probability

Learning graphical models from time series Also, causal discovery, structure discovery in time series 2017-09-20 – 2023-11-28

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

Quantization 2016-03-29 – 2023-11-02

Non-negative matrix factorisation 2019-10-14 – 2023-10-26

Causal inference in highly parameterized ML 2020-09-18 – 2023-10-24

Learning under distribution shift Also transfer learning, covariate shift, transferable learning, domain adaptation, transportability etc 2020-10-17 – 2023-10-24

Wicked tail risks x-risk, y-risk, jackpot, die-risk, 2015-04-30 – 2023-10-11

Brain-like neuronal computation 2015-12-22 – 2023-10-03

Low-rank matrices 2014-08-05 – 2023-09-29

Matrix inverses 2014-08-05 – 2023-09-29

Statistics and machine learning 2011-04-15 – 2023-09-13

Nearly-low-rank Hermitian matrices a.k.a. perturbations of the identity, low-rank-plus-diagonal matrices 2014-08-05 – 2023-09-13

Random number generation 2015-05-14 – 2023-09-08

Annealing in inference Tempering, cooling, Platt scaling… 2020-09-30 – 2023-09-04

Monte Carlo methods 2014-11-16 – 2023-08-26

Potential theory in probability Something about harmonic functions or whatever 2020-02-12 – 2023-08-24

Approximate matrix factorisations and decompositions Sometimes even exact 2014-08-05 – 2023-08-23

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

Large sample theory 2015-02-15 – 2023-08-02

Gradient steps to an ecology of mind The best as enemy of the good 2011-11-27 – 2023-08-02

Ablation studies and lesion studies In order to understand it we must be able to break it 2016-10-26 – 2023-08-01

Factor graphs 2019-12-16 – 2023-07-31

The Gaussian distribution The default probability distribution 2016-06-27 – 2023-07-19

Gaussian process ensembles Bayesian committee machines, product-of-experts 2023-07-12

Exponential families 2016-04-19 – 2023-06-28

Contrastive learning 2020-04-22 – 2023-06-06

Tabular data processing in python CSV to Data lake 2020-11-30 – 2023-05-29

Matrix norms, divergences, metrics 2016-06-03 – 2023-05-29

Neural denoising diffusion models Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models… 2021-11-11 – 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

Singular Value Decomposition The ML workhorse 2014-08-05 – 2023-05-23

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

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

Matrix square roots Whitening, preconditioning etc 2014-08-05 – 2023-05-13

Maximum Mean Discrepancy 2016-08-21 – 2023-05-03

Distances between Gaussian distributions Nearly equivalent to distances between symmetric positive definite matrices 2016-06-27 – 2023-05-03

Reparameterization methods for MC gradient estimation Pathwise gradient estimation, 2018-04-04 – 2023-05-02

Normalizing flows Invertible density models, sounding clever by using the word diffeomorphism like a real mathematician 2018-04-04 – 2023-05-02

Monte Carlo gradient estimation 2020-09-30 – 2023-05-02

Bayesian posterior inference via optimisation Conditioning by gradient 2020-08-17 – 2023-04-28

Data summarization On maps drawn at smaller than 1:1 scale 2019-01-14 – 2023-04-27

Quantified self Instrumentation and analytics for body and soul. Punk DIY precision medicine. 2022-01-11 – 2023-04-24

Model order reduction 2015-03-22 – 2023-04-21

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

Leakage in predictive models 2016-09-05 – 2023-04-12

Optimal rotations 2021-05-18 – 2023-04-06

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

Deep sets invariant and equivariant functions 2022-11-24 – 2023-03-21

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

Transport maps Inference by measure transport, low-dimensional coupling… 2018-04-04 – 2023-02-21

External validity When does what I learn on one data set apply to another? 2020-10-17 – 2023-02-16

The Matrix-Gaussian distribution 2022-08-19 – 2023-02-16

Generative flow nets Gflownets 2021-11-11 – 2023-02-13

Predictive coding Does the model that our brains do bayesian variational prediction make any actual predictions about our brains? 2011-11-27 – 2023-02-09

Visualising probabilistic graphical models Also related models, such as Neural nets 2018-03-29 – 2023-02-02

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

(Kernelized) Stein variational gradient descent KSVD, SVGD 2022-11-02 – 2023-01-09

Probability divergences Metrics, contrasts and divergences and other ways of quantifying how similar are two randomnesses 2014-11-25 – 2023-01-06

Elliptical distributions 2015-06-23 – 2023-01-03

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

Mind as statistical learner 2020-06-23 – 2022-12-06

Density ratio tricks 2022-12-06

Adverse advice selection 2022-01-25 – 2022-11-15

Interaction effects and subgroups are probably what we want to estimate 2022-01-25 – 2022-11-06

Personalized medicine Precision medicine 2022-01-25 – 2022-11-06

Casual anthropic principles Convenience-sampling lived human experience 2020-02-15 – 2022-10-28

Randomised linear algebra 2016-08-16 – 2022-10-22

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

Anomaly detection I don’t define what is abnormal, but I know it when I see it 2015-10-06 – 2022-10-20

Continuous and equilibrium probabilistic graphical models 2014-08-05 – 2022-10-10

Multi level marketing Pyramid schemes, Ponzi schemes, Newcomb unboxing 2022-10-02 – 2022-10-02

Learning graphical models from data Also, causal discovery, structure discovery 2017-09-20 – 2022-10-01

Langevin dynamcs MCMC 2020-08-17 – 2022-10-01

Bayesian inverse problems in function space a.k.a. Bayesian calibration, model uncertainty for PDEs and other wibbly, blobby things 2020-10-13 – 2022-09-26

Physical infrastructure 2022-09-24

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

Score matching 2021-11-11 – 2022-09-23

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

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

Penalised/regularised regression 2016-06-23 – 2022-09-19

Laplace approximations in inference Lightweight uncertainties, especially for heavy neural nets 2021-07-28 – 2022-09-06

Gaussian belief propagation Least squares at maximal elaboration 2014-11-25 – 2022-09-01

Distributional robustness in inference 2019-07-12 – 2022-08-30

Factorial hidden Markov models 2022-08-29

Generic variance reduction in Monte Carlo samplers 2022-08-25

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

Recommender systems 2020-11-30 – 2022-08-15

Causal inference on DAGs Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next 2016-10-26 – 2022-08-06

Instumental variables and two stage regression Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next 2016-10-26 – 2022-08-06

ELBO Evidence lower bound, variational free energy etc 2020-10-02 – 2022-08-03

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

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

Integrated Nested Laplace Approximation 2021-07-28 – 2022-07-26

Empirical mode decomposition Multiplying your exposure to uncertainty principles 2018-01-06 – 2022-07-12

Inverse problems 2016-03-30 – 2022-06-30

Betting 2017-05-09 – 2022-06-27

Markov Chain Monte Carlo methods 2017-08-28 – 2022-06-08

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

Emergent spacetime What are qubits again? 2022-05-26

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

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

Measure-valued stochastic processes Moving masses 2020-10-16 – 2022-05-03

Farming and husbandry of black swans and dragon kings Heavy tailed and Knightian uncertainties for fun and profit 2020-09-22 – 2022-04-30

Causal graphical model reading group 2022 Causal inference 2022-04-01 – 2022-04-29

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

Inference without KL divergence 2019-10-03 – 2022-04-28

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

Vecchia factoring of GP likelihoods Ignore some conditioning in the dependencies and attain a sparse cholesky factor for the precision matrix 2022-04-27

Hierarchical models DAGs, multilevel models, random coefficient models, mixed effect models, structural equation models… 2015-06-07 – 2022-04-21

Markov bridge processes Especially Lévy bridges, Doob h-transforms 2019-10-15 – 2022-04-20

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

Gumbel (soft) max tricks Concrete distribution, relaxed categorical etc 2017-02-20 – 2022-04-01

Pólya-Gamma augmentation trick 2017-02-20 – 2022-04-01

Partition-valued random variates 2022-04-01

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

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

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

Fun with rotational symmetries 2021-01-29 – 2022-03-03

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

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

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

Learning with conservation laws, invariances and symmetries 2020-04-11 – 2022-02-25

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

Stability in dynamical systems Lyapunov exponents and ilk 2019-05-21 – 2022-02-22

Politics as statistical learner 2022-01-31 – 2022-02-17

Betting and prediction markets Market design for good guesses 2017-08-12 – 2022-02-15

Experimental ethics and observational data 2011-04-14 – 2022-02-11

Variational inference On fitting something not too far from a pretty good model that is not too hard 2016-03-22 – 2022-02-10

Probabilistic graphical models 2014-08-05 – 2022-02-08

Random graphical models Causality in amongst confusion 2021-10-26 – 2022-02-07

Risk perception and communication 2011-04-14 – 2022-01-17

Bayesian inverse problems 2016-03-30 – 2022-01-13

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

Categorical random variates 2017-02-20 – 2022-01-12

Statistical projectivity 2020-04-26 – 2022-01-11

Mind as statistical learner 2022-01-09

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

Time frequency analysis Multiplying your exposure to uncertainty principles 2018-01-06 – 2021-12-21

Causality via potential outcomes Neyman-Rubin, counterfactuals, conditional treatment effects, and related tricks 2016-10-26 – 2021-12-10

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

Random rotations 2021-05-18 – 2021-12-01

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

Trading, hedging and portfolios in practice 2017-05-09 – 2021-11-18

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

Deep generative models 2020-12-10 – 2021-11-11

High dimensional statistics 2015-03-12 – 2021-10-28

Neural music synthesis 2016-01-15 – 2021-10-14

Missing data Imputation, estimation despite etc 2021-10-07

Science for policy Using evidence and reason to govern ourselves 2011-08-07 – 2021-09-22

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

Heavy tails Weird things about rare massive events 2020-01-13 – 2021-09-18

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

Risk neutral measure 2021-10-15 – 2021-08-08

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

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

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

Learning summary statistics 2020-04-22 – 2021-07-15

Graph sampling Estimating functionals of graphs 2020-02-15 – 2021-07-06

Extreme value theory On the decay of awfulness with oftenness 2020-01-13 – 2021-06-30

Algebraic probability If you liked it then you prob’ly put a ring on it 2017-06-15 – 2021-06-24

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

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

Learning on tabular data 2020-11-30 – 2021-06-21

Optimal transport metrics Wasserstein distances, Monge-Kantorovich metrics, Earthmover distances 2019-05-30 – 2021-06-08

Energy based models Inference with kinda-tractable un-normalized densities 2021-06-07

Stein’s method His eyes are like angels but his heart is cold / No need to ask / He’s a Stein operator 2021-03-12 – 2021-06-01

Isotropic random vectors 2011-08-10 – 2021-05-24

Randomized low dimensional projections 2021-03-12 – 2021-05-24

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

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

Cross validation 2016-09-05 – 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

Prediction processes Some kind of weird time series formalism 2021-04-09

Generically approximating probability distributions 2021-03-12 – 2021-03-22

Log concave distributions associated tools 2017-08-28 – 2021-03-11

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

Measure concentration inequalities The fancy name for probability inequalities 2014-11-25 – 2021-03-04

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

Frames and Riesz bases Generalisations of orthogonal bases 2017-06-12 – 2021-02-24

Stability in linear dynamical systems This Bodes well 2019-07-19 – 2021-02-16

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

Feynman-Kac formulae 2021-01-27

Generative adversarial networks 2016-10-07 – 2020-12-14

Random embeddings and hashing 2016-12-05 – 2020-12-01

Randomised regression 2017-01-13 – 2020-12-01

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

Weighted data in statistics 2020-11-04 – 2020-11-06

Markov Chain Monte Carlo methods 2020-06-28 – 2020-10-28

Efficient factoring of GP likelihoods 2020-10-16 – 2020-10-26

GP inducing features 2020-10-16 – 2020-10-26

GP inducing variables 2020-10-16 – 2020-10-26

Localized Gaussian processes 2020-10-16 – 2020-10-26

Sparse model selection 2016-09-05 – 2020-10-02

Monte Carlo optimisation 2020-09-30

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

Quantitative risk measurement Mathematics of actuarial and financial disaster 2015-04-30 – 2020-09-22

Independence, conditional, statistical 2016-04-21 – 2020-09-13

Data dimensionality reduction Wherein I teach myself, amongst other things, feature selection, how a sparse PCA works, and decide where to file multidimensional scaling 2015-03-22 – 2020-09-11

Variational autoencoders 2019-11-04 – 2020-09-10

Causal graphical model reading group 2020 An introduction to conditional independence DAGs and their use for causal inference. 2020-08-30 – 2020-09-03

Causal Bayesian networks Staged tree models, probability trees …Causalan Bayesian networks 2020-11-01 – 2020-09-01

Emulators and surrogate models via ML Shortcuts in scientific simulation using ML 2020-08-12 – 2020-08-26

Variational inference On fitting the best model one can be bothered to 2016-03-22 – 2020-05-24

Directed graphical models 2017-09-20 – 2020-05-13

Adaptive Markov Chain Monte Carlo samplers 2020-04-28 – 2020-04-30

Tuning an MCMC sampler 2020-04-30 – 2020-04-30

The cross entropy method 2020-04-24

Mixture models for density estimation 2016-03-29 – 2020-04-24

Analysis/resynthesis of audio 2016-01-15 – 2020-04-09

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

Order statistics 2019-02-21 – 2020-03-17

Restricted isometry properties Plus incoherence, irrepresentability, and other uncertainty bounds for a sparse world, and maybe frame theory, what’s that now? 2017-06-12 – 2020-03-09

Kernel approximation 2016-07-27 – 2020-03-06

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

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

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

Hawkes processes 2019-12-22

Convergence of random variables 2019-12-03

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

Audio source separation 2019-11-04 – 2019-11-26

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

Undirected graphical models 2017-09-20 – 2019-10-28

Sparse regression 2016-06-23 – 2019-10-24

Density estimation Especially non- or semiparametrically 2016-06-06 – 2019-10-16

Zeros of random trigonometric polynomials 2019-05-20 – 2019-10-14

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

Random (element) matrix theory 2014-11-09 – 2019-10-10

Defining dynamics via Gaussian processes 2019-09-18

Probability 2011-07-29 – 2019-07-15

Fourier interpolation 2019-06-19

Nearly sufficient statistics How about “Sufficient sufficiency”? — is that taken? 2018-03-13 – 2019-01-14

Rare-event-conditional estimation 2017-05-29 – 2017-11-10

Quantum probability and quantum information, noncommutative probability 2016-10-16 – 2017-09-18

Quantum-probabilistic graphical models 2017-08-07

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

Generating functions Fancy counting 2017-06-19

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

Compressed sensing and sampling A fancy ways of counting zero 2014-08-18 – 2017-06-14

Marketing psychology 2017-04-27 – 2017-05-29

Uncertainty principles 2017-05-14

Fractional Brownian motion 2017-02-18

Metric entropy 2017-02-13

Special functions 2014-07-25 – 2016-12-21

The simplex 2016-10-25

UNSW Stats reading group 2016 - Causal DAGs An introduction to conditional independence DAGs and their use for causal data. 2016-10-17 – 2016-10-21

Inference from disorder 2016-10-19

Maximum likelihood inference 2015-02-15 – 2016-10-13

Statistical learning theory Eventually including structural risk minimisation, risk bounds, hopefully-uniform convergence rates, VC-dimension, generalisation-and-stability framings etc 2016-07-06 – 2016-08-16

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

Blind deconvolution 2015-03-01 – 2016-07-27

Maximum processes 2016-07-14

Clustering 2015-05-22 – 2016-06-07

Curved exponential families 2016-04-19

Expectation maximisation 2014-08-17 – 2016-04-17

Deconvolution 2015-04-19 – 2016-04-11

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

Hidden variable formalisms in quantum mechanics 2015-11-28

Copula functions 2015-06-23

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