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

External validity Transfer learning, dataset shift, learning under covariate shift, transferable learning, domain adaptation etc 2020-10-17 – 2020-10-22

Stan The flagship Bayesian workhorse 2020-10-19

Teaching Statistics With a side order of communicating statistics 2020-02-12 – 2020-10-19

Variational inference in graphical models Local variational approximation, variation message passing 2020-10-16

Bootstrap Shuffling reality to produce your data 2014-11-26 – 2020-10-16

Differentiating through the Gamma 2020-06-12 – 2020-10-15

Dynamical systems via Koopman operators 2020-10-13

Inverse problems for complex models a.k.a. Bayesian calibration, model uncertainty, 2020-10-13

Probabilistic spectral analysis 2019-11-13 – 2020-10-08

Psychometrics Dimensionality reduction for souls 2017-10-31 – 2020-10-05

R The statistical programming language, not the letter 2011-08-07 – 2020-10-04

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

Extreme value theory On the decay of awfulness with oftenness 2020-01-13 – 2020-09-25

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

Tests, statistical Maybe also design of experiments while we are here? 2014-08-23 – 2020-09-21

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

Causal inference in deep neural nets 2020-09-18

Data summarization On maps drawn at smaller than 1:1 scale 2019-01-14 – 2020-09-18

Garbled highlights from NeurIPS 2020 2020-09-17

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

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

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

Reparameterization tricks in inference Normalizing flows, invertible density models, inference by measure transport, low-dimensional coupling… 2018-04-04 – 2020-09-10

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

Statistical mechanics of statistics 2016-12-01 – 2020-09-10

Publication bias Replication crises, P-values, bitching about journals, other debilities of contemporary science at large 2016-08-30 – 2020-09-07

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

Data dashboards On assuring the client that you are doing something data-sciency via clickable widgets 2020-03-12 – 2020-09-02

Causal inference on DAGs Confounding! This scientist performed miracle graph surgery during an intervention and you won’t believe what happened next 2016-10-26 – 2020-09-01

Probabilistic programming programming with probability distributions for, e.g. Bayesian inference or other inference 2019-10-02 – 2020-08-31

Diagramming and visualising graphical models My need for this is conditionally dependent upon my deadline, given the subject matter 2018-03-29 – 2020-08-30

Kalman filtering of Gaussian Processes 2019-09-18 – 2020-08-30

Random fields as stchastic differential equations On ordering space in time 2020-10-12 – 2020-08-30

Statistical and mathematical python 2015-04-27 – 2020-08-27

Online learning 2018-09-30 – 2020-08-26

Plotting in R 2019-10-13 – 2020-08-26

Simulation based inference a.k.a. Approximate Bayesian Computation or Likelihood-Free-Inference 2020-08-25

Minimum description length 2020-08-06

Plotting stuff in julia 2019-05-31 – 2020-07-25

(Outlier) robust statistics 2014-11-25 – 2020-07-14

(Approximate) matrix factorisation 2014-08-05 – 2020-07-03

Mind as statistical learner 2020-06-23 – 2020-06-26

Learning of manifolds Also topological data analysis; other hip names to follow 2014-08-19 – 2020-06-23

Model complexity penalties Information criteria, degrees of freedom etc 2015-04-22 – 2020-06-22

Living with risk 2011-04-14 – 2020-06-19

Memetics Taste and opinion dynamics 2020-01-30 – 2020-06-16

Stochastic signal sampling Discrete sample representation of continuous stochastic processes 2017-05-30 – 2020-06-11

Covariance functions Mercer kernels, positive definite operators, spare reproducing kernels for that Hilbert space I bought on eBay real cheap 2019-09-16 – 2020-05-28

Long memory time series 0001-01-01 – 2020-05-28

Natural gradient descent Climbing slower on the tricky bits 2019-07-18 – 2020-05-26

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

Lévy stochastic differential equations 2020-05-23

Forecasting Haruspicy 2.0 2015-06-16 – 2020-05-21

Audio/music corpora Smells like Team Audioset 2014-08-08 – 2020-05-19

Tensorflow The framework to use for deep learning if you groupthink like Google 2016-07-11 – 2020-05-15

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

Learning with conservation laws, invariances and symmetries 2020-04-11 – 2020-05-01

Empirical estimation of information Informing yourself from your data how informative your data was 2011-04-19 – 2020-04-28

Statistical relational learning 2020-04-26 – 2020-04-27

Statistical projectivity 2020-04-26

Statistics and machine learning 2011-04-15 – 2020-04-26

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

Likelihood free inference 2020-04-22

Learning summary statistics 2020-04-22

Post stratification Making the optimal lemonade from the fruit life gave you 2020-04-22

Databases viewers / editors 2015-03-04 – 2020-04-22

Survey modelling Adjusting for the Lizardman constant 2019-08-29 – 2020-04-21

Learning graphical models from data What is independent of what? 2017-09-20 – 2020-04-11

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

Particle filters incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names 2014-07-25 – 2020-04-08

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

Model averaging On keeping many incorrect hypotheses and using them all as one goodish one 2017-06-20 – 2020-03-22

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

Cross validation 2016-09-05 – 2020-03-13

Optimal transport metrics Wasserstein distances, Monge-Kantorovich metrics, Earthmover distances 2019-05-30 – 2020-03-06

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

Bias reduction Estimating the bias of an estimator so as to subtract it off again 2020-02-26

Learnable indexes and hashes 2018-01-12 – 2020-02-18

Graph sampling 2020-02-15

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

R Shiny Statistics through the internet 2020-02-11

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

Digital forensics 2020-02-04 – 2020-02-07

Survival analysis and reliability Hazard rates, proportional hazard regression, life testing, mean time to failure 2019-03-12 – 2020-02-05

Bayes for beginners 2016-05-30 – 2020-01-30

Differential privacy 2016-07-28 – 2020-01-24

The Gaussian distribution Also Erf and normality and such 2016-06-27 – 2020-01-23

Data cleaning 90% of statistics 2020-01-22

Learning in adaptive systems On staring into scopophilic abysses 2019-10-19 – 2020-01-22

Applied psephology 2016-07-12 – 2020-01-20

Information geometry 2011-10-21 – 2019-12-27

Hawkes processes 2019-12-22

Factor graphs 2019-12-16

Free energy Fancy analogy for understanding cognition 2011-11-27 – 2019-12-11

Message passing algorithms 2014-11-25 – 2019-12-11

Spatial point process and their statistics 2016-08-17 – 2019-12-04

Non-uniform signal sampling Discrete sample representation of continuous signals without a grid 2019-01-08 – 2019-12-03

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

Classification Labelling losses, fitting classifiers etc 2017-02-20 – 2019-11-13

Probability divergences Metrics, contrasts and divergences and other ways of quantifying how similar are two randomnesses 2014-11-25 – 2019-11-12

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

Gaussian processes on lattices 2019-10-30

Inference on graphical models Given what I know about what I know, what do I know? 2017-09-20 – 2019-10-28

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

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

Bayesians vs frequentists Just because we both get the same answer doesn’t mean neither of us is wrong 2014-11-25 – 2019-10-19

Frequentist consistency of Bayesian methods TFW two flawed methods for understanding the world agree with at least each other 2016-04-12 – 2019-10-19

M-open, M-complete, M-closed 2016-05-30 – 2019-10-18

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

The tidyverse 2019-10-14

Non-negative matrix factorisation 2019-10-14

Exponential families 2016-04-19 – 2019-10-12

Inference without KL divergence 2019-10-03

Spatial processes and statistics thereof 2011-07-29 – 2019-10-03

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

State filtering parameters Tracking things that don’t move 2017-09-15 – 2019-10-01

Probabilistic graphical models over continuous index sets 2014-08-05 – 2019-09-25

Data sets Questions for answers looking for questions 2015-06-26 – 2019-09-25

The interpretation of densities as intensities and vice versa Point process of observations ↔ observation of a point process 2016-09-13 – 2019-09-23

Models for count data 2015-05-14 – 2019-09-22

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

Covariance estimation for stochastic processes 2014-11-16 – 2019-09-21

Nonparametric state filters via Gaussian Processes 2019-09-18

Representer theorems 2019-09-16

Large sample theory 2015-02-15 – 2019-09-09

Non random sampling Greasing non-squeaky wheels 2019-08-27

Hierarchical models DAGs, multilevel models, random coefficient models, mixed effect models… 2015-06-07 – 2019-08-19

Bayesian model selection 2017-08-20 – 2019-07-22

Stability in linear dynamical systems This Bodes well 2019-07-19

Fourier interpolation 2019-06-19

(Weighted) least squares fits 2016-09-22 – 2019-05-22

Stability in dynamical systems Lyapunov exponents and ilk 2019-05-21

Statistics software 2015-02-28 – 2019-04-18

Signal sampling Discrete representation of continuous signals and converse 2017-05-30 – 2019-03-08

Mind reading by computer The ultimate inverse problem 2017-07-01 – 2019-01-21

Algorithmic statistics 2014-07-25 – 2019-01-15

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

Bayesian sparsity 2019-01-08

Variational state filtering 2018-03-19 – 2018-12-07

Learning on manifolds Finding the lowest bit of a krazy straw, from the inside 2011-10-21 – 2018-11-16

Multiple testing 2015-04-22 – 2018-11-05

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

Feedback system identification, linear 2016-07-27 – 2018-10-23

Anomaly detection I don’t define what is normal, but I know it when I see it 2015-10-06 – 2018-10-22

Hamiltonian and Langevin Monte Carlo Physics might be on to something 2018-07-31 – 2018-07-12

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

Feedback system identification, not necessarily linear 2016-08-01 – 2018-01-03

Semi/weakly-supervised learning On extracting nutrition from bullshit 2017-07-24 – 2018-01-03

Integral probability metrics 2016-08-21 – 2017-10-31

Relative age and dating Quantifying cradle-snatching 2017-10-14

Probabilistic graphical models 2014-08-05 – 2017-09-11

Post-selection inference Adaptive data analysis without cheating 2017-08-20

Model/hyperparameter selection 2016-04-15 – 2017-08-20

Quantum-probabilistic graphical models 2017-08-07

State filtering Kalman and friends 2015-06-22 – 2017-07-06

Generating functions Fancy counting 2017-06-19

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

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

On my inability to finish things in finite time 2017-04-04

Randomised regression 2017-01-13 – 2017-02-22

Fractional Brownian motion 2017-02-18

Inverse problems 2016-03-30 – 2017-02-14

Metric entropy 2017-02-13

Random embeddings and hashing 2016-12-05 – 2017-02-13

Garbled highlights from NIPS 2016 2016-12-05 – 2017-02-03

Uncertainty quantification 2016-12-26

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

Contagion processes and their statistics 2016-08-30 – 2016-10-28

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

M-estimation 2016-07-11 – 2016-10-10

Stability (in learning) 2016-05-25 – 2016-10-05

Penalised/regularised regression 2016-06-23 – 2016-09-15

Generalised linear models 2016-03-24 – 2016-08-31

Kernel density estimators 2016-03-05 – 2016-08-18

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

“Approximate models” 2016-07-04

Probably Approximately Correct 2014-11-24 – 2016-05-29

Fractional differential equations 2016-03-22 – 2016-05-05

Curved exponential families 2016-04-19

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

Deconvolution 2015-04-19 – 2016-04-11

Indirect inference 2014-12-23 – 2015-12-15

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

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

Expectation propagation 2015-10-26

Copula functions 2015-06-23

Complexity 2011-11-25 – 2015-04-11

Computational mechanics 2011-10-17 – 2015-01-02