Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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4,500 | 5,072 | Efficient Online Inference for Bayesian
Nonparametric Relational Models
Dae Il Kim1 , Prem Gopalan2 , David M. Blei2 , and Erik B. Sudderth1
1
2
Department of Computer Science, Brown University, {daeil,sudderth}@cs.brown.edu
Department of Computer Science, Princeton University, {pgopalan,blei}@cs.princeton.edu
Abstr... | 5072 |@word illustrating:1 briefly:1 open:1 crucially:1 thereby:1 initial:3 configuration:1 contains:2 score:8 zij:3 ka:13 current:1 assigning:1 must:2 realistic:1 subsequent:1 partition:3 informative:2 shape:1 interpretable:1 update:17 v:3 discovering:1 advancement:1 website:1 yamada:1 colored:1 blei:7 provides:4 node... |
4,501 | 5,073 | Learning with Noisy Labels
Nagarajan Natarajan
Inderjit S. Dhillon
Pradeep Ravikumar
Department of Computer Science, University of Texas, Austin.
{naga86,inderjit,pradeepr}@cs.utexas.edu
Ambuj Tewari
Department of Statistics, University of Michigan, Ann Arbor.
tewaria@umich.edu
Abstract
In this paper, we theoreticall... | 5073 |@word trial:1 version:3 norm:1 stronger:1 nd:1 suitably:4 dekel:1 bylander:2 contraction:1 decomposition:1 pavel:1 biconjugate:5 harder:3 reduction:1 liu:4 lucet:2 tuned:1 interestingly:1 existing:1 analysed:1 yet:1 liva:1 realize:1 numerical:1 benign:1 shape:1 plot:5 juditsky:1 aside:1 instantiate:1 accordingly:... |
4,502 | 5,074 | Low-rank matrix reconstruction and clustering via
approximate message passing
Ryosuke Matsushita
NTT DATA Mathematical Systems Inc.
1F Shinanomachi Rengakan, 35,
Shinanomachi, Shinjuku-ku, Tokyo,
160-0016, Japan
matsur8@gmail.com
Toshiyuki Tanaka
Department of Systems Science,
Graduate School of Informatics, Kyoto Un... | 5074 |@word trial:9 norm:1 nd:1 km:9 bvt:3 covariance:2 thereby:1 tr:1 initial:7 liu:1 contains:1 document:1 amp:53 outperforms:2 existing:1 com:1 si:4 gmail:1 additive:1 numerical:5 kdd:1 analytic:1 seeding:1 update:4 maxv:1 aside:1 intelligence:1 nearness:1 toronto:1 five:3 mathematical:1 symposium:4 incorrect:1 cons... |
4,503 | 5,075 | Sign Cauchy Projections and Chi-Square Kernel
Ping Li
Dept of Statistics & Biostat.
Dept of Computer Science
Rutgers University
pingli@stat.rutgers.edu
Gennady Samorodnitsky
ORIE and Dept of Stat. Science
Cornell University
Ithaca, NY 14853
gs18@cornell.edu
John Hopcroft
Dept of Computer Science
Cornell University
It... | 5075 |@word multitask:1 norm:2 open:1 confirms:1 simulation:2 hsieh:1 gennady:3 sgd:1 euclidian:1 solid:5 reduction:2 liblinear:6 hoiem:1 document:1 interestingly:5 bc:1 err:1 com:1 written:1 john:2 concatenate:3 additive:1 confirming:1 kdd:2 designed:1 drop:1 update:1 plot:4 hash:3 short:2 fa9550:1 provides:5 quantize... |
4,504 | 5,076 | Learning Multi-level Sparse Representations
Ferran Diego
Fred A. Hamprecht
Heidelberg Collaboratory for Image Processing (HCI)
Interdisciplinary Center for Scientific Computing (IWR)
University of Heidelberg, Heidelberg 69115, Germany
{ferran.diego,fred.hamprecht}@iwr.uni-heidelberg.de
Abstract
Bilinear approximation... | 5076 |@word middle:1 eliminating:1 achievable:1 seems:3 norm:8 plsa:1 tensorial:1 simulation:1 decomposition:16 q1:12 schnitzer:1 moment:1 electronics:1 selecting:1 document:1 rightmost:2 activation:10 intriguing:1 written:1 must:1 subsequent:1 shape:4 designed:2 plot:2 greedy:2 pursued:1 intelligence:1 imitate:1 accor... |
4,505 | 5,077 | A New Convex Relaxation for Tensor Completion
Bernardino Romera-Paredes
Department of Computer Science
and UCL Interactive Centre
University College London
Malet Place, London WC1E 6BT, UK
B.RomeraParedes@cs.ucl.ac.uk
Massimiliano Pontil
Department of Computer Science and
Centre for Computational Statistics
and Machi... | 5077 |@word multitask:2 compression:1 advantageous:1 norm:48 paredes:3 open:1 calculus:1 tried:3 bn:21 decomposition:6 rgb:1 thereby:1 initial:2 liu:1 contains:1 romera:3 current:1 wd:1 com:1 si:2 chu:1 john:1 numerical:4 visible:1 j1:2 designed:1 update:1 rpn:1 selected:1 rp1:10 paulin:1 yamada:1 provides:2 authority:... |
4,506 | 5,078 | Latent Maximum Margin Clustering
Guang-Tong Zhou, Tian Lan, Arash Vahdat, and Greg Mori
School of Computing Science
Simon Fraser University
{gza11,tla58,avahdat,mori}@cs.sfu.ca
Abstract
We present a maximum margin framework that clusters data using latent variables. Using latent representations enables our framework ... | 5078 |@word briefly:2 km:6 tried:1 arti:1 initial:1 liu:2 wedding:7 efficacy:4 score:18 contains:2 denoting:1 outperforms:4 current:4 yet:1 assigning:1 written:1 partition:1 hofmann:1 enables:2 remove:1 update:1 grass:2 intelligence:2 instantiate:3 website:1 selected:2 discovering:1 plane:2 short:2 detecting:2 provides... |
4,507 | 5,079 | Statistical analysis of coupled time series with Kernel
Cross-Spectral Density operators.
Michel Besserve
MPI for Intelligent Systems and MPI for Biological Cybernetics, T?ubingen, Germany
michel.besserve@tuebingen.mpg.de
Nikos K. Logothetis
MPI for Biological Cybernetics, T?ubingen
nikos.logothetis@tuebingen.mpg.de
... | 5079 |@word h:9 mild:2 trial:2 version:1 middle:3 norm:21 proportion:1 hyv:1 simulation:1 accounting:1 covariance:7 decomposition:1 pick:1 tr:7 solid:1 reduction:1 tapering:3 initial:1 series:63 contains:2 rkhs:6 interestingly:2 current:1 si:1 subsequent:1 enables:4 reproducible:1 plot:1 stationary:4 intelligence:1 sel... |
4,508 | 508 | A Topographic Product for the Optimization
of Self-Organizing Feature Maps
Hans-Ulrich Bauer, Klaus Pawelzik, Theo Geisel
Institut fUr theoretische Physik and SFB Nichtlineare Dynamik
Universitat Frankfurt
Robert-Mayer-Str. 8-10
W -6000 Frankfurt 11
Fed. Rep . of Germany
email: bauer@asgard.physik.uni-frankfurt.dbp
A... | 508 |@word briefly:1 seems:1 physik:2 pick:1 nt:2 must:1 subsequent:1 speakerindependent:1 plot:1 v:1 nichtlineare:2 node:9 brandt:2 introduce:1 roughly:1 brain:1 pawelzik:6 str:1 becomes:1 underlying:1 notation:1 suffice:1 deutsche:1 what:1 dynamik:2 minimizes:1 interpreted:1 q2:2 quantitative:1 demonstrates:1 classif... |
4,509 | 5,080 | Robust Low Rank Kernel Embeddings of
Multivariate Distributions
Le Song, Bo Dai
College of Computing, Georgia Institute of Technology
lsong@cc.gatech.edu, bodai@gatech.edu
Abstract
Kernel embedding of distributions has led to many recent advances in machine
learning. However, latent and low rank structures prevalent i... | 5080 |@word repository:1 norm:5 nd:2 ci2:2 decomposition:42 covariance:2 pick:1 dramatic:1 recursively:2 ld:1 carry:3 moment:1 contains:1 rkhs:5 xnj:1 current:1 z2:14 si:3 yet:1 dx:2 written:1 readily:3 bd:1 subsequent:1 concatenate:1 partition:2 numerical:1 shape:1 designed:1 plot:1 bickson:1 v:1 intelligence:1 leaf:1... |
4,510 | 5,081 | B-tests: Low Variance Kernel Two-Sample Tests
Matthew Blaschko
Arthur Gretton
Wojciech Zaremba
?
Gatsby Unit
Center for Visual Computing
Equipe
GALEN
?
University College London
Inria Saclay
Ecole
Centrale Paris
United Kingdom
Ch?atenay-Malabry, France
Ch?atenay-Malabry, France
{woj.zaremba,arthur.gretton}@gmail.com, ... | 5081 |@word briefly:1 mmds:1 smirnov:2 nd:1 open:1 bn:3 covariance:5 harder:1 moment:5 series:2 united:1 ecole:1 rkhs:5 bootstrapped:1 existing:1 com:2 exy:1 gmail:1 yet:2 must:1 written:2 fn:2 visible:3 oldenbourg:1 plot:1 drop:2 v:3 half:2 selected:1 fewer:1 es:1 ith:1 core:1 yamada:1 accepting:1 location:1 simpler:2... |
4,511 | 5,082 | On Flat versus Hierarchical Classification in
Large-Scale Taxonomies
Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini
Universit? Joseph Fourier, Laboratoire Informatique de Grenoble
BP 53 - F-38041 Grenoble Cedex 9
firstname.lastname@imag.fr
Abstract
We study in this paper flat and hierarchical classi... | 5082 |@word version:9 norm:1 nd:1 dekel:2 hsieh:1 bioasq:1 tr:1 harder:1 liblinear:2 bai:1 liu:3 series:2 denoting:2 document:5 academia:1 hofmann:1 designed:1 rd2:2 v:2 leaf:2 selected:2 directory:1 gfb:5 record:1 provides:3 node:41 org:1 zhang:1 along:1 direct:2 descendant:1 consists:1 introduce:2 indeed:2 behavior:1... |
4,512 | 5,083 | Robust Bloom Filters for Large Multilabel
Classification Tasks
Moustapha Ciss?e
LIP6, UPMC
Sorbonne Universit?e
Paris, France
first.last@lip6.fr
Nicolas Usunier
UT Compi`egne, CNRS
Heudiasyc UMR 7253
Compi`egne, France
nusunier@utc.fr
Thierry Artieres, Patrick Gallinari
LIP6, UPMC
Sorbonne Universit?e
Paris, France
f... | 5083 |@word version:1 middle:3 compression:3 stronger:1 nd:1 bf:29 dekel:6 hsieh:1 p0:1 recursively:1 liblinear:2 reduction:9 configuration:1 contains:1 score:5 hereafter:1 document:1 ours:1 bitwise:3 existing:1 com:1 must:1 partition:3 confirming:2 cis:1 remove:1 designed:4 v:2 hash:23 half:1 leaf:2 selected:1 egne:2 ... |
4,513 | 5,084 | Gaussian Process Conditional Copulas with
Applications to Financial Time Series
James Robert Lloyd
Engineering Department
University of Cambridge
jrl44@cam.ac.uk
Jos?e Miguel Hern?andez-Lobato
Engineering Department
University of Cambridge
jmh233@cam.ac.uk
Daniel Hern?andez-Lobato
Computer Science Department
Univers... | 5084 |@word version:2 middle:4 csx:1 covariance:11 decomposition:1 accommodate:2 reduction:2 moment:5 configuration:1 series:29 initial:1 daniel:3 outperforms:1 current:1 parameter1:1 cad:2 si:2 worsening:1 written:1 numerical:4 subsequent:1 plot:8 drop:1 update:4 n0:6 statis:1 alone:1 intelligence:3 prohibitive:1 para... |
4,514 | 5,085 | Bayesian Inference and Learning in Gaussian Process
State-Space Models with Particle MCMC
Roger Frigola1 , Fredrik Lindsten2 , Thomas B. Sch?on2,3 and Carl E. Rasmussen1
1. Dept. of Engineering, University of Cambridge, UK, {rf342,cer54}@cam.ac.uk
2. Div. of Automatic Control, Link?oping University, Sweden, lindsten@is... | 5085 |@word briefly:1 version:1 proportionality:1 seek:1 propagate:1 crucially:2 covariance:10 simulation:4 fifteen:1 harder:1 moment:1 reduction:1 liu:2 series:3 contains:1 interestingly:1 existing:1 ka:1 readily:1 subsequent:1 drop:1 plot:1 update:4 resampling:2 v:1 generative:1 selected:1 greedy:1 prohibitive:1 inte... |
4,515 | 5,086 | Multi-Task Bayesian Optimization
Kevin Swersky
Department of Computer Science
University of Toronto
kswersky@cs.toronto.edu
Jasper Snoek?
School of Engineering and Applied Sciences
Harvard University
jsnoek@seas.harvard.edu
Ryan P. Adams
School of Engineering and Applied Sciences
Harvard University
rpa@seas.harvard.... | 5086 |@word exploitation:2 faculty:1 version:4 cnn:4 cox:1 retraining:1 mockus:1 willing:1 zilinskas:1 rgb:1 covariance:5 pick:3 solid:2 reduction:2 initial:1 configuration:1 contains:1 score:2 selecting:2 daniel:1 bootstrapped:2 document:2 existing:1 freitas:2 contextual:2 com:2 must:1 john:1 fn:2 cheap:2 christian:1 ... |
4,516 | 5,087 | Efficient Optimization for
Sparse Gaussian Process Regression
Yanshuai Cao1
1
Marcus A. Brubaker2
David J. Fleet1
2
Department of Computer Science
University of Toronto
TTI-Chicago
Aaron Hertzmann1,3
3
Adobe Research
Abstract
We propose an efficient optimization algorithm for selecting a subset of training data... | 5087 |@word version:1 inversion:2 dalal:1 norm:2 triggs:1 termination:2 covariance:9 decomposition:4 nystr:4 tr:6 harder:1 reduction:7 initial:1 score:4 selecting:4 disparity:1 outperforms:3 existing:1 current:3 comparing:1 chu:1 chicago:1 informative:2 remove:3 update:15 v:1 greedy:6 prohibitive:2 selected:1 ith:1 pro... |
4,517 | 5,088 | Variational Inference for Mahalanobis
Distance Metrics in Gaussian Process Regression
Michalis K. Titsias
Department of Informatics
Athens University of Economics and Business
mtitsias@aueb.gr
Miguel L?azaro-Gredilla
Dpt. Signal Processing & Communications
Universidad Carlos III de Madrid - Spain
miguel@tsc.uc3m.es
... | 5088 |@word determinant:1 version:3 middle:1 inversion:2 seems:1 simulation:1 covariance:5 solid:1 reduction:8 initial:2 contains:1 initialisation:1 tuned:1 existing:3 current:1 wd:1 written:2 must:1 realistic:1 wx:12 remove:1 v:1 intelligence:1 selected:1 guess:1 discovering:1 maximised:1 provides:2 node:2 toronto:1 w... |
4,518 | 5,089 | It is all in the noise: Efficient multi-task Gaussian
process inference with structured residuals
Christoph Lippert
Microsoft Research
Los Angeles, USA
lippert@microsoft.com
Barbara Rakitsch
Machine Learning and Computational Biology
Research Group
Max Planck Institutes T?ubingen, Germany
rakitsch@tuebingen.mpg.de
Ka... | 5089 |@word trial:1 cu:1 simulation:7 covariance:72 decomposition:2 yuc:1 thereby:2 series:1 score:1 genetic:1 outperforms:1 existing:2 com:1 nt:3 written:2 john:1 fn:2 multioutput:1 n0:1 generative:1 selected:4 xk:1 prize:1 core:1 smith:1 filtered:1 math:1 preference:1 simpler:1 zhang:1 five:1 ethanol:3 fitting:2 fals... |
4,519 | 509 | Towards Faster Stochastic Gradient Search
Christian Darken and John Moody
Yale Computer Science, P.O. Box 2158, New Haven, CT 06520
Email: darken@cs.yale.edu
Abstract
Stochastic gradient descent is a general algorithm which includes LMS,
on-line backpropagation, and adaptive k-means clustering as special cases.
The s... | 509 |@word illustrating:1 jacob:4 paid:1 dramatic:2 initial:1 loc:1 seriously:1 current:1 elliptical:1 od:1 john:1 numerical:1 christian:1 pertinent:1 update:1 v:1 stationary:1 provides:1 math:2 location:2 mathematical:1 direct:1 persistent:1 qualitative:1 prove:1 theoretically:1 roughly:1 behavior:4 nor:1 automaticall... |
4,520 | 5,090 | Spike train entropy-rate estimation using hierarchical
Dirichlet process priors
Karin Knudson
Department of Mathematics
kknudson@math.utexas.edu
Jonathan W. Pillow
Center for Perceptual Systems
Departments of Psychology & Neuroscience
The University of Texas at Austin
pillow@mail.utexas.edu
Abstract
Entropy rate qua... | 5090 |@word neurophysiology:1 briefly:1 proportion:1 cs0:1 simulation:1 jacob:1 edric:1 recursively:1 series:4 contains:1 past:1 existing:4 contextual:3 yet:2 must:1 parsing:1 subsequent:1 partition:2 distant:4 discernible:1 plot:3 drop:1 stationary:4 cue:1 xk:3 short:7 memoizer:1 blei:1 provides:2 math:1 node:3 revisi... |
4,521 | 5,091 | Designed Measurements for Vector Count Data
1
Liming Wang, 1 David Carlson, 2 Miguel Dias Rodrigues, 3 David Wilcox,
1
Robert Calderbank and 1 Lawrence Carin
1
Department of Electrical and Computer Engineering, Duke University
2
Department of Electronic and Electrical Engineering, University College London
3
Departme... | 5091 |@word version:1 compression:1 logit:1 nd:1 c0:1 seek:1 tried:1 pulse:2 simulation:2 r:1 prasad:1 dramatic:2 document:26 mmse:7 existing:2 recovered:1 current:11 od:1 wd:2 readily:1 cruz:1 informative:2 analytic:1 shamai:3 designed:15 update:1 v:1 half:1 selected:2 harmany:1 sys:6 ith:2 blei:1 math:1 org:1 five:1 ... |
4,522 | 5,092 | Dirty Statistical Models
Eunho Yang
Department of Computer Science
University of Texas at Austin
eunho@cs.utexas.edu
Pradeep Ravikumar
Department of Computer Science
University of Texas at Austin
pradeepr@cs.utexas.edu
Abstract
We provide a unified framework for the high-dimensional analysis of
?superposition-structu... | 5092 |@word briefly:1 loading:2 norm:20 stronger:3 decomposition:6 covariance:7 score:3 denoting:1 past:1 ka:1 yet:1 written:2 additive:1 sys:1 caveat:2 provides:1 allerton:1 simpler:2 zhang:1 c2:5 become:1 shorthand:1 theoretically:1 inter:1 expected:1 indeed:2 cand:3 nor:1 p1:2 multi:3 decreasing:1 spherical:1 encour... |
4,523 | 5,093 | Summary Statistics for
Partitionings and Feature Allocations
Is??k Bar?s? Fidaner
Computer Engineering Department
Bo?gazic?i University, Istanbul
fidaner@alternatifbilisim.org
Ali Taylan Cemgil
Computer Engineering Department
Bo?gazic?i University, Istanbul
taylan.cemgil@boun.edu.tr
Abstract
Infinite mixture models ... | 5093 |@word version:1 middle:1 proportion:2 open:1 cyprus:1 seek:1 eng:1 innermost:1 pg:4 tr:1 initial:2 liu:1 contains:3 score:1 qatar:1 genetic:1 africa:1 existing:1 current:1 comparing:2 cumulation:2 nt:4 si:6 written:4 indonesia:1 partition:8 informative:3 plot:5 designed:1 update:1 malaysia:1 leaf:3 chile:1 ith:1 ... |
4,524 | 5,094 | Dynamic Clustering via Asymptotics of the
Dependent Dirichlet Process Mixture
Trevor Campbell
MIT
Cambridge, MA 02139
Miao Liu
Duke University
Durham, NC 27708
tdjc@mit.edu
miao.liu@duke.edu
Brian Kulis
Ohio State University
Columbus, OH 43210
Jonathan P. How
MIT
Cambridge, MA 02139
Lawrence Carin
Duke Universit... | 5094 |@word aircraft:8 kulis:3 trial:3 nd:1 vogt:1 d2:1 vermaak:1 recursively:1 reduction:2 liu:2 united:2 selecting:1 tuned:1 longitudinal:1 past:3 outperforms:1 current:11 yet:1 hou:1 john:2 enables:1 analytic:1 jfk:1 christian:2 update:13 generative:2 half:2 selected:1 nq:7 discovering:2 plane:5 scotland:1 wolfram:2... |
4,525 | 5,095 | k-Prototype Learning for 3D Rigid Structures ?
Hu Ding
Department of Computer Science and Engineering
State University of New York at Buffalo
Buffalo, NY14260
huding@buffalo.edu
Ronald Berezney
Department of Biological Sciences
State University of New York at Buffalo
Buffalo, NY14260
berezney@buffalo.edu
Jinhui Xu
D... | 5095 |@word version:2 seems:2 norm:2 vi1:1 hu:1 q1:13 recursively:2 reduction:2 initial:2 configuration:2 contains:4 selecting:1 genetic:1 kahl:1 existing:5 current:2 comparing:2 si:3 must:1 ronald:1 partition:12 shape:10 opin:1 plot:2 update:2 medial:1 selected:2 item:1 realizing:2 core:2 math:1 node:1 firstly:2 c22:7... |
4,526 | 5,096 | Distributed k-Means and k-Median Clustering on
General Topologies
Maria Florina Balcan, Steven Ehrlich, Yingyu Liang
School of Computer Science
Georgia Institute of Technology
Atlanta, GA 30332
{ninamf,sehrlich}@cc.gatech.edu,yliang39@gatech.edu
Abstract
This paper provides new algorithms for distributed clustering f... | 5096 |@word briefly:1 compression:1 polynomial:1 widom:1 heiser:1 pick:1 incurs:1 venkatasubramanian:1 liu:1 outperforms:3 existing:2 surprising:1 si:6 must:1 readily:1 additive:1 partition:11 designed:1 half:1 selected:1 xk:1 fa9550:1 provides:1 node:28 location:1 gx:3 c6:1 zhang:7 five:1 height:5 c2:3 direct:1 become... |
4,527 | 5,097 | Multiclass Total Variation Clustering
Thomas Laurent
Loyola Marymount University
Los Angeles, CA 90045
tlaurent@lmu.edu
Xavier Bresson
University of Lausanne
Lausanne, Switzerland
xavier.bresson@unil.ch
James H. von Brecht
University of California, Los Angeles
Los Angeles, CA 90095
jub@math.ucla.edu
David Uminsky
U... | 5097 |@word trial:3 dkr:4 version:8 norm:2 seems:1 simulation:1 propagate:1 bn:1 decomposition:1 unimpressive:1 asks:1 tice:1 initial:5 contains:3 series:1 outperforms:1 current:3 yet:1 must:3 readily:1 numerical:2 partition:10 plot:1 n0:1 half:1 selected:1 spec:1 intelligence:2 desktop:1 beginning:1 ith:2 fa9550:1 lr:... |
4,528 | 5,098 | Learning Multiple Models via Regularized Weighting
Daniel Vainsencher
Department of Electrical Engineering
Technion, Haifa, Israel
danielv@tx.technion.ac.il
Shie Mannor
Department of Electrical Engineering
Technion, Haifa, Israel
shie@ee.technion.ac.il
Huan Xu
Mechanical Engineering Department
National University of... | 5098 |@word briefly:1 polynomial:1 norm:1 seems:2 proportion:1 reused:1 dekker:1 seek:5 bn:2 elisseeff:1 initial:1 mpexuh:1 series:2 daniel:1 gagliardi:1 current:2 aberrant:1 yet:1 assigning:1 must:7 john:3 additive:1 partition:1 visible:1 happen:1 cheap:1 generative:1 leaf:1 fewer:2 half:1 intelligence:1 plane:1 ith:2... |
4,529 | 5,099 | Regularized Spectral Clustering under the
Degree-Corrected Stochastic Blockmodel
Karl Rohe
Department of Statistics
University of Wisconsin-Madison
Madison, WI
karlrohe@stat.wisc.edu
Tai Qin
Department of Statistics
University of Wisconsin-Madison
Madison, WI
qin@stat.wisc.edu
Abstract
Spectral clustering is a fast ... | 5099 |@word version:7 pw:1 norm:3 proportion:1 nd:1 c0:2 simulation:7 tried:1 decomposition:5 zbl:2 contains:3 score:16 current:2 written:1 john:1 numerical:1 partition:12 informative:1 shape:3 analytic:1 remove:2 plot:1 v:1 xk:2 ith:1 jiashun:1 provides:5 node:71 ames:2 liberal:1 five:1 c2:2 beta:1 symposium:2 natalie... |
4,530 | 51 | 495
REFLEXIVE ASSOCIATIVE MEMORIES
Hendrlcus G. Loos
Laguna Research Laboratory, Fallbrook, CA 92028-9765
ABSTRACT
In the synchronous discrete model, the average memory capacity of
bidirectional associative memories (BAMs) is compared with that of
Hopfield memories, by means of a calculat10n of the percentage of good
... | 51 |@word norm:3 cha:1 cml:3 heteroassociative:1 accounting:1 thres:2 reduction:1 configuration:1 contains:1 must:2 remove:1 bart:1 half:8 selected:1 device:2 pointer:1 ik:1 soffer:1 combine:1 manner:2 hresholding:1 mechanic:1 project:1 notation:1 mass:1 transformation:6 hypothetical:1 act:2 bipolar:3 um:1 unit:1 appea... |
4,531 | 510 | Threshold Network Learning in the Presence of
Equivalences
John Shawe-Taylor
Department of Computer Science
Royal Holloway and Bedford New College
University of London
Egham, Surrey TW20 OEX, UK
Abstract
This paper applies the theory of Probably Approximately Correct (PAC)
learning to multiple output feedforward thre... | 510 |@word km:1 simplifying:1 tr:1 substitution:2 selecting:1 chervonenkis:5 lang:1 john:6 fn:1 shawetaylor:1 warmuth:1 manfred:1 slh:1 completeness:1 bijection:1 node:27 prove:2 introduce:2 indeed:1 multi:1 automatically:1 equipped:1 considering:3 begin:2 bounded:2 notation:1 what:1 unified:1 guarantee:1 certainty:1 e... |
4,532 | 5,100 | Moment-based Uniform Deviation Bounds for
k-means and Friends
Matus Telgarsky
Sanjoy Dasgupta
Computer Science and Engineering, UC San Diego
{mtelgars,dasgupta}@cs.ucsd.edu
Abstract
Suppose k centers are fit to m points by heuristically minimizing the k-means
cost; what is the corresponding fit over the source distrib... | 5100 |@word manageable:1 polynomial:1 norm:12 duda:1 bf:6 heuristically:1 d2:1 heretofore:1 covariance:8 p0:17 pick:1 mention:1 boundedness:6 carry:1 moment:28 contains:1 score:5 series:1 bc:3 com:1 must:2 written:1 readily:1 subsequent:1 zeger:2 numerical:1 drop:1 update:2 selected:1 guess:2 steepest:1 core:1 farther:... |
4,533 | 5,101 | Statistical Active Learning Algorithms
Vitaly Feldman
IBM Research - Almaden
vitaly@post.harvard.edu
Maria Florina Balcan
Georgia Institute of Technology
ninamf@cc.gatech.edu
Abstract
We describe a framework for designing efficient active learning algorithms that are
tolerant to random classification noise and differ... | 5101 |@word private:16 faculty:1 version:3 polynomial:7 suitably:1 dekel:1 d2:2 simulation:8 covariance:2 invoking:1 asks:1 harder:1 reduction:2 chervonenkis:1 ours:1 past:1 current:2 beygelzimer:3 chu:1 attracted:1 must:1 informative:1 guess:3 warmuth:1 isotropic:8 mccallum:1 smith:1 core:1 record:4 fa9550:1 filtered:... |
4,534 | 5,102 | Predictive PAC Learning and Process Decompositions
Aryeh Kontorovich
Computer Science Department
Ben Gurion University
Beer Sheva 84105 Israel
karyeh@cs.bgu.ac.il
Cosma Rohilla Shalizi
Statistics Department
Carnegie Mellon University
Pittsburgh, PA 15213 USA
cshalizi@cmu.edu
Abstract
We informally call a stochastic ... | 5102 |@word version:2 open:2 seek:1 decomposition:7 attainable:1 pick:1 asks:1 initial:3 contains:1 series:2 pub:1 chervonenkis:2 ecole:1 past:14 existing:1 bradley:1 lang:1 written:1 must:12 john:1 fn:1 realistic:1 happen:1 gurion:1 enables:1 drop:1 aside:1 implying:1 stationary:14 selected:2 device:1 item:1 warmuth:1... |
4,535 | 5,103 | Adaptivity to Local Smoothness and Dimension in
Kernel Regression
Samory Kpotufe
Toyota Technological Institute-Chicago?
samory@ttic.edu
Vikas K Garg
Toyota Technological Institute-Chicago
vkg@ttic.edu
Abstract
We present the first result for kernel regression where the procedure adapts locally
at a point x to both ... | 5103 |@word version:1 c0:19 d2:1 simulation:1 decomposition:1 invoking:1 pick:2 selecting:3 tuned:2 existing:1 comparing:1 must:6 fn:33 subsequent:1 chicago:2 designed:1 selected:5 math:1 mcdiarmid:2 mathematical:1 c2:6 ouput:1 prove:2 consists:2 combine:2 introduce:1 x0:5 expected:1 inspired:1 globally:1 decreasing:2 ... |
4,536 | 5,104 | A Comparative Framework for
Preconditioned Lasso Algorithms
Michael I. Jordan
Nebojsa Jojic
Fabian L. Wauthier
Computer Science Division
Microsoft Research, Redmond
Statistics and WTCHG
jojic@microsoft.com University of California, Berkeley
University of Oxford
jordan@cs.berkeley.edu
flw@stats.ox.ac.uk
Abstract
The L... | 5104 |@word briefly:2 eliminating:1 seems:2 norm:2 underline:1 suitably:2 crucially:1 initial:1 series:2 selecting:1 elliptical:1 com:1 comparing:9 recovered:1 yet:1 must:1 readily:2 wanted:1 plot:2 v:16 nebojsa:1 generative:10 instantiate:1 accordingly:1 core:2 underestimating:1 mitigation:1 constructed:2 direct:2 ik:... |
4,537 | 5,105 | New Subsampling Algorithms for Fast Least Squares
Regression
Paramveer S. Dhillon1 Yichao Lu2 Dean Foster2
Lyle Ungar1
1
2
Computer & Information Science, Statistics (Wharton School)
University of Pennsylvania, Philadelphia, PA, U.S.A
{dhillon|ungar}@cis.upenn.edu
foster@wharton.upenn.edu, yichaolu@sas.upenn.edu
Abst... | 5105 |@word version:1 norm:3 proportion:1 bf:11 covariance:14 decomposition:1 mention:1 solid:3 initial:1 contains:1 comparing:1 chazelle:1 must:1 john:2 plot:2 oldest:1 xk:1 lr:1 provides:1 completeness:1 firstly:1 zhang:1 mathematical:2 constructed:1 c2:4 fitting:2 theoretically:1 upenn:3 expected:1 growing:1 multi:1... |
4,538 | 5,106 | Faster Ridge Regression via the Subsampled Randomized
Hadamard Transform
Yichao Lu1 Paramveer S. Dhillon2 Dean Foster1
Lyle Ungar2
1
2
Statistics (Wharton School), Computer & Information Science
University of Pennsylvania, Philadelphia, PA, U.S.A
{dhillon|ungar}@cis.upenn.edu
foster@wharton.upenn.edu, yichaolu@sas.upe... | 5106 |@word trial:1 version:1 inversion:1 norm:3 tried:4 covariance:3 decomposition:1 tr:4 solid:2 recursively:1 reduction:1 contains:1 series:1 selecting:1 daniel:1 current:1 chazelle:1 concatenate:1 subsequent:3 hypothesize:1 v:1 fewer:1 nq:1 caveat:1 firstly:5 zhang:1 constructed:1 ik:4 prove:1 consists:1 introduce:... |
4,539 | 5,107 | Sequential Transfer in Multi-armed Bandit
with Finite Set of Models
Mohammad Gheshlaghi Azar ?
Alessandro Lazaric ?
School of Computer Science
INRIA Lille - Nord Europe
CMU
Team SequeL
Emma Brunskill ?
School of Computer Science
CMU
Abstract
Learning from prior tasks and transferring that experience to improve future... | 5107 |@word mild:1 trial:1 multitask:2 version:1 norm:4 nd:4 dekel:1 open:2 simulation:3 decomposition:4 pick:1 incurs:2 reduction:1 moment:13 liu:1 series:2 contains:3 venkatasubramanian:1 outperforms:1 current:7 com:1 recovered:1 contextual:1 yet:2 written:1 numerical:4 j1:1 confirming:1 remove:1 update:3 v:1 station... |
4,540 | 5,108 | Prior-free and prior-dependent regret bounds for
Thompson Sampling
S?ebastien Bubeck, Che-Yu Liu
Department of Operations Research and Financial Engineering,
Princeton University
sbubeck@princeton.edu, cheliu@princeton.edu
Abstract
We consider the stochastic multi-armed bandit problem with a prior distribution
on the... | 5108 |@word cu:2 seems:1 nd:1 open:1 simulation:1 decomposition:1 liu:1 series:1 tuned:1 past:2 outperforms:1 current:1 dx:3 must:1 reminiscent:1 numerical:3 realistic:1 remove:1 drop:2 beginning:1 completeness:1 c2:2 beta:3 prove:2 indeed:2 surge:1 multi:7 inspired:4 armed:11 bounded:8 bonus:1 what:2 developed:1 findi... |
4,541 | 5,109 | Two-Target Algorithms for Infinite-Armed Bandits
with Bernoulli Rewards
Thomas Bonald?
Department of Networking and Computer Science
Telecom ParisTech
Paris, France
thomas.bonald@telecom-paristech.fr
Alexandre Prouti`ere??
Automatic Control Department
KTH
Stockholm, Sweden
alepro@kth.se
Abstract
We consider an infini... | 5109 |@word exploitation:3 version:6 simulation:1 initial:1 contains:4 selecting:1 current:3 nt:1 rocquencourt:1 numerical:2 remove:1 selected:8 metrika:1 item:1 short:3 provides:1 successive:2 preference:1 teytaud:2 mathematical:1 beta:7 incorrect:2 prove:3 consists:3 introduce:1 expected:7 themselves:1 planning:1 mul... |
4,542 | 511 | Neural Network Analysis of Event Related
Potentials and Electroencephalogram Predicts
Vigilance
Rita Venturini
William W. Lytton
Terrence J. Sejnowski
Computational Neurobiology Laboratory
The Salk Institute
La J oBa, CA 92037
Abstract
Automated monitoring of vigilance in attention intensive tasks such as
air traff... | 511 |@word neurophysiology:2 trial:2 determinant:1 instruction:1 pulse:1 simulation:6 solid:2 initial:2 united:1 past:1 motor:1 progressively:1 half:1 device:1 tone:2 short:2 filtered:1 five:2 along:1 profound:1 sustained:2 inter:2 rapid:1 simulator:1 brain:3 decreasing:1 little:1 window:3 electroencephalography:3 prov... |
4,543 | 5,110 | Thompson Sampling for 1-Dimensional Exponential
Family Bandits
Emilie Kaufmann
Institut Mines-Telecom; Telecom ParisTech
kaufmann@telecom-paristech.fr
Nathaniel Korda
INRIA Lille - Nord Europe, Team SequeL
nathaniel.korda@inria.fr
Remi Munos INRIA Lille - Nord Europe, Team SequeL
remi.munos@inria.fr
Abstract
Thomps... | 5110 |@word exploitation:1 c0:1 open:1 decomposition:3 p0:3 concise:1 liu:1 contains:1 renewed:1 pna:1 contextual:2 nt:1 varx:1 yet:1 informative:1 shape:1 drop:1 intelligence:1 xk:1 beginning:1 short:1 provides:1 completeness:1 honda:1 along:1 c2:21 direct:1 beta:3 introduce:6 notably:1 indeed:2 expected:3 multi:3 dec... |
4,544 | 5,111 | Bayesian Mixture Modeling and Inference based
Thompson Sampling in Monte-Carlo Tree Search
Aijun Bai
Univ. of Sci. & Tech. of China
baj@mail.ustc.edu.cn
Feng Wu
University of Southampton
fw6e11@ecs.soton.ac.uk
Xiaoping Chen
Univ. of Sci. & Tech. of China
xpchen@ustc.edu.cn
Abstract
Monte-Carlo tree search (MCTS) ha... | 5111 |@word h:3 exploitation:3 version:1 briefly:3 middle:1 seems:1 hector:1 open:1 simulation:13 recursively:3 initial:2 bai:1 selecting:6 past:3 subjective:1 existing:1 current:6 comparing:4 com:1 dx:1 must:3 guez:1 john:1 realistic:2 informative:2 confirming:1 shlomo:1 update:4 v:1 stationary:5 generative:1 selected... |
4,545 | 5,112 | Density estimation from unweighted k-nearest
neighbor graphs: a roadmap
Ulrike von Luxburg
and
Morteza Alamgir
Department of Computer Science
University of Hamburg, Germany
{luxburg,alamgir}@informatik.uni-hamburg.de
Abstract
Consider an unweighted k-nearest neighbor graph on n points that have been sampled i.i.d. fr... | 5112 |@word middle:1 version:1 suitably:1 open:1 grey:2 simulation:3 p0:9 harder:1 carry:1 reduction:2 contains:1 score:2 series:1 current:2 si:1 yet:4 dx:2 written:1 drop:1 plot:6 alone:4 half:1 leaf:1 selected:1 short:1 location:1 readability:1 warmup:1 mathematical:2 along:12 constructed:1 direct:1 predecessor:1 bor... |
4,546 | 5,113 | Sketching Structured Matrices for
Faster Nonlinear Regression
David P. Woodruff
IBM Almaden Research Center
San Jose, CA 95120
dpwoodru@us.ibm.com
Haim Avron
Vikas Sindhwani
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
{haimav,vsindhw}@us.ibm.com
Abstract
Motivated by the desire to extend fast randomiz... | 5113 |@word mild:1 polynomial:18 norm:9 seems:1 nd:1 km:7 d2:2 covariance:1 decomposition:3 hsieh:1 incurs:1 thereby:1 versatile:1 series:2 contains:1 woodruff:9 com:2 si:2 written:1 truct:4 additive:14 numerical:2 confirming:1 dtq:3 designed:1 plot:2 hash:1 rrt:1 fewer:1 device:1 item:1 kyk:1 rudin:1 ksm:1 core:1 prov... |
4,547 | 5,114 | Trading Computation for Communication:
Distributed Stochastic Dual Coordinate Ascent
Tianbao Yang
NEC Labs America, Cupertino, CA 95014
tyang@nec-labs.com
Abstract
We present and study a distributed optimization algorithm by employing a stochastic dual coordinate ascent method. Stochastic dual coordinate ascent method... | 5114 |@word briefly:1 version:1 norm:7 open:1 hsieh:1 pressure:1 sgd:9 pick:2 mention:1 venkatasubramanian:1 initial:2 contains:1 past:1 bradley:1 com:2 comparing:4 luo:1 chu:2 xmk:1 kdd:7 update:10 bickson:1 v:4 selected:2 xk:8 beginning:2 core:4 provides:1 math:1 node:4 zhang:3 along:1 constructed:2 become:2 deterior... |
4,548 | 5,115 | Locally Adaptive Bayesian Multivariate Time Series
Bruno Scarpa
Department of Statistical Sciences
University of Padua
Via Cesare Battisti 241, 35121, Padua, Italy
scarpa@stat.unipd.it
Daniele Durante
Department of Statistical Sciences
University of Padua
Via Cesare Battisti 241, 35121, Padua, Italy
durante@stat.unip... | 5115 |@word middle:1 version:1 inversion:2 polynomial:3 loading:2 seems:2 underline:1 confirms:2 simulation:13 covariance:29 dramatic:1 solid:4 accommodate:1 reduction:4 initial:2 series:19 united:1 denoting:1 longitudinal:1 reaction:1 com:2 worsening:1 hoboken:1 additive:1 enables:1 plot:8 treating:1 update:7 stationa... |
4,549 | 5,116 | A Latent Source Model for
Nonparametric Time Series Classification
George H. Chen
MIT
georgehc@mit.edu
Stanislav Nikolov
Twitter
snikolov@twitter.com
Devavrat Shah
MIT
devavrat@mit.edu
Abstract
For classifying time series, a nearest-neighbor approach is widely used in practice
with performance often competitive with... | 5116 |@word version:5 polynomial:1 nd:1 vldb:1 seek:1 prasad:2 accounting:2 decomposition:1 reduction:1 moment:2 initial:1 series:86 daniel:1 document:2 ours:1 batista:1 outperforms:2 existing:5 luigi:1 com:1 yet:2 guez:1 readily:1 luis:1 additive:1 christian:1 treating:1 plot:1 v:2 generative:1 half:3 fpr:6 contribute... |
4,550 | 5,117 | Multilinear Dynamical Systems
for Tensor Time Series
Mark Rogers
Lei Li
Stuart Russell
EECS Department, University of California, Berkeley
markrogersjr@berkeley.edu, {leili,russell}@cs.berkeley.edu
Abstract
Data in the sciences frequently occur as sequences of multidimensional arrays
called tensors. How can hidden, ev... | 5117 |@word mri:1 version:1 humidity:1 tensorial:1 calculus:1 covariance:9 decomposition:18 tr:7 recursively:1 reduction:2 series:25 contains:1 outperforms:1 existing:2 z2:1 yet:1 written:3 must:1 tenet:1 j1:13 compel:1 christian:1 motor:1 treating:1 update:2 aside:1 selected:1 ntrain:3 accordingly:1 isotropic:3 marine... |
4,551 | 5,118 | What do row and column marginals reveal about
your dataset?
Behzad Golshan
Boston University
behzad@cs.bu.edu
John W. Byers
Boston University
byers@cs.bu.edu
Evimaria Terzi
Boston University
evimaria@cs.bu.edu
Abstract
Numerous datasets ranging from group memberships within social networks to
purchase histories on ... | 5118 |@word version:1 polynomial:6 norm:1 open:1 cha:1 hyv:2 simulation:1 decomposition:1 p0:3 q1:1 pick:1 asks:1 recursively:1 reduction:1 liu:1 cobb:1 efficacy:2 selecting:1 interestingly:2 existing:4 recovered:1 comparing:2 yet:1 assigning:1 must:3 bie:1 john:1 informative:1 kdd:2 designed:1 plot:1 depict:1 ainen:1 ... |
4,552 | 5,119 | Error-Minimizing Estimates and Universal
Entry-Wise Error Bounds for Low-Rank Matrix
Completion
Franz J. Kir?aly?
Department of Statistical Science and
Centre for Inverse Problems
University College London
f.kiraly@ucl.ac.uk
Louis Theran?
Institute of Mathematics
Discrete Geometry Group
Freie Universit?at Berlin
thera... | 5119 |@word multitask:1 version:1 briefly:1 polynomial:9 norm:7 manageable:1 km:15 closure:2 theran:4 covariance:2 ld:3 initial:1 contains:1 selecting:1 outperforms:2 yet:1 dx:2 exposing:1 determinantal:1 additive:3 e22:1 plot:1 acar:1 v:1 precaution:1 half:2 instantiate:1 parameterization:1 short:1 provides:1 math:2 o... |
4,553 | 512 | Structural Risk Minimization
for Character Recognition
I. Guyon, V. Vapnik, B. Boser, L. Bottou, and S. A. Solla
AT&T Bell Laboratories
Holmdel, NJ 07733, USA
Abstract
The method of Structural Risk Minimization refers to tuning the capacity
of the classifier to the available amount of training data. This capacity is ... | 512 |@word polynomial:2 advantageous:1 norm:1 nd:1 seems:1 duda:1 thereby:1 reduction:3 initial:1 contains:1 chervonenkis:1 diagonalized:1 wd:7 comparing:1 must:3 reminiscent:1 analytic:1 half:1 xk:2 te3t:3 provides:4 along:1 constructed:1 direct:1 become:2 c2:1 consists:1 introduce:2 expected:3 examine:1 brain:5 termi... |
4,554 | 5,120 | Synthesizing Robust Plans
under Incomplete Domain Models
Tuan A. Nguyen
Subbarao Kambhampati
Minh Do
Arizona State University
natuan@asu.edu
Arizona State University
rao@asu.edu
NASA Ames Research Center
minh.do@nasa.gov
Abstract
Most current planners assume complete domain models and focus on generating
correct... | 5120 |@word version:1 loading:4 d2:1 seek:1 pg:1 pick:4 mention:1 solid:1 delgado:1 reduction:1 initial:5 contains:3 ours:4 ala:1 prefix:1 past:1 existing:2 lave:1 current:2 yet:1 must:1 planet:1 distant:1 enables:1 intelligence:11 asu:2 amir:1 smith:1 completeness:1 provides:1 ames:1 five:1 c2:2 direct:1 become:1 init... |
4,555 | 5,121 | Which Space Partitioning Tree to Use for Search?
A. G. Gray
Georgia Tech.
Atlanta, GA 30308
agray@cc.gatech.edu
P. Ram
Georgia Tech. / Skytree, Inc.
Atlanta, GA 30308
p.ram@gatech.edu
Abstract
We consider the task of nearest-neighbor search with the class of binary-spacepartitioning trees, which includes kd-trees, p... | 5121 |@word version:1 stronger:1 covariance:9 tr:1 harder:2 recursively:3 liu:1 contains:4 selecting:2 karger:1 mages:4 outperforms:1 existing:4 current:2 deteriorating:2 yet:1 partition:43 remove:2 hash:10 v:20 greedy:1 leaf:4 implying:1 intelligence:3 plane:2 xk:3 ruhl:1 farther:2 quantizer:2 quantized:1 node:21 trav... |
4,556 | 5,122 | Solving inverse problem of Markov chain
with partial observations
Tetsuro Morimura
IBM Research - Tokyo
tetsuro@jp.ibm.com
Takayuki Osogami
IBM Research - Tokyo
osogami@jp.ibm.com
Tsuyoshi Id?e
IBM T.J. Watson Research Center
tide@us.ibm.com
Abstract
The Markov chain is a convenient tool to represent the dynamics o... | 5122 |@word version:2 logit:1 d2:2 seek:1 ld:10 initial:14 loc:6 score:2 past:1 existing:1 current:2 com:5 surprising:1 si:4 john:1 update:3 rd2:1 stationary:13 intelligence:5 device:1 accordingly:1 steepest:2 realizing:1 colored:1 readability:1 location:5 preference:3 org:1 simpler:1 zhang:1 mathematical:1 along:3 dif... |
4,557 | 5,123 | Robust Data-Driven Dynamic Programming
Daniel Kuhn
?cole Polytechnique F?d?rale de Lausanne
CH-1015 Lausanne, Switzerland
daniel.kuhn@epfl.ch
Grani A. Hanasusanto
Imperial College London
London SW7 2AZ, UK
g.hanasusanto11@imperial.ac.uk
Abstract
In stochastic optimal control the distribution of the exogenous noise i... | 5123 |@word mild:1 middle:1 polynomial:1 achievable:1 nd:1 d2:2 willing:1 simulation:1 seek:2 carolina:2 hu:1 incurs:1 thereby:1 tr:7 profit:6 harder:1 boundedness:1 ld:2 initial:1 minmax:1 series:2 exclusively:1 selecting:1 configuration:1 daniel:2 outperforms:2 existing:1 recovered:1 discretization:3 current:1 ka:1 y... |
4,558 | 5,124 | Online Variational Approximations to
non-Exponential Family Change Point Models:
With Application to Radar Tracking
Ryan Turner
Northrop Grumman Corp.
ryan.turner@ngc.com
Steven Bottone
Northrop Grumman Corp.
steven.bottone@ngc.com
Clay Stanek
Northrop Grumman Corp.
clay.stanek@ngc.com
Abstract
The Bayesian online ... | 5124 |@word aircraft:8 version:1 briefly:2 middle:2 km:4 p0:2 q1:1 incurs:1 mention:1 moment:2 liu:1 series:6 contains:2 pub:1 score:1 existing:2 current:2 com:3 nt:7 jupp:1 yet:2 must:6 john:1 partition:1 shape:2 analytic:1 grumman:3 treating:1 drop:2 update:14 n0:4 v:2 implying:1 generative:1 accordingly:1 inspection... |
4,559 | 5,125 | q-OCSVM: A q-Quantile Estimator for
High-Dimensional Distributions
Assaf Glazer
Michael Lindenbaum
Shaul Markovitch
Department of Computer Science, Technion - Israel Institute of Technology
{assafgr,mic,shaulm}@cs.technion.ac.il
Abstract
In this paper we introduce a novel method that can efficiently estimate a family... | 5125 |@word repository:6 version:3 polynomial:1 proportion:4 smirnov:2 nd:1 decomposition:1 moment:1 liu:1 contains:1 series:2 document:8 ours:1 interestingly:1 outperforms:1 existing:2 recovered:2 scovel:1 surprising:2 noc:13 john:4 subsequent:1 informative:1 wellbehaved:1 analytic:1 bart:1 half:10 greedy:3 intelligen... |
4,560 | 5,126 | Unsupervised Structure Learning of Stochastic
And-Or Grammars
Kewei Tu
Maria Pavlovskaia
Song-Chun Zhu
Center for Vision, Cognition, Learning and Art
Departments of Statistics and Computer Science
University of California, Los Angeles
{tukw,mariapavl,sczhu}@ucla.edu
Abstract
Stochastic And-Or grammars compactly repres... | 5126 |@word kgk:2 solan:1 decomposition:1 pick:2 accommodate:1 recursively:1 reduction:7 initial:7 configuration:18 contains:7 fragment:66 ours:6 o2:6 existing:7 outperforms:3 si:2 assigning:1 written:1 parsing:6 remove:1 update:3 alone:1 generative:1 selected:1 greedy:4 intelligence:4 accordingly:2 desktop:1 reciproca... |
4,561 | 5,127 | Rapid Distance-Based Outlier Detection via Sampling
1
Mahito Sugiyama1 Karsten M. Borgwardt1,2
Machine Learning and Computational Biology Research Group, MPIs T?ubingen, Germany
2
Zentrum f?ur Bioinformatik, Eberhard Karls Universit?at T?ubingen, Germany
{mahito.sugiyama,karsten.borgwardt}@tuebingen.mpg.de
Abstract
... | 5127 |@word trial:3 repository:3 version:2 proportion:1 vldb:1 tried:1 dramatic:1 recursively:1 schwabacher:2 carry:1 moment:2 liu:2 zimek:3 score:19 lichman:1 renewed:1 interestingly:1 outperforms:7 spambase:3 current:1 surprising:1 si:5 numerical:1 partition:7 kdd:1 designed:1 half:1 ubuntu:1 pvldb:1 prize:1 core:2 r... |
4,562 | 5,128 | One-shot learning by inverting a compositional causal
process
Ruslan Salakhutdinov
Dept. of Statistics and Computer Science
University of Toronto
rsalakhu@cs.toronto.edu
Brenden M. Lake
Dept. of Brain and Cognitive Sciences
MIT
brenden@mit.edu
Joshua B. Tenenbaum
Dept. of Brain and Cognitive Sciences
MIT
jbt@mit.edu... | 5128 |@word trial:10 version:1 seems:2 open:3 instruction:2 tried:2 shot:15 initial:1 generatively:1 liu:1 score:4 zij:11 document:1 interestingly:1 current:1 com:1 si:19 yet:3 written:2 parsing:4 must:1 realistic:1 blur:2 shape:1 motor:11 designed:1 v:6 generative:3 selected:4 intelligence:6 item:2 plane:2 beginning:2... |
4,563 | 5,129 | Stochastic Majorization-Minimization Algorithms
for Large-Scale Optimization
Julien Mairal
LEAR Project-Team - INRIA Grenoble
julien.mairal@inria.fr
Abstract
Majorization-minimization algorithms consist of iteratively minimizing a majorizing surrogate of an objective function. Because of its simplicity and its wide
a... | 5129 |@word mild:1 stronger:2 norm:4 open:1 hu:1 hsieh:1 mention:1 tr:1 recursively:2 liblinear:16 initial:1 series:1 tuned:1 ours:1 past:2 existing:2 outperforms:1 current:4 comparing:1 tackling:1 written:2 fn:17 numerical:2 plot:1 update:10 n0:6 juditsky:1 stationary:7 website:1 beginning:1 core:2 iterates:4 provides... |
4,564 | 513 | Hierarchical Transformation of Space in the
Visual System
Alexandre Pouget
Stephen A. Fisher
Terrence J. Sejnowski
Computational Neurobiology Laboratory
The Salk Institute
La Jolla, CA 92037
Abstract
Neurons encoding simple visual features in area VI such as orientation,
direction of motion and color are organized ... | 513 |@word trial:1 schoen:1 trotter:1 simulation:1 initial:1 contains:2 john:2 shape:1 motor:1 zacks:1 designed:1 nonspatial:1 footing:1 contribute:1 location:15 successive:1 along:2 become:1 fixation:3 expected:1 simulator:1 provided:1 retinotopic:10 project:1 what:1 interpreted:2 monkey:1 recruit:1 developed:4 findin... |
4,565 | 5,130 | Robust Transfer Principal Component Analysis
with Rank Constraints
Yuhong Guo
Department of Computer and Information Sciences
Temple University, Philadelphia, PA 19122, USA
yuhong@temple.edu
Abstract
Principal component analysis (PCA), a well-established technique for data analysis and processing, provides a convenien... | 5130 |@word version:2 polynomial:1 norm:22 km:4 seek:4 decomposition:5 reduction:7 configuration:1 contains:2 efficacy:2 document:1 bc:7 outperforms:2 recovered:2 ka:4 nt:6 comparing:1 concatenate:1 additive:2 update:4 stationary:4 intelligence:1 discovering:2 provides:2 zhang:1 mathematical:1 rnt:4 constructed:3 ik:2 ... |
4,566 | 5,131 | Online Robust PCA via Stochastic Optimization
Huan Xu
ME Department
National University of Singapore
mpexuh@nus.edu.sg
Jiashi Feng
ECE Department
National University of Singapore
jiashi@nus.edu.sg
Shuicheng Yan
ECE Department
National University of Singapore
eleyans@nus.edu.sg
Abstract
Robust PCA methods are typica... | 5131 |@word mild:1 version:1 briefly:1 norm:16 shuicheng:1 simulation:4 covariance:1 decomposition:4 accounting:1 ronchetti:1 tr:2 klk:1 reduction:1 initial:1 mpexuh:1 contains:1 ours:1 past:1 recovered:2 current:2 comparing:1 luo:1 pcp:20 john:2 fn:2 realistic:2 additive:1 numerical:1 plot:5 update:5 stationary:3 plan... |
4,567 | 5,132 | The Fast Convergence of Incremental PCA
Akshay Balsubramani
UC San Diego
abalsubr@cs.ucsd.edu
Sanjoy Dasgupta
UC San Diego
dasgupta@cs.ucsd.edu
Yoav Freund
UC San Diego
yfreund@cs.ucsd.edu
Abstract
We consider a situation in which we see samples Xn ? Rd drawn i.i.d. from some
distribution with mean zero and unknown... | 5132 |@word mild:2 version:1 seems:2 norm:1 stronger:1 open:1 d2:3 crucially:1 covariance:8 pick:3 sgd:2 vno:12 moment:5 necessity:1 reduction:1 series:1 exclusively:1 initial:6 written:1 fn:14 subsequent:1 additive:1 drop:2 update:10 n0:1 generative:2 prohibitive:1 intelligence:2 warmuth:2 oldest:1 characterization:1 ... |
4,568 | 5,133 | Probabilistic Principal Geodesic Analysis
P. Thomas Fletcher
School of Computing
University of Utah
Salt Lake City, UT
fletcher@sci.utah.edu
Miaomiao Zhang
School of Computing
University of Utah
Salt Lake City, UT
miaomiao@sci.utah.edu
Abstract
Principal geodesic analysis (PGA) is a generalization of principal compo... | 5133 |@word determinant:2 mri:3 briefly:2 middle:1 norm:1 open:1 covariance:1 reduction:1 initial:6 configuration:2 contains:1 series:1 zij:7 rkhs:1 current:2 dx:1 must:2 written:1 john:1 numerical:2 shape:23 remove:1 atlas:1 update:2 generative:1 intelligence:2 accordingly:1 plane:1 isotropic:2 hamiltonian:5 smith:1 h... |
4,569 | 5,134 | Fast Algorithms for Gaussian Noise Invariant
Independent Component Analysis
Luis Rademacher
James Voss
Ohio State University
Ohio State University
Computer Science and Engineering,
Computer Science and Engineering,
2015 Neil Avenue, Dreese Labs 495.
2015 Neil Avenue, Dreese Labs 586.
Columbus, OH 43210
Columbus, OH 43... | 5134 |@word briefly:1 version:5 polynomial:1 nd:1 d2:2 hu:8 simulation:1 hyv:4 covariance:6 decomposition:2 decorrelate:1 mlk:1 atrix:2 moment:7 series:1 interestingly:1 outperforms:1 existing:1 comparing:1 surprising:1 si:8 written:1 luis:1 additive:6 numerical:1 subsequent:1 wx:1 update:5 generative:1 intelligence:1 ... |
4,570 | 5,135 | Online PCA for Contaminated Data
Jiashi Feng
ECE Department
National University of Singapore
jiashi@nus.edu.sg
Huan Xu
ME Department
National University of Singapore
mpexuh@nus.edu.sg
Shie Mannor
EE Department
Technion
shie@ee.technion.ac.il
Shuicheng Yan
ECE Department
National University of Singapore
eleyans@nus.e... | 5135 |@word mild:3 determinant:1 briefly:1 norm:2 c0:2 shuicheng:1 simulation:5 covariance:7 decomposition:2 ronchetti:1 minus:1 initial:19 mpexuh:1 contains:1 ours:2 current:5 wd:2 scatter:1 must:1 john:3 distant:1 partition:3 numerical:1 remove:1 drop:1 update:8 intelligence:1 selected:1 warmuth:1 accordingly:3 plane... |
4,571 | 5,136 | Fantope Projection and Selection:
A near-optimal convex relaxation of sparse PCA
Vincent Q. Vu
The Ohio State University
vqv@stat.osu.edu
Juhee Cho
University of Wisconsin, Madison
chojuhee@stat.wisc.edu
Jing Lei
Carnegie Mellon University
leij09@gmail.com
Karl Rohe
University of Wisconsin, Madison
karlrohe@stat.wis... | 5136 |@word version:3 polynomial:2 seems:1 norm:11 open:1 simulation:5 covariance:17 decomposition:2 tr:4 reduction:2 initial:1 plentiful:1 uncovered:1 dspca:6 liu:3 ours:2 nonparanormal:3 past:1 existing:2 elliptical:2 com:1 comparing:2 gmail:1 scatter:1 yet:1 subsequent:1 numerical:1 weyl:2 statis:6 update:3 mackey:1... |
4,572 | 5,137 | One-shot learning and big data with n = 2
Dean P. Foster
University of Pennsylvania
Philadelphia, PA
dean@foster.net
Lee H. Dicker
Rutgers University
Piscataway, NJ
ldicker@stat.rutgers.edu
Abstract
We model a ?one-shot learning? situation, where very few observations
y1 , ..., yn ? R are available. Associated with ... | 5137 |@word version:1 nd:1 open:1 d2:1 simulation:5 covariance:2 shot:27 moment:2 necessity:1 contains:1 score:3 series:3 neeman:1 bc:36 o2:1 existing:1 outperforms:3 contextual:3 comparing:2 subsequent:1 numerical:1 informative:2 enables:1 pursued:2 intelligence:1 warmuth:1 inspection:1 beginning:1 vanishing:1 smith:1... |
4,573 | 5,138 | The Randomized Dependence Coefficient
David Lopez-Paz, Philipp Hennig, Bernhard Sch?olkopf
Max Planck Institute for Intelligent Systems
Spemannstra?e 38, T?ubingen, Germany
{dlopez,phennig,bs}@tue.mpg.de
Abstract
We introduce the Randomized Dependence Coefficient (RDC), a measure of nonlinear dependence between rando... | 5138 |@word version:2 reshef:4 norm:1 nd:2 crucially:1 decomposition:1 kent:1 covariance:1 nystr:1 series:2 score:2 selecting:3 lightweight:1 favouring:1 analysed:1 si:2 must:1 additive:3 numerical:1 partition:2 designed:1 plot:1 statis:1 n0:1 greedy:2 selected:1 sarcos:2 detecting:1 philipp:1 org:2 zhang:1 five:2 dn:1... |
4,574 | 5,139 | Sparse Additive Text Models with Low Rank
Background
Lei Shi
Baidu.com, Inc.
P.R. China
shilei06@baidu.om
Abstract
The sparse additive model for text modeling involves the sum-of-exp computing,
whose cost is consuming for large scales. Moreover, the assumption of equal background across all classes/topics may be too ... | 5139 |@word version:1 middle:1 advantageous:1 norm:12 proportion:5 seek:1 linearized:1 decomposition:2 eng:1 thres:1 pick:1 inefficiency:1 loc:1 denoting:1 document:19 interestingly:3 outperforms:1 existing:3 current:1 com:1 yet:2 assigning:3 tackling:1 additive:16 plot:2 update:8 generative:11 selected:1 discovering:4... |
4,575 | 514 | Hierarchies of adaptive experts
Robert A. Jacobs
Michael I. Jordan
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
In this paper we present a neural network architecture that discovers a
recursive decomposition of its input space. Based on a generalization... | 514 |@word autoassociator:1 seems:1 simulation:2 lobe:1 jacob:8 decomposition:3 covariance:1 recursively:2 trinary:1 comparing:1 nowlan:4 activation:3 si:1 yet:1 must:1 readily:1 realize:1 belmont:1 additive:1 partition:4 treating:1 selected:1 leaf:6 item:1 flare:1 ith:8 coarse:2 parameterizations:1 ames:1 oak:3 mathem... |
4,576 | 5,140 | Documents as multiple overlapping windows into a
grid of counts
Alessandro Perina1
Nebojsa Jojic1
1
Manuele Bicego2
Andrzej Turski1
Microsoft Corporation, Redmond, WA
2
University of Verona, Italy
Abstract
In text analysis documents are often represented as disorganized bags of words;
models of such count features... | 5140 |@word middle:2 kondor:1 proportion:3 verona:1 seal:2 almond:2 bun:1 instruction:1 gradual:1 chili:2 pick:9 reduction:2 wrapper:1 rind:1 moment:1 score:2 slotted:1 yxx:1 salzmann:1 document:24 shrimp:1 interestingly:1 outperforms:1 wd:3 cooker:1 surprising:1 com:1 must:2 grain:1 refines:1 distant:1 visible:4 shape... |
4,577 | 5,141 | On Algorithms for Sparse Multi-factor NMF
Siwei Lyu
Xin Wang
Computer Science Department
University at Albany, SUNY
Albany, NY 12222
{slyu,xwang26}@albany.edu
Abstract
Nonnegative matrix factorization (NMF) is a popular data analysis method, the
objective of which is to approximate a matrix with all nonnegative com... | 5141 |@word illustrating:1 norm:9 seek:2 decomposition:6 brightness:1 solid:1 edric:1 reduction:1 electronics:1 configuration:1 contains:2 initial:4 daniel:1 document:3 interestingly:1 outperforms:1 freitas:1 comparing:1 subsequent:1 numerical:3 remove:1 update:13 n0:20 v:1 greedy:2 fewer:1 prohibitive:1 xk:27 ugander:... |
4,578 | 5,142 | Learning Adaptive Value of Information
for Structured Prediction
Ben Taskar
University of Washington
Seattle, WA
taskar@cs.washington.edu
David Weiss
University of Pennsylvania
Philadelphia, PA
djweiss@cis.upenn.edu
Abstract
Discriminative methods for learning structured models have enabled wide-spread
use of very ri... | 5142 |@word version:1 eliminating:1 pick:1 recursively:1 initial:1 liu:1 series:1 score:4 tuned:1 ours:2 past:1 current:8 nt:1 si:3 yet:1 assigning:1 must:4 parsing:4 concatenate:2 informative:2 hofmann:1 sponsored:1 update:2 resampling:1 generative:1 prohibitive:1 cue:1 selected:1 greedy:9 intelligence:1 mccallum:1 sh... |
4,579 | 5,143 | Symbolic Opportunistic Policy Iteration for
Factored-Action MDPs
Aswin Raghavana Roni Khardonb Alan Ferna Prasad Tadepallia
a
School of EECS, Oregon State University, Corvallis, OR, USA
{nadamuna,afern,tadepall}@eecs.orst.edu
b
Department of Computer Science, Tufts University, Medford, MA, USA
roni@cs.tufts.edu
Abstra... | 5143 |@word version:4 compression:5 coarseness:1 tadepalli:1 heuristically:1 hu:1 prasad:2 delgado:1 initial:1 interestingly:1 existing:1 current:4 written:1 must:1 gaona:1 john:1 ronald:2 treating:1 plot:1 update:2 v:2 greedy:4 leaf:8 prohibitive:1 intelligence:3 offpolicy:1 core:2 provides:1 node:7 karina:1 successiv... |
4,580 | 5,144 | Point Based Value Iteration with Optimal Belief
Compression for Dec-POMDPs
Charles L. Isbell
College of Computing
Georgia Institute of Technology
Atlanta, GA 30332
isbell@cc.gatech.edu
Liam MacDermed
College of Computing
Georgia Institute of Technology
Atlanta, GA 30332
liam@cc.gatech.edu
Abstract
We present four ma... | 5144 |@word version:1 compression:17 open:3 contraction:1 concise:1 recursively:2 initial:4 series:1 selecting:1 outperforms:3 existing:11 current:10 skipping:1 must:10 partition:1 designed:1 n0:1 greedy:1 selected:1 fewer:1 intelligence:9 core:1 mental:1 revisited:1 successive:1 hyperplanes:3 traverse:1 five:3 along:7... |
4,581 | 5,145 | Convergence of Monte Carlo Tree Search in
Simultaneous Move Games
Viliam Lis?y1
Vojt?ech Kova?r??k1
Marc Lanctot2
Branislav Bo?sansk?y1
2
1
Department of Knowledge Engineering
Maastricht University, The Netherlands
marc.lanctot
@maastrichtuniversity.nl
Agent Technology Center
Dept. of Computer Science and Engine... | 5145 |@word version:2 achievable:1 polynomial:1 bf:5 d2:1 simulation:9 maes:1 initial:2 contains:1 selecting:4 denoting:1 current:7 comparing:1 michal:2 assigning:1 numerical:1 happen:1 j1:1 update:9 intelligence:5 selected:8 advancement:1 leaf:10 vmin:3 serialized:1 short:1 infrastructure:2 node:19 org:1 teytaud:1 mat... |
4,582 | 5,146 | Estimation Bias in Multi-Armed Bandit Algorithms
for Search Advertising
Tao Qin
Microsoft Research Asia
taoqin@microsoft.com
Min Xu
Machine Learning Department
Carnegie Mellon University
minx@cs.cmu.edu
Tie-Yan Liu
Microsoft Research Asia
tie-yan.liu@microsoft.com
Abstract
In search advertising, the search engine n... | 5146 |@word trial:1 exploitation:1 briefly:1 version:1 seems:1 stronger:1 c0:2 unif:7 willing:1 simulation:6 bn:4 reduction:1 liu:2 score:3 selecting:1 omniscient:1 existing:1 current:1 com:2 contextual:1 must:2 realistic:1 numerical:1 partition:1 alam:1 drop:1 sponsored:5 update:1 half:1 fewer:2 record:2 vorobeychik:1... |
4,583 | 5,147 | Optimization, Learning, and Games with Predictable
Sequences
Alexander Rakhlin
University of Pennsylvania
Karthik Sridharan
University of Pennsylvania
Abstract
We provide several applications of Optimistic Mirror Descent, an online learning
algorithm based on the idea of predictable sequences. First, we recover the M... | 5147 |@word version:4 norm:8 nd:1 open:2 unif:4 gradual:1 decomposition:1 asks:1 dramatic:1 reduction:1 selecting:1 interestingly:1 past:1 nt:10 yet:2 benign:1 remove:1 designed:1 hypothesize:1 update:8 fund:1 guess:1 beginning:2 chiang:1 provides:1 equi:1 contribute:1 simpler:1 kelner:1 mathematical:1 h4:2 direct:1 sy... |
4,584 | 5,148 | Minimax Optimal Algorithms
for Unconstrained Linear Optimization
H. Brendan McMahan
Google Reasearch
Seattle, WA
mcmahan@google.com
Jacob Abernethy?
Computer Science and Engineering
University of Michigan
jabernet@umich.edu
Abstract
We design and analyze minimax-optimal algorithms for online linear optimization
games... | 5148 |@word version:1 polynomial:1 norm:1 stronger:1 replicate:2 dekel:2 additively:1 jacob:6 citeseer:1 mention:1 minus:1 initial:2 contains:2 selecting:1 egt:1 interestingly:1 existing:1 com:1 surprising:1 yet:1 must:5 written:1 update:1 warmuth:4 manfred:3 characterization:8 provides:1 earnings:1 gx:4 mathematical:1... |
4,585 | 5,149 | Online Learning with Costly Features and Labels
Navid Zolghadr
Department of Computing Science
University of Alberta
zolghadr@ualberta.ca
G?abor Bart?ok
Department of Computer Science
ETH Z?urich
bartok@inf.ethz.ch
Russell Greiner
Andr?as Gy?orgy
Csaba Szepesv?ari
Department of Computing Science, University of Albert... | 5149 |@word mild:1 innovates:1 version:6 achievable:1 norm:2 seems:2 dekel:2 open:3 contains:1 selecting:3 denoting:1 existing:1 current:2 discretization:6 nt:8 si:2 yet:1 must:3 readily:2 realistic:1 drop:2 update:1 bart:2 intelligence:1 selected:4 device:1 beginning:1 ith:2 prespecified:2 record:1 d2d:1 provides:3 eq... |
4,586 | 515 | Linear Operator for Object Recognition
Ronen Bssri
Shimon Ullman?
M.I.T. Artificial Intelligence Laboratory
and Department of Brain and Cognitive Science
545 Technology Square
Cambridge, MA 02139
Abstract
Visual object recognition involves the identification of images of 3-D objects seen from arbitrary viewpoints. We... | 515 |@word version:1 middle:1 solid:1 contains:2 existing:2 xand:1 assigning:1 must:1 visible:1 shape:2 designed:1 intelligence:5 core:1 math:1 location:3 simpler:1 constructed:1 supply:1 edelman:2 consists:1 prove:1 fitting:1 recognizable:1 introduce:2 commenting:1 lehtio:2 brain:1 compensating:1 actual:2 considering:... |
4,587 | 5,150 | The Pareto Regret Frontier
Wouter M. Koolen
Queensland University of Technology
wouter.koolen@qut.edu.au
Abstract
Performance guarantees for online learning algorithms typically take the form of
regret bounds, which express that the cumulative loss overhead compared to the
best expert in hindsight is small. In the co... | 5150 |@word trial:1 briefly:1 manageable:1 achievable:8 seems:1 compression:1 version:2 open:1 gradual:1 queensland:1 jacob:3 incurs:1 mention:1 harder:1 substitution:1 series:1 daniel:1 tuned:1 erven:1 comparing:1 analysed:2 must:3 john:1 additive:1 progressively:2 update:2 v:2 pursued:1 warmuth:8 core:1 manfred:4 chi... |
4,588 | 5,151 | Online Learning with Switching Costs and Other
Adaptive Adversaries
Nicol`o Cesa-Bianchi
Universit`a degli Studi di Milano
Italy
Ofer Dekel
Microsoft Research
USA
Ohad Shamir
Microsoft Research
and the Weizmann Institute
Abstract
We study the power of different types of adaptive (nonoblivious) adversaries in
the set... | 5151 |@word exploitation:4 version:5 stronger:2 dekel:2 open:1 seek:1 attainable:3 invoking:1 eld:1 incurs:1 arti:1 harder:1 reduction:8 ours:1 interestingly:1 past:5 existing:2 current:4 od:1 surprising:1 written:1 must:5 informative:1 cant:1 designed:2 interpretable:1 implying:2 intelligence:1 warmuth:1 accordingly:1... |
4,589 | 5,152 | High-Dimensional Gaussian Process Bandits
Josip Djolonga
ETH Z?urich
josipd@ethz.ch
Andreas Krause
ETH Z?urich
krausea@ethz.ch
Volkan Cevher
EPFL
volkan.cevher@epfl.ch
Abstract
Many applications in machine learning require optimizing unknown functions
defined over a high-dimensional space from noisy samples that ar... | 5152 |@word determinant:1 exploitation:9 middle:1 polynomial:1 norm:12 faculty:1 nd:1 suitably:2 km:1 d2:1 decomposition:1 pick:5 dramatic:1 incurs:1 asks:1 thereby:1 tr:1 harder:1 carry:1 reduction:1 score:5 rkhs:12 outperforms:3 existing:1 freitas:2 ka:3 si:17 bd:3 must:2 lorentz:1 numerical:2 confirming:1 shape:1 re... |
4,590 | 5,153 | On Poisson Graphical Models
Eunho Yang
Department of Computer Science
University of Texas at Austin
eunho@cs.utexas.edu
Pradeep Ravikumar
Department of Computer Science
University of Texas at Austin
pradeepr@cs.utexas.edu
Genevera I. Allen
Department of Statistics and
Electrical & Computer Engineering
Rice Universit... | 5153 |@word briefly:1 version:1 middle:1 seems:2 simulation:1 covariance:1 accommodate:1 liu:8 series:3 denoting:1 document:1 interestingly:1 nonparanormal:2 genetic:1 existing:2 comparing:1 incidence:3 yet:1 written:1 reminiscent:1 must:1 numerical:1 partition:8 shape:1 atlas:2 depict:1 accordingly:3 sys:1 caveat:1 no... |
4,591 | 5,154 | Conditional Random Fields via Univariate
Exponential Families
Eunho Yang
Department of Computer Science
University of Texas at Austin
eunho@cs.utexas.edu
Pradeep Ravikumar
Department of Computer Science
University of Texas at Austin
pradeepr@cs.utexas.edu
Genevera I. Allen
Department of Statistics and
Electrical & Co... | 5154 |@word trial:2 cu:2 version:1 c0:1 tensorial:1 seek:2 simulation:4 covariance:9 tr:6 t2n:3 liu:7 series:1 egfr:3 denoting:1 genetic:2 outperforms:1 existing:2 bradley:2 contextual:1 bsj:2 dx:3 must:1 attracted:1 written:1 partition:5 plot:2 atlas:2 v:1 alone:1 generative:3 greedy:1 selected:2 accordingly:1 paramet... |
4,592 | 5,155 | Scalable kernels for graphs with continuous attributes
Aasa Feragen, Niklas Kasenburg
Machine Learning and Computational Biology Group
Max Planck Institutes T?ubingen and DIKU, University of Copenhagen
{aasa,niklas.kasenburg}@diku.dk
Jens Petersen1 ,
Marleen de Bruijne1,2
1
DIKU, University of Copenhagen
2
Erasmus Med... | 5155 |@word trial:1 version:1 middle:1 kondor:1 flach:1 open:2 dirksen:4 recursively:3 contains:2 perret:1 outperforms:1 ka:1 comparing:4 must:2 cruz:1 shape:1 gv:15 plot:1 update:3 hash:2 leaf:2 selected:1 ith:5 prize:1 short:1 node:90 height:1 mehlhorn:2 along:7 schweitzer:1 become:1 transducer:1 prove:2 consists:1 d... |
4,593 | 5,156 | Near-optimal Anomaly Detection in Graphs
using Lov?asz Extended Scan Statistic
Akshay Krishnamurthy
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15213
akshaykr@cs.cmu.edu
James Sharpnack
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
jsharpna@gmail.com
Aarti Sing... | 5156 |@word mild:1 briefly:1 kondor:2 polynomial:1 norm:2 physik:1 calculus:1 bn:3 decomposition:3 jacob:1 citeseer:1 commute:1 dramatic:1 venkatasubramanian:1 selecting:1 daniel:1 ours:1 document:1 kahl:1 aberrant:1 com:1 incidence:1 activation:5 gmail:1 must:3 written:1 ust:5 stemming:1 john:1 ronald:1 limp:1 j1:3 an... |
4,594 | 5,157 | Analyzing the Harmonic Structure
in Graph-Based Learning
Xiao-Ming Wu1 , Zhenguo Li3 , and Shih-Fu Chang1,2
1
Department of Electrical Engineering, Columbia University
2
Department of Computer Science, Columbia University
3
Huawei Noah?s Ark Lab, Hong Kong
{xmwu, sfchang}@ee.columbia.edu, li.zhenguo@huawei.com
Abstra... | 5157 |@word kong:1 trial:1 stronger:1 proportion:1 seems:3 open:2 d2:1 confirms:1 simulation:1 commute:6 ld:1 carry:1 interestingly:1 existing:1 com:1 comparing:6 si:54 lang:1 attracted:1 must:1 chicago:1 informative:3 drop:7 a1k:4 implying:1 provides:3 node:1 five:3 mathematical:4 dn:2 become:1 focs:1 consists:1 coifm... |
4,595 | 5,158 | Learning Gaussian Graphical Models with Observed
or Latent FVSs
Alan S. Willsky
Department of EECS
Massachusetts Institute of Technology
willsky@mit.edu
Ying Liu
Department of EECS
Massachusetts Institute of Technology
liu_ying@mit.edu
Abstract
Gaussian Graphical Models (GGMs) or Gauss Markov random fields are widely... | 5158 |@word determinant:1 version:3 briefly:1 manageable:1 polynomial:6 open:1 covariance:24 decomposition:1 concise:1 reduction:2 initial:4 liu:22 contains:1 series:1 selecting:1 karger:1 current:1 recovered:1 si:2 dx:1 chicago:1 partition:4 jfk:1 plot:1 alone:1 greedy:9 selected:6 intelligence:1 parametrization:1 fa9... |
4,596 | 5,159 | Global MAP-Optimality by Shrinking the
Combinatorial Search Area with Convex Relaxation
Bogdan Savchynskyy1
J?org Kappes2
Paul Swoboda2
Christoph Schn?orr1,2
1
Heidelberg Collaboratory for Image Processing, Heidelberg University, Germany
bogdan.savchynskyy@iwr.uni-heidelberg.de
2
Image and Pattern Analysis Group, Heide... | 5159 |@word version:1 manageable:1 stronger:1 norm:2 flach:1 tried:1 grk:1 decomposition:6 inpainting:1 carry:1 n8:3 initial:11 contains:4 efficacy:1 series:1 past:1 existing:1 com:1 givry:1 dechter:1 numerical:1 remove:1 drop:1 greedy:1 selected:1 intelligence:1 plane:9 smith:1 recompute:1 math:1 node:33 provides:3 re... |
4,597 | 516 | Neural Network Routing for Random Multistage
Interconnection Networks
Mark W. Goudreau
Princeton University
and
NEe Research Institute, Inc.
4 Independence Way
Princeton, NJ 08540
c. Lee Giles
NEC Research Institute, Inc.
4 Independence Way
Princeton, NJ 08540
Abstract
A routing scheme that uses a neural network has... | 516 |@word simulation:2 accommodate:1 ld:2 liu:2 mag:1 suppressing:1 current:4 router:37 must:2 oml:1 subsequent:1 designed:1 greedy:11 selected:1 liapunov:1 beginning:2 sys:1 provides:1 successive:1 rc:1 constructed:1 viable:1 manner:2 expected:2 indeed:1 behavior:1 globally:1 encouraging:1 actual:1 considering:1 incr... |
4,598 | 5,160 | First-Order Decomposition Trees
Nima Taghipour
Jesse Davis
Hendrik Blockeel
Department of Computer Science, KU Leuven
Celestijnenlaan 200A, B-3001 Heverlee, Belgium
Abstract
Lifting attempts to speedup probabilistic inference by exploiting symmetries in the
model. Exact lifted inference methods, like their propositio... | 5160 |@word version:2 polynomial:2 dtrees:28 vi1:1 nd:3 adnan:2 decomposition:34 accounting:1 innermost:1 minus:1 accommodate:1 recursively:4 substitution:4 configuration:3 contains:4 interestingly:1 existing:3 contextual:1 nt:11 si:1 assigning:1 dx:6 written:4 dechter:1 partition:2 plm:7 fund:1 alone:1 intelligence:12... |
4,599 | 5,161 | Binary to Bushy: Bayesian Hierarchical Clustering
with the Beta Coalescent
Yuening Hu1 , Jordan Boyd-Graber2 , Hal Daum`e III3 , Z. Irene Ying4
1, 3: Computer Science, 2: iSchool and UMIACS, 4: Agricultural Research Service
1, 2, 3: University of Maryland, 4: Department of Agriculture
ynhu@cs.umd.edu, {jbg,hal}@umiacs.... | 5161 |@word briefly:1 c0:2 covariance:2 p0:5 recursively:1 initial:6 contains:1 score:15 document:6 dpmms:4 outperforms:2 existing:3 freitas:1 recovered:2 com:1 comparing:1 si:4 must:2 parsing:2 john:1 subsequent:2 partition:19 realistic:1 christian:2 remove:1 atlas:1 interpretable:1 update:4 resampling:1 half:1 discov... |
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