botorch
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pytorch
Bayesian optimization in PyTorch
# Hello, I am applying the tutorial "Multi-Fidelity BO with Discrete Fidelities using KG" to a normalized Currin function with an extra fidelity. I thought it was working so far...
Dear BoTorch developers, I'm having some issues related to the use of different acquisition functions with a `pairwise_gp` model. Specifically, the issue arises when the user uses acquisition functions that...
This is an issue that came up in #754: Currently, tutorial so the links in the `.md` doc files are such that the website points to the rendered tutorials (e.g....
## Issue description Hello, I built a Multi-fidelity deep GP (https://arxiv.org/pdf/1903.07320.pdf) on top of the class DeepGP. After fitting the GP, I get 10 samples from the distribution. I need...
`botorch.optim.utils.sample_all_priors` is failing to sample priors that GPyTorch can sample with `module.sample_from_prior`. The reason is that the shape of the parameter is being passed to the `sample` method: https://github.com/pytorch/botorch/blob/913aa0e510dde10568c2b4b911124cdd626f6905/botorch/optim/utils.py#L43 However,...
There is some confusion among some users regarding the distinction and usage of constraints (see e.g. #432). We should write up a doc section explaining them: How are parameter constraints...
Hi Botorch developers, I came across the functionality of performing LOOCV directly within botorch and I like it. Only one thing is missing for me: One cannot pass modelspecific arguments...
# 🐛 Bug As far as I can tell `botorch.optim.optimize_acqf` leaves a tiny bit of memory behind somewhere. It seems worse for q-batched acquisition functions (at least, for qUCB and...
I want to use the contextual GPs, in particular the `LCEMGP`, for a project. The existing documentation, i.e., the docstrings, do not present any examples, which makes it challenging to...
I have been using SingleTaskGP with the default priors, but today out of curiosity I decided to take a look at the priors. The input lengthscale prior seems ok assuming...
