-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathcode.html
More file actions
175 lines (150 loc) · 7.84 KB
/
code.html
File metadata and controls
175 lines (150 loc) · 7.84 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head profile="http://gmpg.org/xfn/11">
<title>Isabel Valera</title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
<meta name="robots" content="index, follow" />
<link rel="stylesheet" type="text/css" href="css/style.css" media="screen" />
<!-- Alt Stylesheet -->
<link href="css/purple.css" rel="stylesheet" type="text/css" />
<!-- Custom Favicon -->
<link rel="shortcut icon" href="images/cropped-isabel1-300x194.jpg"/>
<link href="css/shortcodes.css" rel="stylesheet" type="text/css" />
<!-- Custom Stylesheet -->
<link href="css/custom.css" rel="stylesheet" type="text/css" />
<style type="text/css" media="screen">
html { margin-top: 28px !important; }
* html body { margin-top: 28px !important; }
</style>
<link href="http://fonts.googleapis.com/css?family=Anton" rel="stylesheet" type="text/css" />
</head>
<body class="single single-post postid-48 single-format-standard logged-in admin-bar no-customize-support chrome">
<?html
?>
<div id="container">
<div id="header" class="col-full">
<div id="logo" class="fr">
<a href="#" title="Isabel Valera, Max Planck Institute for Intelligent Systems">
<img class="title" style="border:6px solid #CBA8E7;padding:3px" width="220px" src="images/IsaWeb.png" alt="Isabel Valera" />
</a>
<span class="site-title">
<a href="http://localhost/isaWp">
Isabel Valera
</a>
</span>
<span class="site-description">Saarland Informatics Campus</span>
</div><!-- /#logo -->
<div id="descripcion" class="nav" style="margin-top:5px;">
<h1 style="font-size:40px;color:#3B0466"> Prof. Dr. Isabel Valera</h1><br/>
<h1>Saarland Informatics Campus</br></br></h1>
</div>
<div id="pagenav" class="nav fl">
<ul>
<li class="b page_item current_page_item">
<a href="https://ivaleram.github.io/">Home</a>
</li>
<li class="page_item page-item-8">
<a href="https://ivaleram.github.io/publications.html">Research</a>
</li>
<li class="page_item page-item-8">
<a href="https://ivaleram.github.io/code.html">Software</a>
</li>
<li class="page_item page-item-44">
<a href="https://ivaleram.github.io/cv.html">CV</a>
</li>
</ul>
</div><!-- /#pagenav -->
</div>
<div id="content" class="col-full">
<div id="sidebar" class="col-right">
<div id="search_main" class="widget">
<table class="tablePurple">
<col style="width:80px"></col>
<col style="width:200px"></col>
<tbody>
<tr>
<td class="celdaClara">
E-mail:
</td>
<td class="celdaOscura">
ivalera@cs.uni-saarland.de
</td>
</tr>
<tr>
<td class="celdaClara">
Address:
</td>
<td class="celdaOscura">
Department of Computer Science</br>
Saarland Informatics Campus</br>
Bldg. E1 1, R. 225</br>
66123 Saarbrücken, Germany
</td>
</tr>
<tr>
<td class="celdaClara">
Phone:
</td>
<td class="celdaOscura">
+49 (0)681 302-57328
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="main" class="col-left">
<div class="post">
<h2 class="title">UNSUPERVISED LEARNING<span style="font-size:18px;font-family: 'times'"></h2>
<span class="titleYear">GENERAL LATENT FEATURE MODELING</span>
Check out the implementation of a general Bayesian nonparametric latent feature model suitable for heterogeneous datasets. This implementation includes code for data exploration and missing data imputation.</br> </br>
- <b><a href="https://github.com/ivaleraM/GLFM"> GENERAL LATENT FEATURE MODEL (GLFM)</a></b> (Python, Matlab an R)</br>
</br>
You can find more information about the GLFM in our <a href="https://arxiv.org/abs/1706.03779">Arxiv</a> paper and <a href="https://papers.nips.cc/paper/5231-general-table-completion-using-a-bayesian-nonparametric-model.pdf">NIPS'14</a> paper. Please, feel free to send any suggestions, comments, bugs or alternative implementation to melanie[at]tsc.uc3m.es or isabel.valera[at]tuebingen.mpg.de
</br></br>
<span class="titleYear">CLUSTERING OF CONTINUOUS-TIME STREAMING DATA</span>
Check out the implementation of the hierarchical Dirichlet-Hawkes process (hdhp), which includes both the generation and the inference algorithm to cluster continuous-time grouped streaming data.</br> </br>
- <b><a href="https://github.com/Networks-Learning/hdhp.py"> HIERARCHICAL DIRICHLET-HAWKES PROCESS (HDHP)</a></b> (Python)</br>
</br>
You can find more information about the HDHP in our <a href="https://arxiv.org/pdf/1610.05775.pdf">WWW'17</a> paper. Please, feel free to send any suggestions, comments, bugs or alternative implementation to cmav[at]bu.edu
</br></br>
<span class="titleYear">SOURCE SEPARATION</span>
Check out the implementation of the infinite factorial dynamical model (iFDM), a general Bayesian non-
parametric model for source separation.</br> </br>
- <b><a href="https://github.com/franrruiz/iFDM"> INFINITE FACTORIAL DYNAMICAL MODEL (iFDM)</a></b> (Matlab)</br>
</br>
You can find more information about the iFDM in our <a href="https://papers.nips.cc/paper/5667-infinite-factorial-dynamical-model.pdf">NIPS'15</a> paper. Please, feel free to send any suggestions, comments, bugs or alternative implementation to f.ruiz[at]columbia.edu or miv24[at]cam.ac.uk
</div>
</div>
<div id="main" class="col-left">
<div class="post">
<h2 class="title">COMPUTATIONAL DISCRIMINATION<span style="font-size:18px;font-family: 'times'"></h2>
Check out the implementation of fair logistic regression, which is able to provide predictions that do not discriminate with respect to one or more sensitive attributes. </br> </br>
- <b><a href="https://github.com/mbilalzafar/fair-classification" >FAIR CLASSIFICATION</a></b> (Python)</br>
</br>
You can find more information about fair classifiers in our <a href="https://arxiv.org/abs/1507.05259">AISTATS'17</a> and <a href="https://arxiv.org/abs/1610.08452">WWW'17</a> papers. Please, feel free to send any suggestions, comments, bugs or alternative implementation to mzafar[at]mpi-sws.org
</div>
</div>
<div class="fix"></div>
</div>
<div id="extended-footer">
<div class="col-full">
<div class="block one">
</div><!-- /.block -->
<div class="block two">
</div><!-- /.block -->
<div class="block three">
</div><!-- /.block -->
</div><!-- /.col-full -->
</div><!-- /#extended-footer -->
<div id="footer">
<div class="col-full">
<div id="copyright" class="col-left">
<p>© 2020 Isabel Valera, Saarlan Informatics Campus</p>
</div>
<div id="credit" class="col-right">
</div>
</div><!-- /.col-full -->
</div><!-- /#footer -->
</div>
</body>