014-11-27
– 2020-10-16Monte Carlo optimisation
2020-09-30Statistics of spatio-temporal processes
2020-09-11
– 2020-09-11Long memory time series
2011-11-13
– 2020-05-28Malliavin calculus
2020-05-23
– 2020-05-25Lévy stochastic differential equations
2020-05-23Epidemics
2020-03-10
– 2020-04-03Infinitesimal generators
Generators of the transition semi-group, connection to Kolmogorov forward equations
2017-05-29
– 2020-02-05Poisson point processes
2019-10-14
– 2020-01-29Convergence of random variables
2019-12-03Statistical learning theory for time series
2016-11-03
– 2019-10-01Wiener theorem
Now with bonus Bochner!
2019-05-08Sparse stochastic processes identification and sampling
Discrete sample representation of sparse continuous stochastic processes
2018-11-22
– 2018-10-29Pattern formation
2014-12-23
– 2018-10-09Stochastic processes and fields
Probabilistic structures over index sets and state spaces
2014-08-05
– 2017-06-15Fractional Brownian motion
2017-02-18Dynamical systems
2016-04-26
– 2016-07-27Maximum processes
2016-07-14High frequency time series estimation
2016-06-12
– 2015-12-02