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| author | Thibaut Horel <thibaut.horel@gmail.com> | 2013-12-12 17:48:03 -0500 |
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| committer | Thibaut Horel <thibaut.horel@gmail.com> | 2013-12-12 17:48:03 -0500 |
| commit | 7f6b960702e38cf2e34d5594ba2b37a45f8a9520 (patch) | |
| tree | 0959b7bb483fd3af4568941b95f53778479ac80d /conclusion.tex | |
| parent | 184fa7504c3f2b4c1ee797e91fe8b919eff51ae9 (diff) | |
| download | recommendation-7f6b960702e38cf2e34d5594ba2b37a45f8a9520.tar.gz | |
Reimport things from the appendix to the main part for the camera-ready version
Diffstat (limited to 'conclusion.tex')
| -rwxr-xr-x | conclusion.tex | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/conclusion.tex b/conclusion.tex index 6b17237..4db1030 100755 --- a/conclusion.tex +++ b/conclusion.tex @@ -5,7 +5,7 @@ polynomial time. %Our objective function, commonly known as the Bayes $D$-optima A natural question to ask is to what extent ou results %we present here generalize to other machine learning tasks beyond linear regression. We outline -a path to such a generalization in Appendix~\ref{sec:ext}: %. In +a path to such a generalization in \cite{arxiv}: %. In %particular, although the information gain is not generally a submodular %function, we show that for a wide class of models in which experiment |
