-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathschedule.html
More file actions
511 lines (505 loc) · 14.2 KB
/
Copy pathschedule.html
File metadata and controls
511 lines (505 loc) · 14.2 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
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
---
title: Schedule
layout: default
---
<head><title>Conference Schedule</title></head>
<h1>AISTATS Conference Schedule</h2>
<h3>Wednesday 12 May</h3>
<table>
<tr>
<td width="20%" ><a name="registration1"></a>16:00 - 19:00</td>
<td width="90%">Registration
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="reception1"></a>19:30 - 21:30</td>
<td width="90%">Dinner
</td>
</tr>
</table><h3>Thursday 13 May</h3>
<table>
<tr>
<td width="20%" ><a name="breakfast1"></a>07:45 - 08:30</td>
<td width="90%">Breakfast
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="invited1"></a>08:30 - 08:45</td>
<td width="90%">Welcome
<br><i>Organizers</i>
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Invited Talk</b>
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="invited3"></a>08:45 - 09:45</td>
<td width="90%">Forensic Statistics: Where are We and Where are We Going?
<br><i>Richard Gill</i>
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Network Models</b>
(Chair B. Schölkopf)
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="130"></a>09:45 - 10:10</td>
<td width="90%">Boosted optimization for network classification
<br><i>T. Hancock and H. Mamitsuka </i>
<a href="http://proceedings.mlr.press/v9/hancock10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/hancock10a/hancock10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="294"></a>10:10 - 10:35</td>
<td width="90%">Detecting weak but hierarchically-structured patterns in networks
<br><i>A. Singh and R. Nowak </i>
<a href="http://proceedings.mlr.press/v9/singh10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/singh10a/singh10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="coffee1"></a>10:35 - 13:00</td>
<td width="90%">Coffee Break and <a href="{{ site.url }}/{{ site.baseurl }}/poster1.html">Poster Session I</a>
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="lunch1"></a>13:00 - 17:00</td>
<td width="90%">Lunch
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Statistical Learning
Theory </b>(Chair Y. Altun)
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="257"></a>17:00 - 17:25</td>
<td width="90%">Risk bounds for transduction and semi-supervised learning relative to data structure
<br><i>G. Lever </i>
<a href="http://proceedings.mlr.press/v9/lever10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/lever10a/lever10a.pdf">[pdf]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/lever10a/lever10aSupple.xyz">[supplementary]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="242"></a>17:25 - 17:50</td>
<td width="90%">Multiclass-multilabel classification with more labels than examples
<br><i>O. Dekel and O. Shamir </i>
<a href="http://proceedings.mlr.press/v9/dekel10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/dekel10a/dekel10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="157"></a>17:50 - 18:15</td>
<td width="90%">Empirical Bernstein boosting
<br><i>P. Shivaswamy and T. Jebara </i>
<a href="http://proceedings.mlr.press/v9/shivaswamy10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/shivaswamy10a/shivaswamy10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="tea1"></a>18:15 - 18:45</td>
<td width="90%">Tea Break
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Bayesian
nonparametrics and causal inference</b> (Chair S. Petrone)
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="100"></a>18:45 - 19:10</td>
<td width="90%">Sufficient covariates and linear propensity analysis
<br><i>H. Guo and P. Dawid </i>
<a href="http://proceedings.mlr.press/v9/guo10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/guo10a/guo10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="167"></a>19:10 - 19:35</td>
<td width="90%">Dirichlet process mixtures of generalised linear models
<br><i>L. Hannah, D. Blei and W. Powell </i>
<a href="http://proceedings.mlr.press/v9/hannah10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/hannah10a/hannah10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="318"></a>19:35 - 20:00</td>
<td width="90%">Bayesian Gaussian process latent variable model
<br><i>M. Titsias and N. Lawrence </i>
<a href="http://proceedings.mlr.press/v9/titsias10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/titsias10a/titsias10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="banquet1"></a>20:00 - 22:00</td>
<td width="90%">Conference Banquet
</td>
</tr>
</table><h3>Friday 14 May</h3>
<table>
<tr>
<td width="20%" ><a name="breakfast2"></a>07:45 - 08:45</td>
<td width="90%">Breakfast
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Invited Talk</b>
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="invited1"></a>08:45 - 09:45</td>
<td width="90%">Approximate Bayesian Computation: What, Why and How?
<br><i>Simon Tavaré</i>
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Deep Learning</b>
(Chair Y. Bengio)
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="296"></a>09:45 - 10:10</td>
<td width="90%">Factored 3-way restricted Boltzmann machines for modeling natural images
<br><i>M. Ranzato, A. Krizhevsky and G. Hinton </i>
<a href="http://proceedings.mlr.press/v9/ranzato10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/ranzato10a/ranzato10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="255"></a>10:10 - 10:35</td>
<td width="90%">Learning the structure of deep sparse graphical models
<br><i>R. Adams, H. Wallach and Z. Ghahramani </i>
<a href="http://proceedings.mlr.press/v9/adams10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/adams10a/adams10a.pdf">[pdf]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/adams10a/adams10aSupple.xyz">[supplementary]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="coffee2"></a>10:35 - 13:00</td>
<td width="90%">Coffee Break and <a href="{{ site.url }}/{{ site.baseurl }}/poster2.html">Poster Session II</a>
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="lunch2"></a>13:00 - 17:00</td>
<td width="90%">Lunch
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Approximate
Inference</b> (Chair A. Globerson)
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="117"></a>17:00 - 17:25</td>
<td width="90%">Solving the uncapacitated facility location problem using message passing problems
<br><i>N. Lazic, B. Frey and P. Arabi </i>
<a href="http://proceedings.mlr.press/v9/lazic10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/lazic10a/lazic10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="309"></a>17:25 - 17:50</td>
<td width="90%">Dense message passing for sparse principal component analysis
<br><i>K. Sharp and M. Rattray </i>
<a href="http://proceedings.mlr.press/v9/sharp10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/sharp10a/sharp10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="274"></a>17:50 - 18:15</td>
<td width="90%">Focused belief propagation for query-specific inference
<br><i>A. Chechetka and C. Guestrin </i>
<a href="http://proceedings.mlr.press/v9/chechetka10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/chechetka10a/chechetka10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="tea2"></a>18:15 - 18:45</td>
<td width="90%">Tea Break
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Online Learning,
Control & Information Theory</b> (Chair A. Singh)
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="233"></a>18:45 - 19:10</td>
<td width="90%">Exploiting feature covariance in high-dimensional online learning
<br><i>J. Ma, A. Kulesza, M. Dredze, K. Crammer, L. Saul and F. Pereira </i>
<a href="http://proceedings.mlr.press/v9/ma10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/ma10a/ma10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="149"></a>19:10 - 19:35</td>
<td width="90%">REGO: Rank-based estimation of Renyi information using Euclidean graph optimization
<br><i>B. Poczos, C. Szepesvari and S. Kirshner </i>
<a href="http://proceedings.mlr.press/v9/poczos10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/poczos10a/poczos10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="113"></a>19:35 - 20:00</td>
<td width="90%">Coherent inference on optimal play in game trees
<br><i>P. Hennig, D. Stern and T. Graepel </i>
<a href="http://proceedings.mlr.press/v9/hennig10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/hennig10a/hennig10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="dinner2"></a>20:00 - 22:00</td>
<td width="90%">Dinner
</td>
</tr>
</table><h3>Saturday 15 May</h3>
<table>
<tr>
<td width="20%" ><a name="breakfast3"></a>07:45 - 08:45</td>
<td width="90%">Breakfast
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Invited Talk</b>
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="invited2"></a>08:45 - 09:45</td>
<td width="90%">Nonparametric Learning of Functions and Graphs in High Dimensions
<br><i>John Lafferty</i>
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Kernel Methods</b>
(Chair A. Gretton)
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="66"></a>09:45 - 10:10</td>
<td width="90%">Nonlinear functional regression: a functional RKHS approach
<br><i>H. Kadri </i>
<a href="http://proceedings.mlr.press/v9/kadri10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/kadri10a/kadri10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="134"></a>10:10 - 10:35</td>
<td width="90%">On the relation between universality, characteristic kernels and RKHS embedding of measures
<br><i>B. Sriperumbudur, K. Fukumizu and G. Lanckreit </i>
<a href="http://proceedings.mlr.press/v9/sriperumbudur10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/sriperumbudur10a/sriperumbudur10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="coffee2"></a>10:35 - 13:00</td>
<td width="90%">Coffee Break and <a href="{{ site.url }}/{{ site.baseurl }}/poster3.html">Poster Session III</a>
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="lunch3"></a>13:00 - 17:00</td>
<td width="90%">Lunch
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Graphical Models and
Causal Inference</b> (Chair I. Murray)
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="259"></a>17:00 - 17:25</td>
<td width="90%">On combining graph-based variance reduction schemes
<br><i>V. Gogate and R. Dechter </i>
<a href="http://proceedings.mlr.press/v9/gogate10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/gogate10a/gogate10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="251"></a>17:25 - 17:50</td>
<td width="90%">Convex structure learning in log-linear models beyond pairwise potentials
<br><i>M. Schmidt and K. Murphy </i>
<a href="http://proceedings.mlr.press/v9/schmidt10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/schmidt10a/schmidt10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="241"></a>17:50 - 18:15</td>
<td width="90%">Modeling annotator expertise: learning when everybody knows a bit of something
<br><i>R. Rosales, Y. Yan, G. Fung and J. Dy </i>
<a href="http://proceedings.mlr.press/v9/yan10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/yan10a/yan10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="tea1"></a>18:15 - 18:45</td>
<td width="90%">Tea Break
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ></td> <td width="90%"><b>Low-rank Methods &
Information Retrieval</b> (Chair M. Niranjan)
</td>
</tr>
</table>
<table>
<tr>
<td width="20%" ><a name="151"></a>18:45 - 19:10</td>
<td width="90%">Fluid dynamics models for low rank discriminant analysis
<br><i>Y.-K. Noh, B.-T. Zhang and D. Lee </i>
<a href="http://proceedings.mlr.press/v9/noh10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/noh10a/noh10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="43"></a>19:10 - 19:35</td>
<td width="90%">Reduced-rank hidden Markov models
<br><i>S. Siddiqi, B. Boots and G. Gordon </i>
<a href="http://proceedings.mlr.press/v9/siddiqi10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/siddiqi10a/siddiqi10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="225"></a>19:35 - 20:00</td>
<td width="90%">Half transductive ranking
<br><i>B. Bai, J. Weston, D. Grangier, R. Collobert, C. Cortes and M. Mohri </i>
<a href="http://proceedings.mlr.press/v9/bai10a.html">[abs]</a>
<a target="_blank" href="http://proceedings.mlr.press/v9/bai10a/bai10a.pdf">[pdf]</a>
</td>
</tr>
</table>
<p>
<table>
<tr>
<td width="20%" ><a name="dinner3"></a>20:00 - 22:00</td>
<td width="90%">Dinner
</td>
</tr>
</table>