Via Alexander Terenin: stochastic gradient descent can be used as an efficient approximate sampling algorithm for Gaussian processes. Looks super cool: https://arxiv.org/abs/2306.11589

#GaussianProcesses #Bayesian

@tylerjburch Yes, I hear you 😓

You've likely already fixed your installation and I'm not sure whether you're using #jupyter, but I found this guide really helpful:

https://pkseeg.com/post/jupyter-venv/

Now, I always warn people to never mess with the base installation of #python on a machine but use (virtual) environments instead.

Good luck with the #GaussianProcesses, I'm going through the #pymc tutorials for it right now 😎

I'm eyeballs deep into understanding #GaussianProcesses (GPs). There are great resources out there but I can thoroughly recommend this introductory paper on #Distill by Görtler et al. The interactive plots are a great https://doi.org/10.23915/distill.00017

Let's say I have samples of a bounded (but potentially noisy) function at some fixed interval of points. How would I go about determining the likelihood that my observed data was sampled from an *increasing* function with heteroscedastic noise? #machinelearning #statistics #GaussianProcesses ??

I have, once again, made the strange choice to write a blog. This one is about Gaussian Process and, particularly, about what the Markov property looks like when you don't have a linear notion of time to help you define a past and present.

Like all my GP posts, this one is wildly technical but with an aim towards being somewhat useful. The information here is hard to find unless you want to read a 400 page book translated from Russian
https://dansblog.netlify.app/posts/2023-01-21-markov/markov.html

#GaussianProcesses #machinelearning

Watch our own @sethaxen summarize our recent #NeurIPS2022 workshop paper on modeling European #paleoclimate using #GaussianProcesses!

https://youtu.be/ZFiJHmZbpZA

@ml4science @unituebingen @sommer @alvaro

@sethaxen @NeuripsConf @ArviZ I'm not attending but I do love talking about #GaussianProcesses ...

Today's #machinelearning #paper is 'Using the Nystrom Method to Speed Up Kernel Machines' (and old one, but a good one); one of the earlier attempts at scaling #GaussianProcesses to large datasets. For models that are able to capture such nonlinear relationships, the fundamentals are still all about linear algebra (eigen-decompositions of matrices in this case).

If anyone's interested, I fleshed out a few of the proofs and put the notes on my company's website: https://www.engineeringdataanalytics.com/technical-notes

👋 This is my first time attending @NeuripsConf (virtually to reduce carbon emissions).

On Friday I'll join the workshop "Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems," where we have a paper, poster, and lightning talk on GPs for modeling #paleoclimate.

If you're attending and want to chat about #GaussianProcesses, probabilistic programming (#ProbProg), or @ArviZ, ping me!

#NeurIPS2022

Check out some results from one of our current projects! #Spatiotemporal modeling of European #paleoclimate using doubly sparse #GaussianProcesses

https://arxiv.org/abs/2211.08160

Our preprint "Spatiotemporal modeling of European #paleoclimate using doubly sparse Gaussian processes" is now on #arXiv!

This is one of the outcomes of a cooperation we (@sethaxen, Alex Gessner, and Álvaro Tejero-Cantero) are currently running with @sommer and Nils Weitzel.

The paper, as well as a lightning talk and poster, were accepted to the #NeurIPS2022 workshop on #GaussianProcesses, #Spatiotemporal Modeling, and Decision-making Systems #GPSMDMS

https://arxiv.org/abs/2211.08160

I just fixed some typos in my blogpost on priors for #GaussianProcesses. The way you know it's my blog is that the guy who emailed me said "the equation after footnote 99 doesn't match how you used it after footnote 108".

#MachineLearning #statistics #bayesian #Stan

https://dansblog.netlify.app/posts/2022-09-07-priors5/priors5.html