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Update apt cache in Github action running notebooks #535

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1 change: 1 addition & 0 deletions .github/workflows/run-notebooks.yml
Expand Up @@ -23,6 +23,7 @@ jobs:
- name: Install dependencies
run: |
python -m pip install --upgrade pip
sudo apt-get update
sudo apt-get install $(cat apt.txt)
pip install -r docs/requirements.txt
pip install .
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27 changes: 0 additions & 27 deletions docs/source/research/bibliography/related.bib
Expand Up @@ -57,33 +57,6 @@ @inproceedings{GarnettOH2013
optimization in high-dimensional spaces.}
}

@article{HennigS2012,
title = {Entropy Search for Information-Efficient Global Optimization},
author = {Hennig, P. and Schuler, CJ.},
month = jun,
volume = 13,
pages = {1809-1837},
abstract = {Contemporary global optimization algorithms are based on
local measures of utility, rather than a probability measure
over location and value of the optimum. They thus attempt to
collect low function values, not to learn about the
optimum. The reason for the absence of probabilistic global
optimizers is that the corresponding inference problem is
intractable in several ways. This paper develops desiderata
for probabilistic optimization algorithms, then presents a
concrete algorithm which addresses each of the computational
intractabilities with a sequence of approximations and
explicitly adresses the decision problem of maximizing
information gain from each evaluation. },
journal = {Journal of Machine Learning Research},
year = 2012,
file =
{http://jmlr.csail.mit.edu/papers/volume13/hennig12a/hennig12a.pdf},
link = {http://jmlr.csail.mit.edu/papers/v13/hennig12a.html},
code = {http://probabilistic-optimization.org/Global.html}
}


@article{garrabrant,
author = {Scott Garrabrant and Tsvi Benson-Tilsen and Andrew Critch and Nate Soares and Jessica Taylor},
title = {Logical Induction},
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