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| author | Thibaut Horel <thibaut.horel@gmail.com> | 2015-12-11 18:00:43 -0500 |
|---|---|---|
| committer | Thibaut Horel <thibaut.horel@gmail.com> | 2015-12-11 18:00:43 -0500 |
| commit | deaa0d78822933f651005a8355120693d48a9d4a (patch) | |
| tree | 129abcccd30b7c1ea725f4085852d953c4352bba /finale/final_report.tex | |
| parent | 8fd65558f9149d4161dbedad6d250dea30c1d239 (diff) | |
| download | cascades-deaa0d78822933f651005a8355120693d48a9d4a.tar.gz | |
Use accepted style, looks cleaner
Diffstat (limited to 'finale/final_report.tex')
| -rw-r--r-- | finale/final_report.tex | 32 |
1 files changed, 28 insertions, 4 deletions
diff --git a/finale/final_report.tex b/finale/final_report.tex index 5f4e441..4203fa5 100644 --- a/finale/final_report.tex +++ b/finale/final_report.tex @@ -6,9 +6,13 @@ \usepackage[capitalize, noabbrev]{cleveref} \usepackage{graphicx} \usepackage{bbm} -%\usepackage{fullpage} +\usepackage{times} +\usepackage{subfigure} +\usepackage{natbib} +\usepackage{algorithm} +\usepackage{algorithmic} \input{def} -\usepackage{icml2015} +\usepackage[accepted]{icml2015} %\usepackage{algpseudocode} \DeclareMathOperator*{\argmax}{arg\,max} \DeclareMathOperator*{\argmin}{arg\,min} @@ -38,12 +42,32 @@ \newtheorem*{example}{Example} \newtheorem*{remark}{Remark} -\title{Bayesian and Active Learning for Graph Inference} +\title{} \author{Thibaut Horel \and Jean Pouget-Abadie} \begin{document} -\maketitle +\twocolumn[ +\icmltitle{Bayesian and Active Learning for Graph Inference} + +% It is OKAY to include author information, even for blind +% submissions: the style file will automatically remove it for you +% unless you've provided the [accepted] option to the icml2015 +% package. +\icmlauthor{Thibaut Horel}{thorel@seas.harvard.edu} +\icmladdress{Harvard University} +\icmlauthor{Jean Pouget-Abadie}{jeanpougetabadie@g.harvard.edu} +\icmladdress{Harvard University} + +% You may provide any keywords that you +% find helpful for describing your paper; these are used to populate +% the "keywords" metadata in the PDF but will not be shown in the document +\icmlkeywords{Sparse Recovery, Cascade Models, Graph Inference, Networks, +Diffusion Processes} + +\vskip 0.3in +] + \begin{abstract} The Network Inference Problem (NIP) is the machine learning challenge of recovering the edges and edge weights of an unknown weighted graph from the |
