Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
4,900 | 5,436 | Exploiting easy data in online optimization
Amir Sani
Gergely Neu
Alessandro Lazaric
SequeL team, INRIA Lille ? Nord Europe, France
{amir.sani,gergely.neu,alessandro.lazaric}@inria.fr
Abstract
We consider the problem of online optimization, where a learner chooses a decision from a given decision set and suffers some ... | 5436 |@word version:7 stronger:3 nd:1 dekel:1 open:8 multipoint:1 simulation:1 forecaster:1 pick:3 paid:1 initial:3 series:1 tuned:7 past:3 existing:3 outperforms:1 current:1 nally:1 od:8 erven:2 nitesimal:1 written:1 must:1 numerical:1 partition:1 shape:1 designed:3 update:5 aside:1 v:1 amir:2 accordingly:2 cult:3 war... |
4,901 | 5,437 | Learning Mixtures of Ranking Models?
Avrim Blum
Carnegie Mellon University
avrim@cs.cmu.edu
Pranjal Awasthi
Princeton University
pawashti@cs.princeton.edu
Aravindan Vijayaraghavan
New York University
vijayara@cims.nyu.edu
Or Sheffet
Harvard University
osheffet@seas.harvard.edu
Abstract
This work concerns learning ... | 5437 |@word version:2 kapil:1 inversion:3 polynomial:12 suitably:1 km:1 sheffet:1 decomposition:16 covariance:2 pick:2 kz1:1 carry:1 moment:17 initial:1 cyclic:1 contains:1 mi0:1 ktv:1 daniel:3 prefix:10 past:1 existing:2 outperforms:1 current:2 recovered:2 z2:1 assigning:1 tackling:1 must:2 yet:3 john:2 peyton:1 parti... |
4,902 | 5,438 | Optimal Regret Minimization in Posted-Price
Auctions with Strategic Buyers
Mehryar Mohri
Courant Institute and Google Research
251 Mercer Street
New York, NY 10012
? Medina
Andres Munoz
Courant Institute
251 Mercer Street
New York, NY 10012
mohri@cims.nyu.edu
munoz@cims.nyu.edu
Abstract
We study revenue optimizati... | 5438 |@word mild:1 private:1 version:1 leighton:7 stronger:1 advantageous:1 dekel:1 seek:4 simulation:1 attainable:1 thereby:4 reduction:1 initial:1 liu:1 series:1 offering:2 ours:1 past:1 existing:1 current:1 comparing:2 surprising:1 must:5 readily:1 pertinent:1 designed:2 plot:2 depict:1 n0:1 sponsored:2 leaf:2 selec... |
4,903 | 5,439 | Rates of convergence for nearest neighbor
classification
Sanjoy Dasgupta
Computer Science and Engineering
University of California, San Diego
dasgupta@cs.ucsd.edu
Kamalika Chaudhuri
Computer Science and Engineering
University of California, San Diego
kamalika@cs.ucsd.edu
Abstract
We analyze the behavior of nearest ne... | 5439 |@word mild:1 achievable:2 stronger:1 open:3 p0:2 pick:5 moment:1 series:2 contains:2 exclusively:1 omniscient:1 past:1 existing:1 comparing:1 dx:1 must:3 informative:1 wellbehaved:1 remove:1 interpretable:1 discrimination:2 leaf:2 beginning:1 randolph:1 core:1 characterization:1 along:1 specialize:1 introduce:1 x... |
4,904 | 544 | Improving the Performance of Radial Basis
Function Networks by Learning Center Locations
Thomas Dietterich
Department of Computer Science
Oregon State University
Corvallis, OR 97331-3202
Dietrich Wettschereck
Department of Computer Science
Oregon State University
Corvallis, OR 97331-3202
Abstract
Three methods for i... | 544 |@word version:1 proportion:1 norm:3 open:1 tried:3 euclidian:1 initial:1 configuration:3 series:1 score:2 tuned:1 blank:1 surprising:1 activation:1 lang:2 must:3 girosi:3 plot:2 interpretable:1 mackey:1 alone:3 v:2 discovering:1 intelligence:2 cse:1 location:29 sigmoidal:1 mathematical:2 symposium:1 indeed:1 windo... |
4,905 | 5,440 | The limits of squared
Euclidean distance regularization?
?
Micha? Derezinski
Computer Science Department
University of California, Santa Cruz
CA 95064, U.S.A.
mderezin@soe.ucsc.edu
Manfred K. Warmuth
Computer Science Department
University of California, Santa Cruz
CA 95064, U.S.A.
manfred@cse.ucsc.edu
Abstract
Some ... | 5440 |@word version:6 polynomial:1 stronger:1 norm:10 seems:1 nd:3 open:4 decomposition:2 pick:2 incurs:1 solid:2 carry:1 initial:2 contains:1 past:1 comparing:1 activation:7 must:1 cruz:2 additive:1 plot:7 update:4 v:3 half:3 selected:1 intelligence:1 warmuth:7 manfred:2 math:1 cse:1 node:2 banff:1 herbrich:1 zhang:1 ... |
4,906 | 5,441 | ?How hard is my MDP??
The distribution-norm to the rescue
Odalric-Ambrym Maillard
The Technion, Haifa, Israel
odalric-ambrym.maillard@ens-cachan.org
Timothy A. Mann
The Technion, Haifa, Israel
mann.timothy@gmail.com
Shie Mannor
The Technion, Haifa, Israel
shie@ee.technion.ac.il
Abstract
In Reinforcement Learning (RL... | 5441 |@word version:4 achievable:1 norm:62 stronger:1 nd:1 polynomial:1 open:2 simulation:4 decomposition:1 p0:11 arti:1 selecting:1 past:1 existing:4 err:2 current:3 com:1 comparing:1 gmail:1 dx:4 must:1 herring:2 john:1 cant:4 remove:1 greedy:1 fewer:1 selected:2 intelligence:1 cult:2 beginning:2 short:1 core:1 provi... |
4,907 | 5,442 | On Communication Cost of Distributed Statistical
Estimation and Dimensionality
Ankit Garg
Department of Computer Science, Princeton University
garg@cs.princeton.edu
Tengyu Ma
Department of Computer Science, Princeton University
tengyu@cs.princeton.edu
Huy L. Nguy?e? n
Simons Institute, UC Berkeley
hlnguyen@cs.princeto... | 5442 |@word private:9 stronger:2 d2:1 covariance:1 decomposition:1 harder:1 reduction:3 venkatasubramanian:2 erkip:1 existing:2 comparing:1 must:3 written:2 john:3 treating:1 drop:1 designed:1 update:1 ith:6 zhang:3 mathematical:1 direct:14 become:2 chakrabarti:1 focs:3 prove:10 shorthand:2 privacy:1 introduce:1 indeed... |
4,908 | 5,443 | Difference of Convex Functions Programming
for Reinforcement Learning
Bilal Piot1,2 , Matthieu Geist1 , Olivier Pietquin2,3
MaLIS research group (SUPELEC) - UMI 2958 (GeorgiaTech-CNRS), France
2
LIFL (UMR 8022 CNRS/Lille 1) - SequeL team, Lille, France
3
University Lille 1 - IUF (Institut Universitaire de France), Fra... | 5443 |@word norm:20 r:9 decomposition:8 covariance:1 kappen:1 reduction:1 chervonenkis:1 initialisation:1 rkhs:2 bilal:2 comparing:1 si:60 yet:1 john:1 belmont:1 j1:2 hypothesize:1 plot:2 n0:1 stationary:2 greedy:8 instantiate:1 selected:1 short:1 provides:3 boosting:3 revisited:1 c2:6 direct:2 constructed:1 farahmand:... |
4,909 | 5,444 | Learning Neural Network Policies with Guided Policy
Search under Unknown Dynamics
Sergey Levine and Pieter Abbeel
Department of Electrical Engineering and Computer Science
University of California, Berkeley
Berkeley, CA 94709
{svlevine, pabbeel}@eecs.berkeley.edu
Abstract
We present a policy search method that uses i... | 5444 |@word faculty:1 middle:1 proportion:1 stronger:1 pieter:1 additively:1 simulation:2 linearized:1 covariance:6 thereby:1 initial:6 ours:2 outperforms:2 existing:1 current:6 must:2 shape:1 motor:5 designed:2 treating:1 update:4 succeeding:1 stationary:3 intelligence:2 fewer:6 website:1 parameterization:2 ith:1 para... |
4,910 | 5,445 | Near-optimal Reinforcement Learning
in Factored MDPs
Ian Osband
Stanford University
iosband@stanford.edu
Benjamin Van Roy
Stanford University
bvr@stanford.edu
Abstract
Any reinforcement learning algorithm
that applies to all Markov decision
?
processes (MDPs) will suffer ( SAT ) regret on some MDP, where T is
the ela... | 5445 |@word exploitation:1 dtk:3 polynomial:6 stronger:2 norm:3 d2:4 simulation:1 crucially:1 seek:1 decomposition:1 diuk:2 delgado:1 reduction:2 initial:2 daniel:1 existing:1 current:1 nt:7 si:7 must:1 ronald:4 designed:2 stationary:1 intelligence:4 selected:1 leaf:1 prohibitive:1 xk:6 short:1 karina:1 daphne:4 ucrl2:... |
4,911 | 5,446 | Optimizing Energy Production Using Policy Search
and Predictive State Representations
Yuri Grinberg
Doina Precup
School of Computer Science, McGill University
Montreal, QC, Canada
{ygrinb,dprecup}@cs.mcgill.ca
Michel Gendreau?
?
Ecole
Polytechnique de Montr?eal
Montreal, QC, Canada
michel.gendreau@cirrelt.ca
Abstrac... | 5446 |@word rani:1 version:1 loading:1 open:1 simulation:8 decomposition:2 p0:3 minus:1 epartement:1 initial:4 series:2 mag:1 ecole:2 warmer:1 outperforms:1 existing:1 past:1 current:7 disaggregation:1 discretization:2 must:1 realistic:1 plot:9 drop:1 interpretable:1 alone:2 generative:8 half:1 intelligence:2 breton:1 ... |
4,912 | 5,447 | RAAM: The Benefits of Robustness in Approximating
Aggregated MDPs in Reinforcement Learning
Dharmashankar Subramanian
IBM T. J. Watson Research Center
Yorktown Heights, NY 10598
dharmash@us.ibm.com
Marek Petrik
IBM T. J. Watson Research Center
Yorktown Heights, NY 10598
mpetrik@us.ibm.com
Abstract
We describe how to... | 5447 |@word version:1 briefly:2 polynomial:5 norm:8 instrumental:1 r:3 q1:1 initial:2 outperforms:1 com:2 si:3 must:2 readily:1 john:1 tilted:1 additive:1 update:4 stationary:2 alone:1 selected:1 intelligence:2 smith:1 vrieze:2 provides:1 mannor:1 node:3 height:2 constructed:6 consists:1 prove:2 overhead:5 compose:1 no... |
4,913 | 5,448 | Reducing the Rank of Relational Factorization
Models by Including Observable Patterns
Maximilian Nickel1,2
Xueyan Jiang3,4
Volker Tresp3,4
Poggio Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
2 Istituto Italiano di Tecnologia, Genova, Italy
3 Ludwig Maximilian University, Munich, Germany
4 Siemens AG,... | 5448 |@word briefly:1 nchen:1 seek:2 decomposition:8 tr:1 reduction:1 celebrated:1 score:9 ati:1 outperforms:2 existing:3 com:1 nell:1 yet:1 must:1 additive:13 partition:9 informative:1 enables:1 update:10 intelligence:3 selected:1 leaf:2 beginning:1 core:2 yamada:1 blei:1 provides:3 multiset:1 node:1 zhang:1 along:1 c... |
4,914 | 5,449 | A? Sampling
Chris J. Maddison
Dept. of Computer Science
University of Toronto
cmaddis@cs.toronto.edu
Daniel Tarlow, Tom Minka
Microsoft Research
{dtarlow,minka}@microsoft.com
Abstract
The problem of drawing samples from a discrete distribution can be converted into
a discrete optimization problem [1, 2, 3, 4]. In thi... | 5449 |@word version:3 achievable:1 reused:1 open:1 termination:2 heuristically:1 mezuman:1 seek:2 carolina:1 covariance:1 configuration:2 contains:1 exclusively:1 series:2 daniel:2 existing:1 current:2 com:1 subcomponents:1 must:1 john:1 refines:1 partition:9 v:2 stationary:1 half:2 instantiate:1 leaf:1 fewer:5 bart:1 ... |
4,915 | 545 | Self-organisation in real neurons:
Anti-Hebb in 'Channel Space'?
Anthony J. Bell
AI-lab,
Vrije U niversiteit Brussel
Pleinlaan 2, B-I050 Brussels
BELGIUM, (tony@arti.vub.ac.be)
Abstract
Ion channels are the dynamical systems of the nervous system. Their
distribution within the membrane governs not only communication ... | 545 |@word version:2 seems:1 nd:1 open:1 instruction:1 squid:1 simulation:2 seek:1 linearized:1 arti:1 mainen:1 tuned:1 current:10 activation:1 intriguing:1 must:1 physiol:2 realistic:1 pertinent:1 rinzel:1 alone:1 half:1 leaf:1 nervous:2 plane:2 sys:1 short:1 preference:1 simpler:1 along:1 become:1 ik:2 consists:4 man... |
4,916 | 5,450 | Asynchronous Anytime Sequential Monte Carlo
Arnaud Doucet
Yee Whye Teh
Department of Statistics
University of Oxford
Oxford, UK
{doucet,y.w.teh}@stats.ox.ac.uk
Brooks Paige
Frank Wood
Department of Engineering Science
University of Oxford
Oxford, UK
{brooks,fwood}@robots.ox.ac.uk
Abstract
We introduce a new sequenti... | 5450 |@word version:1 briefly:1 eliminating:1 disk:1 termination:1 simulation:5 propagate:3 seek:1 decomposition:1 jacob:2 attainable:1 dramatic:1 recursively:1 initial:10 configuration:1 series:2 exclusively:1 selecting:1 fa8750:1 existing:2 current:1 comparing:2 must:3 subsequent:1 happen:1 randal:1 christian:1 seedi... |
4,917 | 5,451 | Probabilistic ODE Solvers with Runge-Kutta Means
Michael Schober
MPI for Intelligent Systems
T?bingen, Germany
mschober@tue.mpg.de
David Duvenaud
Department of Engineering
Cambridge University
dkd23@cam.ac.uk
Philipp Hennig
MPI for Intelligent Systems
T?bingen, Germany
phennig@tue.mpg.de
Abstract
Runge-Kutta method... | 5451 |@word version:1 polynomial:3 seems:3 nd:3 open:4 physik:1 hu:1 covariance:11 tr:1 minus:1 solid:1 shading:1 recursively:3 moment:1 initial:5 series:5 rkhs:2 interestingly:1 renewed:1 current:3 si:1 yet:1 ws1:1 attracted:1 written:1 intriguing:1 numerical:9 subsequent:1 matured:1 visibility:1 designed:1 remove:1 p... |
4,918 | 5,452 | A Wild Bootstrap for Degenerate Kernel Tests
Kacper Chwialkowski
Department of Computer Science
University College London
London, Gower Street, WC1E 6BT
kacper.chwialkowski@gmail.com
Dino Sejdinovic
Gatsby Computational Neuroscience Unit, UCL
17 Queen Square, London WC1N 3AR
dino.sejdinovic@gmail.com
Arthur Gretton
... | 5452 |@word trial:1 version:6 arcones:1 seems:2 stronger:1 extinction:3 norm:1 open:2 covariance:2 decomposition:1 prokhorov:4 brightness:1 tr:2 harder:1 reduction:1 series:18 contains:1 document:2 bootstrapped:16 rkhs:5 bradley:1 com:4 comparing:3 z2:4 si:1 gmail:3 lang:1 written:3 universality:1 drop:4 designed:1 v:4... |
4,919 | 5,453 | (Almost) No Label No Cry
Giorgio Patrini1,2 , Richard Nock1,2 , Paul Rivera1,2 , Tiberio Caetano1,3,4
Australian National University1 , NICTA2 , University of New South Wales3 , Ambiata4
Sydney, NSW, Australia
{name.surname}@anu.edu.au
Abstract
In Learning with Label Proportions (LLP), the objective is to learn a sup... | 5453 |@word repository:1 inversion:1 stronger:1 proportion:28 norm:3 km:1 d2:3 grey:1 bn:1 decomposition:1 nsw:1 pick:1 rivera:1 tr:3 shot:8 reduction:2 initial:1 liu:2 contains:2 lichman:1 hereafter:2 wj2:1 seriously:1 denoting:1 outperforms:2 freitas:1 current:1 comparing:1 beygelzimer:1 mushroom:1 follower:1 written... |
4,920 | 5,454 | Consistent Binary Classification with Generalized
Performance Metrics
Oluwasanmi Koyejo?
Department of Psychology,
Stanford University
sanmi@stanford.edu
Nagarajan Natarajan?
Department of Computer Science,
University of Texas at Austin
naga86@cs.utexas.edu
Pradeep Ravikumar
Department of Computer Science,
University... | 5454 |@word mild:1 briefly:1 c0:8 suitably:1 open:2 cha:1 d2:14 hu:1 citeseer:1 ld:2 score:3 selecting:1 tuned:1 b01:4 dx:4 john:1 fn:13 wx:1 enables:2 designed:2 plot:1 intelligence:2 hyuk:1 characterization:2 zhang:2 mathematical:1 constructed:1 c2:10 prove:1 combine:2 jac:3 indeed:1 expected:3 multi:5 euters:3 ming:... |
4,921 | 5,455 | Extended and Unscented Gaussian Processes
Daniel M. Steinberg
NICTA
daniel.steinberg@nicta.com.au
Edwin V. Bonilla
The University of New South Wales
e.bonilla@unsw.edu.au
Abstract
We present two new methods for inference in Gaussian process (GP) models
with general nonlinear likelihoods. Inference is based on a varia... | 5455 |@word cnn:4 version:1 inversion:11 nd:1 egp:17 linearized:8 bn:4 covariance:7 decomposition:1 tr:3 carry:1 moment:1 initial:1 series:7 daniel:2 interestingly:1 dubourg:1 outperforms:1 existing:2 ka:2 com:1 marquardt:2 yet:1 written:1 fn:4 numerical:2 happen:1 analytic:1 update:14 fund:1 intelligence:2 guess:1 par... |
4,922 | 5,456 | Hamming Ball Auxiliary Sampling for Factorial
Hidden Markov Models
Christopher Yau
Wellcome Trust Centre for Human Genetics
University of Oxford
cyau@well.ox.ac.uk
Michalis K. Titsias
Department of Informatics
Athens University of Economics and Business
mtitsias@aueb.gr
Abstract
We introduce a novel sampling algorith... | 5456 |@word version:2 polynomial:2 seems:1 simulation:2 crucially:1 decomposition:1 pick:1 solid:3 electronics:1 configuration:8 series:2 contains:1 initial:2 tuned:1 outperforms:1 current:8 disaggregation:10 comparing:1 activation:1 written:1 must:3 john:1 periodically:1 subsequent:1 additive:4 informative:1 plot:3 up... |
4,923 | 5,457 | Log-Hilbert-Schmidt metric between positive definite
operators on Hilbert spaces
H`a Quang Minh
Marco San Biagio
Vittorio Murino
Istituto Italiano di Tecnologia
Via Morego 30, Genova 16163, ITALY
{minh.haquang,marco.sanbiagio,vittorio.murino}@iit.it
Abstract
This paper introduces a novel mathematical and computationa... | 5457 |@word h:33 kulis:1 determinant:2 version:8 briefly:1 ixx:2 norm:9 stronger:1 open:1 rgb:1 covariance:35 decomposition:2 tr:13 contains:2 cherian:1 series:1 salzmann:2 rkhs:17 outperforms:4 current:4 recovered:1 selected:2 short:1 core:1 coarse:1 gx:4 uppsala:1 zhang:1 mathematical:8 quang:1 constructed:1 along:1 ... |
4,924 | 5,458 | Robust Classi?cation Under Sample Selection Bias
Anqi Liu
Department of Computer Science
University of Illinois at Chicago
Chicago, IL 60607
aliu33@uic.edu
Brian D. Ziebart
Department of Computer Science
University of Illinois at Chicago
Chicago, IL 60607
bziebart@uic.edu
Abstract
In many important machine learning a... | 5458 |@word mild:1 repository:2 version:1 norm:1 proportion:1 nd:1 covariance:2 eld:2 solid:2 moment:11 liu:1 contains:1 lichman:1 com:1 jaynes:1 anqi:1 mushroom:1 scatter:1 must:1 john:2 chicago:4 cant:1 plot:1 selected:1 weighing:1 amir:1 cult:2 xk:1 mccallum:1 short:2 provides:4 direct:3 become:1 prove:2 advocate:1 ... |
4,925 | 5,459 | Tree-structured Gaussian Process Approximations
Thang Bui
Richard Turner
tdb40@cam.ac.uk
ret26@cam.ac.uk
Computational and Biological Learning Lab, Department of Engineering
University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK
Abstract
Gaussian process regression can be accelerated by constructing a sm... | 5459 |@word proceeded:1 version:4 briefly:1 km:6 d2:1 seek:1 covariance:12 decomposition:1 eng:1 concise:1 g050821:1 recursively:1 ld:9 moment:1 reduction:1 series:11 score:1 selecting:2 outperforms:1 existing:1 current:3 comparing:1 surprising:1 must:3 fn:9 subsequent:1 numerical:1 happen:1 sdes:1 remove:1 plot:1 upda... |
4,926 | 546 | Interpretation of Artificial Neural Networks:
Mapping Knowledge-Based Neural Networks into Rules
Geoffrey Towell
Jude W. Shavlik
Computer Sciences Department
U ni versity of Wisconsin
Madison, WI 53706
Abstract
We propose and empirically evaluate a method for the extraction of expertcomprehensible rules from traine... | 546 |@word retraining:1 tat:1 simplifying:1 fonn:1 thereby:1 minus:12 solid:1 initial:8 comparing:1 nt:21 nowlan:2 conjunctive:1 must:2 refines:1 eleven:1 designed:1 plot:1 intelligence:4 fewer:1 weighing:1 provides:1 contribute:1 location:1 org:1 simpler:1 consists:1 eleventh:1 sacrifice:1 nto:1 behavior:2 nor:1 versi... |
4,927 | 5,460 | Best-Arm Identi?cation in Linear Bandits
Marta Soare
Alessandro Lazaric
R?mi Munos? ?
INRIA Lille ? Nord Europe, SequeL Team
{marta.soare,alessandro.lazaric,remi.munos}@inria.fr
Abstract
We study the best-arm identi?cation problem in linear bandit, where the rewards
of the arms depend linearly on an unknown parameter ... | 5460 |@word exploitation:3 version:3 norm:2 proportion:1 open:1 seek:1 r:1 simulation:1 ality:1 soare:3 kalyanakrishnan:1 jacob:1 arti:1 contains:1 xnj:1 freitas:1 current:1 contextual:1 jinbo:1 chu:1 written:1 john:1 numerical:2 partition:1 informative:1 cant:2 shape:1 designed:2 update:1 n0:1 implying:1 half:1 select... |
4,928 | 5,461 | Bounded Regret for Finite-Armed Structured Bandits
R?emi Munos
INRIA
Lille, France1
remi.munos@inria.fr
Tor Lattimore
Department of Computing Science
University of Alberta, Canada
tlattimo@ualberta.ca
Abstract
We study a new type of K-armed bandit problem where the expected return of
one arm may depend on the return... | 5461 |@word innovates:1 exploitation:1 version:1 suitably:1 open:1 calculus:1 attainable:1 liu:1 contains:1 tuned:1 ours:1 ecole:1 current:1 comparing:1 surprising:1 must:4 written:2 attracted:1 john:1 ronald:1 remove:1 drop:1 aside:1 intelligence:1 greedy:1 revisited:1 along:1 constructed:1 prove:2 dan:1 inside:1 theo... |
4,929 | 5,462 | Efficient learning by implicit exploration in bandit
problems with side observations
Tom?as? Koc?ak
Gergely Neu
Michal Valko
R?emi Munos?
SequeL team, INRIA Lille ? Nord Europe, France
{tomas.kocak,gergely.neu,michal.valko,remi.munos}@inria.fr
Abstract
We consider online learning problems under a a partial observabil... | 5462 |@word version:2 tedious:1 subscriber:1 pick:1 incurs:3 accommodate:2 necessity:1 series:1 selecting:2 tuned:1 current:1 com:1 michal:2 update:1 resampling:3 bart:5 implying:1 selected:2 stationary:1 warmuth:3 accordingly:1 mannor:7 node:7 revisited:1 clarified:1 unbounded:1 along:4 constructed:1 welldefined:1 con... |
4,930 | 5,463 | Learning to Optimize via
Information-Directed Sampling
Daniel Russo
Stanford University
Stanford, CA 94305
djrusso@stanford.edu
Benjamin Van Roy
Stanford University
Stanford, CA 94305
bvr@stanford.edu
Abstract
We propose information-directed sampling ? a new algorithm for online optimization problems in which a deci... | 5463 |@word trial:3 exploitation:4 version:4 illustrating:1 briefly:1 seems:1 norm:1 logit:1 c0:2 open:1 simulation:5 seek:1 attainable:1 carry:1 reduction:2 initial:1 contains:1 selecting:3 daniel:1 tuned:5 offering:2 outperforms:3 past:1 freitas:1 current:1 contextual:1 surprising:1 must:3 written:1 john:1 numerical:... |
4,931 | 5,464 | Bayesian Inference for Structured Spike and
Slab Priors
Michael Riis Andersen, Ole Winther & Lars Kai Hansen
DTU Compute, Technical University of Denmark
DK-2800 Kgs. Lyngby, Denmark
{miri, olwi, lkh}@dtu.dk
Abstract
Sparse signal recovery addresses the problem of solving underdetermined
linear inverse problems subjec... | 5464 |@word oostenveld:1 inversion:3 norm:2 nd:1 tedious:1 d2:1 grey:3 covariance:17 jacob:1 outlook:1 delgado:1 moment:5 series:4 mosher:1 amp:10 o2:1 z2:3 activation:1 numerical:4 partition:1 dupont:1 cis:1 designed:1 update:10 rd2:1 precaution:1 intelligence:1 fewer:1 isotropic:1 parametrization:1 provides:2 beaucha... |
4,932 | 5,465 | Estimation with Norm Regularization
Arindam Banerjee
Sheng Chen
Farideh Fazayeli
Vidyashankar Sivakumar
Department of Computer Science & Engineering
University of Minnesota, Twin Cities
{banerjee,shengc,farideh,sivakuma}@cs.umn.edu
Abstract
Analysis of non-asymptotic estimation error and structured statistical re... | 5465 |@word version:3 norm:66 suitably:3 calculus:1 covariance:4 tr:4 contains:1 series:2 interestingly:6 past:2 existing:4 yet:1 mesh:1 subsequent:2 pertinent:1 implying:4 isotropic:30 provides:1 characterization:9 mathematical:2 c2:3 direct:1 expected:1 behavior:1 considering:2 becomes:1 pof:1 bounded:4 argmin:1 subs... |
4,933 | 5,466 | Efficient Sampling for Learning Sparse Additive
Models in High Dimensions
Hemant Tyagi
ETH Z?urich
htyagi@inf.ethz.ch
Andreas Krause
ETH Z?urich
krausea@ethz.ch
Bernd G?artner
ETH Z?urich
gaertner@inf.ethz.ch
Abstract
We consider theP
problem of learning sparse additive models, i.e., functions of the
form: f (x) = ... | 5466 |@word faculty:1 polynomial:5 stronger:2 norm:9 suitably:1 disk:1 d2:6 simulation:3 liu:1 series:1 denoting:3 rkhs:3 ours:1 existing:2 recovered:3 dx:4 mesh:2 numerical:2 realistic:1 additive:14 enables:1 resampling:2 implying:2 provides:2 detecting:1 math:1 location:1 zhang:1 five:1 along:9 h4:2 become:2 differen... |
4,934 | 5,467 | Deterministic Symmetric Positive Semidefinite Matrix
Completion
William E. Bishop1,2 , Byron M. Yu2,3,4
Machine Learning, 2 Center for the Neural Basis of Cognition,
3
Biomedical Engineering, 4 Electrical and Computer Engineering
Carnegie Mellon University
{wbishop, byronyu}@cmu.edu
1
Abstract
We consider the problem... | 5467 |@word illustrating:1 briefly:1 version:2 norm:3 open:1 simulation:4 covariance:6 citeseer:1 nystr:3 boundedness:1 reduction:3 initial:2 configuration:1 contains:1 necessity:4 existing:3 current:1 recovered:4 ka:1 yet:1 must:3 john:2 sanjiv:1 drop:1 plot:1 intelligence:1 selected:1 shababo:1 ith:1 record:2 certifi... |
4,935 | 5,468 | Active Regression by Stratification
Sivan Sabato
Department of Computer Science
Ben Gurion University, Beer Sheva, Israel
sabatos@cs.bgu.ac.il
Remi Munos?
INRIA
Lille, France
remi.munos@inria.fr
Abstract
We propose a new active learning algorithm for parametric linear regression with
random design. We provide finite... | 5468 |@word mild:1 version:1 achievable:2 c0:7 open:3 crucially:1 covariance:1 tr:2 boundedness:1 reduction:1 series:2 selecting:1 current:2 ganti:2 comparing:1 beygelzimer:1 dx:8 partition:13 gurion:1 cheap:1 atlas:1 intelligence:2 xk:1 short:1 provides:3 math:1 readability:1 zhang:2 unbounded:1 mathematical:1 direct:... |
4,936 | 5,469 | A Drifting-Games Analysis for Online Learning and
Applications to Boosting
Haipeng Luo
Department of Computer Science
Princeton University
Princeton, NJ 08540
haipengl@cs.princeton.edu
Robert E. Schapire?
Department of Computer Science
Princeton University
Princeton, NJ 08540
schapire@cs.princeton.edu
Abstract
We pro... | 5469 |@word briefly:1 version:5 norm:6 seems:2 open:1 jacob:3 pick:3 concise:1 incurs:1 boundedness:1 recursively:1 reduction:1 series:3 daniel:1 outperforms:1 existing:5 recovered:2 z2:2 luo:2 surprising:2 si:1 assigning:2 tackling:1 yet:1 written:1 dx:1 readily:1 numerical:4 additive:1 treating:1 drop:1 warmuth:3 rea... |
4,937 | 547 | Incrementally Learning Time-varying Half-planes
Anthony Kuh *
Dept. of Electrical Engineering
University of Hawaii at Manoa
Honolulu, ill 96822
Thomas Petsche t
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540
Ronald L. Rivest+
Laboratory for Computer Science
MIT
Cambridge, MA 02139
Abstract
We ... | 547 |@word h:9 version:1 briefly:1 achievable:3 seems:1 simulation:9 eng:1 initial:1 ours:1 current:4 com:1 si:2 yet:1 must:1 cruz:1 ronald:2 subsequent:1 wx:1 benign:12 mislabels:2 hypothesize:3 update:1 half:18 greedy:13 guess:3 warmuth:2 plane:17 node:1 five:1 ucsc:1 direct:1 consists:1 introduce:1 expected:1 ra:2 b... |
4,938 | 5,470 | Distance-Based Network Recovery
under Feature Correlation
David Adametz, Volker Roth
Department of Mathematics and Computer Science
University of Basel, Switzerland
{david.adametz,volker.roth}@unibas.ch
Abstract
We present an inference method for Gaussian graphical models when only pairwise distances of n objects are ... | 5470 |@word determinant:5 version:1 briefly:3 kondor:1 sex:1 d2:3 covariance:6 decomposition:5 thereby:1 tr:2 configuration:4 series:3 score:9 exclusively:1 zuk:2 daniel:1 interestingly:1 bhattacharyya:4 existing:1 unibas:1 current:1 recovered:1 comparing:1 must:6 reminiscent:1 visible:1 blur:1 informative:1 shape:5 en... |
4,939 | 5,471 | Decomposing Parameter Estimation Problems
Khaled S. Refaat, Arthur Choi, Adnan Darwiche
Computer Science Department
University of California, Los Angeles
{krefaat,aychoi,darwiche}@cs.ucla.edu
Abstract
We propose a technique for decomposing the parameter learning problem in
Bayesian networks into independent learning ... | 5471 |@word repository:2 wiesel:1 adnan:6 d2:2 seek:2 decomposition:23 incurs:1 reduction:1 initial:1 lichman:1 interestingly:2 freitas:1 surprising:1 culprit:1 si:5 mushroom:1 yet:2 must:1 dechter:1 partition:5 analytic:2 plot:1 depict:1 stationary:19 intelligence:7 leaf:5 realizing:1 node:11 contribute:2 daphne:1 dn:... |
4,940 | 5,472 | Global Sensitivity Analysis
for MAP Inference in Graphical Models
Jasper De Bock
Ghent University, SYSTeMS
Ghent (Belgium)
Cassio P. de Campos
Queen?s University
Belfast (UK)
Alessandro Antonucci
IDSIA
Lugano (Switzerland)
jasper.debock@ugent.be
c.decampos@qub.ac.uk
alessandro@idsia.ch
Abstract
We study the sens... | 5472 |@word repository:2 polynomial:1 stronger:1 open:1 bn:2 contraction:1 thereby:1 minus:1 outlook:1 harder:1 reduction:1 initial:1 configuration:8 contains:5 series:1 existing:2 yet:1 must:1 dechter:1 numerical:2 happen:1 partition:2 designed:1 plot:1 intelligence:2 selected:2 flare:1 xk:1 short:1 normalising:1 prov... |
4,941 | 5,473 | Multi-scale Graphical Models for Spatio-Temporal
Processes
Firdaus Janoos?
Huseyin Denli
Niranjan Subrahmanya
ExxonMobil Corporate Strategic Research
Annandale, NJ 08801
Abstract
Learning the dependency structure between spatially distributed observations of
a spatio-temporal process is an important problem in many fi... | 5473 |@word version:1 inversion:4 loading:1 nd:1 hyv:1 simulation:2 pressure:12 thereby:4 harder:1 initial:2 series:13 contains:2 current:5 com:1 comparing:1 activation:1 yet:1 hoboken:1 tarantola:1 numerical:1 enables:1 designed:1 progressively:1 stationary:3 discovering:1 selected:1 geologic:2 xk:9 parametrization:2 ... |
4,942 | 5,474 | Active Learning and Best-Response Dynamics
Maria-Florina Balcan
Carnegie Mellon
ninamf@cs.cmu.edu
Emma Cohen
Georgia Tech
ecohen@gatech.edu
Christopher Berlind
Georgia Tech
cberlind@gatech.edu
Kaushik Patnaik
Georgia Tech
kpatnaik3@gatech.edu
Avrim Blum
Carnegie Mellon
avrim@cs.cmu.edu
Le Song
Georgia Tech
lsong@cc.... | 5474 |@word trial:1 briefly:1 version:3 polynomial:2 faculty:1 open:1 seek:1 minus:1 initial:9 configuration:1 daniel:1 ketch:3 current:2 comparing:1 beygelzimer:1 yet:2 mesh:1 partition:1 kdd:1 plot:1 update:46 v:4 half:4 selected:1 intelligence:1 fa9550:1 location:3 mathematical:1 c2:1 become:2 incorrect:6 prove:3 co... |
4,943 | 5,475 | Provable Tensor Factorization with Missing Data
Prateek Jain
Microsoft Research
Bangalore, India
prajain@microsoft.com
Sewoong Oh
Dept. of Industrial and Enterprise Systems Engineering
University of Illinois at Urbana-Champaign
Urbana, IL 61801
swoh@illinois.edu
Abstract
We study the problem of low-rank tensor factor... | 5475 |@word norm:13 stronger:1 c0:2 open:1 simulation:4 crucially:1 decomposition:24 initial:7 celebrated:2 series:1 liu:1 selecting:1 daniel:1 interestingly:1 existing:2 current:3 com:1 recovered:2 surprising:1 refines:1 numerical:8 acar:1 plot:1 update:1 alone:1 intelligence:1 selected:1 guess:1 vanishing:1 caveat:1 ... |
4,944 | 5,476 | Generalized Higher-Order Orthogonal Iteration for
Tensor Decomposition and Completion
Yuanyuan Liu? , Fanhua Shang??, Wei Fan? , James Cheng? , Hong Cheng?
?
Dept. of Systems Engineering and Engineering Management,
The Chinese University of Hong Kong
?
Dept. of Computer Science and Engineering, The Chinese University o... | 5476 |@word kong:3 version:1 norm:27 paredes:1 linearized:1 bn:1 decomposition:21 inpainting:3 boundedness:1 liu:8 series:1 romera:1 past:1 existing:4 outperforms:1 current:1 com:2 recovered:3 optim:1 chu:1 numerical:1 acar:1 update:6 nq:2 core:10 yamada:1 math:2 cse:1 successive:1 pun:2 zhang:1 five:2 along:4 c2:2 dir... |
4,945 | 5,477 | Neural Word Embedding
as Implicit Matrix Factorization
Omer Levy
Department of Computer Science
Bar-Ilan University
omerlevy@gmail.com
Yoav Goldberg
Department of Computer Science
Bar-Ilan University
yoav.goldberg@gmail.com
Abstract
We analyze skip-gram with negative-sampling (SGNS), a word embedding
method introduc... | 5477 |@word multitask:1 version:1 middle:1 norm:1 c0:2 rivlin:1 km:1 solan:1 decomposition:1 pavel:1 contrastive:2 yih:1 harder:1 reduction:1 configuration:2 contains:4 score:1 selecting:2 interestingly:2 outperforms:2 past:1 com:2 contextual:1 gmail:2 must:2 john:3 stemming:1 additive:1 ronan:2 enables:1 christian:1 h... |
4,946 | 5,478 | Scaling-up Importance Sampling for Markov Logic
Networks
Vibhav Gogate
Department of Computer Science
University of Texas at Dallas
vgogate@hlt.utdallas.edu
Deepak Venugopal
Department of Computer Science
University of Texas at Dallas
dxv021000@utdallas.edu
Abstract
Markov Logic Networks (MLNs) are weighted first-ord... | 5478 |@word polynomial:2 vi1:1 adnan:1 d2:3 tried:1 thereby:1 liu:1 contains:3 fa8750:2 existing:2 current:1 partition:3 plot:2 update:2 braz:2 intelligence:7 website:1 ubuntu:1 mln:44 ith:2 core:1 completeness:1 equi:2 successive:1 along:2 constructed:1 c2:2 become:1 symposium:1 darwiche:1 expected:1 p1:4 pkdd:1 black... |
4,947 | 5,479 | Sparse Random Features Algorithm as
Coordinate Descent in Hilbert Space
Ian E.H. Yen 1
Ting-Wei Lin 2
Shou-De Lin 2 Pradeep Ravikumar 1 Inderjit S. Dhillon 1
Department of Computer Science
1: University of Texas at Austin, 2: National Taiwan University
1: {ianyen,pradeepr,inderjit}@cs.utexas.edu,
2: {b97083,sdlin}@c... | 5479 |@word repository:1 polynomial:1 norm:7 hsieh:1 decomposition:6 liblinear:2 selecting:1 rkhs:2 existing:2 current:1 z2:2 nt:6 surprising:1 attracted:1 written:1 additive:1 numerical:1 remove:1 v:1 greedy:10 prohibitive:1 selected:1 warmuth:1 beginning:2 steepest:1 manfred:1 caveat:1 iterates:1 boosting:22 provides... |
4,948 | 548 | Benchmarking Feed-Forward Neural Networks:
Models and Measures
Leonard G. C. Harney
Computing Discipline
Macquarie University
NSW2109
AUSTRALIA
Abstract
Existing metrics for the learning performance of feed-forward neural networks do
not provide a satisfactory basis for comparison because the choice of the training
e... | 548 |@word trial:16 cu:1 proportion:4 jacob:2 nsw:1 dramatic:1 initial:2 initialisation:1 existing:2 must:1 john:3 realistic:1 shape:1 asymptote:1 plot:1 drop:1 reciprocal:2 provides:1 along:1 fitting:2 ry:3 little:1 provided:1 what:2 minimizes:1 developed:1 ti:4 scaled:3 normally:1 unit:1 maximise:1 limit:25 optimised... |
4,949 | 5,480 | Latent Support Measure Machines
for Bag-of-Words Data Classification
Yuya Yoshikawa
Nara Institute of Science and Technology
Nara, 630-0192, Japan
yoshikawa.yuya.yl9@is.naist.jp
Tomoharu Iwata
NTT Communication Science Laboratories
Kyoto, 619-0237, Japan
iwata.tomoharu@lab.ntt.co.jp
Hiroshi Sawada
NTT Service Evolut... | 5480 |@word faculty:6 polynomial:2 proportion:1 advantageous:1 covariance:1 decomposition:1 yih:1 moment:3 wrapper:1 liu:1 document:45 rkhs:4 outperforms:1 existing:2 current:4 comparing:1 com:1 john:2 numerical:2 hofmann:1 krikamol:1 intelligence:1 plane:1 ith:7 short:1 yamada:1 blei:3 multiset:2 simpler:1 zhang:1 yos... |
4,950 | 5,481 | Fast Prediction for Large-Scale Kernel Machines
Cho-Jui Hsieh, Si Si, and Inderjit S. Dhillon
Department of Computer Science
University of Texas at Austin
Austin, TX 78712 USA
{cjhsieh,ssi,inderjit}@cs.utexas.edu
Abstract
Kernel machines such as kernel SVM and kernel ridge regression usually construct high quality mo... | 5481 |@word polynomial:9 nd:1 hsieh:6 decomposition:2 covariance:1 nystr:35 reduction:2 liblinear:2 configuration:1 series:1 selecting:3 interestingly:1 outperforms:1 ka:1 com:1 si:4 written:1 additive:1 partition:2 kdd:1 designed:1 v:5 stationary:4 greedy:3 fewer:1 selected:2 leaf:2 ntrain:7 half:1 plane:2 core:2 ck2:... |
4,951 | 5,482 | Testing Unfaithful Gaussian Graphical Models
Sekhar Tatikonda
Department of Electrical Engineering
Yale University
17 Hillhouse Ave, New Haven, CT 06511
sekhar.tatikonda@yale.edu
De Wen Soh
Department of Electrical Engineering
Yale University
17 Hillhouse Ave, New Haven, CT 06511
dewen.soh@yale.edu
Abstract
The glob... | 5482 |@word determinant:3 version:1 inversion:1 covariance:19 mention:1 series:3 surprising:1 must:8 partition:9 j1:9 intelligence:1 characterization:1 provides:1 completeness:1 node:51 mathematical:1 along:1 viable:1 dewen:1 prove:1 consists:1 eleventh:1 paragraph:1 indeed:1 expected:1 detects:1 considering:1 becomes:... |
4,952 | 5,483 | Sampling for Inference in Probabilistic Models with
Fast Bayesian Quadrature
Roman Garnett
Knowledge Discovery and Machine Learning
University of Bonn
rgarnett@uni-bonn.de
Tom Gunter, Michael A. Osborne
Engineering Science
University of Oxford
{tgunter,mosb}@robots.ox.ac.uk
Philipp Hennig
MPI for Intelligent Systems
... | 5483 |@word exploitation:2 polynomial:1 simulation:1 crucially:2 covariance:9 tr:1 shading:1 disappointingly:1 moment:8 reduction:4 inefficiency:1 series:1 initial:2 selecting:5 tuned:1 existing:4 comparing:1 dx:4 numerical:3 partition:2 informative:3 visible:2 cheap:4 analytic:3 afield:1 designed:1 extrapolating:1 plo... |
4,953 | 5,484 | Do Deep Nets Really Need to be Deep?
Rich Caruana
Microsoft Research
rcaruana@microsoft.com
Lei Jimmy Ba
University of Toronto
jimmy@psi.utoronto.ca
Abstract
Currently, deep neural networks are the state of the art on problems such as speech
recognition and computer vision. In this paper we empirically demonstrate t... | 5484 |@word cnn:24 compression:17 achievable:3 logit:1 decomposition:1 contains:1 score:1 interestingly:1 outperforms:1 err:1 current:3 com:1 surprising:2 activation:2 yet:3 must:1 gpu:2 written:1 john:1 happen:1 informative:1 enables:1 asymptote:2 designed:2 v:3 intelligence:1 fewer:3 core:1 provides:1 toronto:2 sigmo... |
4,954 | 5,485 | Deep Convolutional Neural Network for Image
Deconvolution
Li Xu ?
Lenovo Research & Technology
xulihk@lenovo.com
Jimmy SJ. Ren
Lenovo Research & Technology
jimmy.sj.ren@gmail.com
Jiaya Jia
The Chinese University of Hong Kong
leojia@cse.cuhk.edu.hk
Ce Liu
Microsoft Research
celiu@microsoft.com
Abstract
Many fundament... | 5485 |@word kong:2 cnn:28 version:1 inversion:11 compression:8 middle:1 disk:4 decomposition:2 inpainting:2 accommodate:1 reduction:1 necessity:1 liu:1 contains:3 configuration:1 series:2 initial:1 shum:1 tuned:1 ours:4 suppressing:1 deconvolutional:2 document:1 outperforms:1 existing:6 com:3 gmail:1 yet:2 written:1 ad... |
4,955 | 5,486 | Identifying and attacking the saddle point
problem in high-dimensional non-convex optimization
Yann N. Dauphin Razvan Pascanu Caglar Gulcehre Kyunghyun Cho
Universit?e de Montr?eal
dauphiya@iro.umontreal.ca, r.pascanu@gmail.com,
gulcehrc@iro.umontreal.ca, kyunghyun.cho@umontreal.ca
Yoshua Bengio
Universit?e de Montr?e... | 5486 |@word version:2 norm:3 calculus:1 willing:1 confirms:1 tried:1 recapitulate:1 covariance:2 decomposition:1 pick:1 sgd:13 thereby:1 initial:1 series:1 interestingly:1 outperforms:2 existing:1 current:1 com:1 trustworthy:1 gmail:1 must:3 gpu:1 numerical:4 distant:1 shape:2 remove:1 designed:1 plot:1 depict:1 update... |
4,956 | 5,487 | Learning with Pseudo-Ensembles
Ouais Alsharif
McGill University
Montreal, QC, Canada
ouais.alsharif@gmail.com
Philip Bachman
McGill University
Montreal, QC, Canada
phil.bachman@gmail.com
Doina Precup
McGill University
Montreal, QC, Canada
dprecup@cs.mcgill.ca
Abstract
We formalize the notion of a pseudo-ensemble, a... | 5487 |@word cnn:2 norm:4 c0:1 tedious:1 instruction:1 seek:2 tried:1 bachman:4 covariance:1 sgd:3 thereby:1 recursively:1 reduction:2 initial:2 contains:1 rippel:1 rkhs:1 ours:1 document:1 outperforms:1 existing:4 past:2 com:5 activation:1 gmail:2 yet:1 written:3 parsing:1 gpu:1 stemming:1 ctn:5 partition:1 aside:1 v:1... |
4,957 | 5,488 | On the Information Theoretic Limits
of Learning Ising Models
Karthikeyan Shanmugam1? , Rashish Tandon2? , Alexandros G. Dimakis1? , Pradeep Ravikumar2?
1
Department of Electrical and Computer Engineering, 2 Department of Computer Science
The University of Texas at Austin, USA
?
karthiksh@utexas.edu, ? rashish@cs.utexa... | 5488 |@word version:2 polynomial:1 stronger:2 physik:1 d2:1 p0:1 pg:2 harder:1 liu:1 uncovered:2 series:1 selecting:1 existing:1 yet:2 must:2 john:1 subsequent:1 happen:1 partition:1 interpretable:1 joy:1 amir:1 dembo:1 short:3 alexandros:1 certificate:1 provides:2 characterization:6 node:15 zhang:1 narayana:1 rc:1 mat... |
4,958 | 5,489 | A Probabilistic Framework for Multimodal Retrieval
using Integrative Indian Buffet Process
Larry S. Davis
Institute for Advanced Computer Studies
University of Maryland
College Park, MD 20742 USA
lsd@umiacs.umd.edu
Bahadir Ozdemir
Department of Computer Science
University of Maryland
College Park, MD 20742 USA
ozdemi... | 5489 |@word kulis:1 repository:1 judgement:1 proportion:1 integrative:9 vldb:1 tried:1 covariance:3 jacob:1 tr:2 initial:3 contains:2 selecting:1 tuned:1 outperforms:2 existing:2 imaginary:1 comparing:1 luo:1 written:1 kdd:1 analytic:1 gist:1 update:1 hash:2 generative:1 intelligence:3 discovering:1 short:2 record:1 pr... |
4,959 | 549 | A Neural Net Model for Adaptive Control of
Saccadic Accuracy by Primate Cerebellum and
Brainstem
Paul Deana, John E. W. Mayhew and Pat Langdon
Department of Psychology a and Artificial Intelligence
Vision Research Unit, University of Sheffield,
Sheffield S10 2TN, England.
Abstract
Accurate saccades require interactio... | 549 |@word trial:4 retraining:1 simulation:3 thereby:1 ulus:1 initial:9 configuration:1 foveal:1 series:2 l__:1 optican:2 langdon:6 current:1 must:1 olive:2 john:1 physiol:1 subsequent:1 realistic:1 plasticity:3 analytic:1 motor:5 infant:4 intelligence:1 tenn:1 deubel:2 provides:1 coarse:1 math:1 location:2 organising:... |
4,960 | 5,490 | Multivariate f -Divergence Estimation With
Confidence
Alfred O. Hero III
Department of EECS
University of Michigan
Ann Arbor, MI
hero@eecs.umich.edu
Kevin R. Moon
Department of EECS
University of Michigan
Ann Arbor, MI
krmoon@umich.edu
Abstract
The problem of f -divergence estimation is important in the fields of mac... | 5490 |@word neurophysiology:1 repository:2 compression:1 achievable:1 simulation:2 covariance:7 simplifying:1 series:2 lichman:1 z2:1 comparing:5 dx:5 must:2 john:1 numerical:1 partition:3 enables:2 plot:3 intelligence:2 kandasamy:1 provides:2 math:2 mathematical:3 constructed:1 c2:4 symposium:1 walther:1 prove:3 manne... |
4,961 | 5,491 | Parallel Double Greedy Submodular Maximization
Xinghao Pan1 Stefanie Jegelka1 Joseph Gonzalez1 Joseph Bradley1 Michael I. Jordan1,2
1
Department of Electrical Engineering and Computer Science, and 2 Department of Statistics
University of California, Berkeley, Berkeley, CA USA 94720
{xinghao,stefje,jegonzal,josephkb,jor... | 5491 |@word uee:1 arabic:5 version:23 compression:1 bf:1 open:1 cipar:1 reduction:2 contains:2 series:1 siebel:1 ue1:1 document:2 fa8750:1 horvitz:1 comparing:2 boldi:3 must:1 determinantal:2 partition:1 predetermined:1 kdd:1 enables:2 remove:1 update:1 greedy:44 selected:2 item:5 serialized:1 core:2 provides:1 tahoe:1... |
4,962 | 5,492 | From MAP to Marginals: Variational Inference in
Bayesian Submodular Models
Andreas Krause
Department of Computer Science
ETH Z?urich
krausea@ethz.ch
Josip Djolonga
Department of Computer Science
ETH Z?urich
josipd@inf.ethz.ch
Abstract
Submodular optimization has found many applications in machine learning and
beyond... | 5492 |@word kohli:1 determinant:1 faculty:1 cu:4 polynomial:2 norm:2 suitably:1 semidifferential:1 open:2 closure:1 seek:1 decomposition:1 pick:5 carry:1 document:4 interestingly:1 existing:1 ka:2 si:8 written:1 determinantal:5 additive:1 partition:15 enables:1 remove:1 plot:1 update:3 greedy:5 intelligence:5 selected:... |
4,963 | 5,493 | Stochastic Network Design in Bidirected Trees
Xiaojian Wu1
1
Daniel Sheldon1,2
Shlomo Zilberstein1
School of Computer Science, University of Massachusetts Amherst
2
Department of Computer Science, Mount Holyoke College
Abstract
We investigate the problem of stochastic network design in bidirected trees. In this
pr... | 5493 |@word h:12 version:3 pw:3 polynomial:8 hu:16 seek:1 propagate:1 r:7 decomposition:1 pick:1 thereby:1 harder:2 recursively:5 n8:4 initial:1 contains:3 leandro:1 daniel:6 outperforms:3 existing:2 yajun:1 atlantic:1 discretization:7 bradley:1 assigning:1 written:1 must:1 herring:1 realistic:1 shlomo:4 trout:1 cheap:... |
4,964 | 5,494 | Constrained convex minimization
via model-based excessive gap
Quoc Tran-Dinh and Volkan Cevher
Laboratory for Information and Inference Systems (LIONS)
?
Ecole
Polytechnique F?ed?erale de Lausanne (EPFL), CH1015-Lausanne, Switzerland
{quoc.trandinh, volkan.cevher}@epfl.ch
Abstract
We introduce a model-based excessive... | 5494 |@word version:2 polynomial:1 norm:3 c0:4 linearized:1 bn:2 decomposition:4 initial:2 score:1 ecole:1 tuned:1 prefix:1 existing:3 ka:21 optim:3 toh:1 yet:1 dx:16 must:1 chu:1 numerical:6 wx:3 padmm:5 tailoring:1 update:11 v:4 instantiate:1 xk:33 volkan:2 num:1 iterates:4 characterization:4 math:2 kaxk:2 org:2 math... |
4,965 | 5,495 | Learning to Search in Branch-and-Bound Algorithms?
He He Hal Daum?e III
Department of Computer Science
University of Maryland
College Park, MD 20740
{hhe,hal}@cs.umd.edu
Jason Eisner
Department of Computer Science
Johns Hopkins University
Baltimore, MD 21218
jason@cs.jhu.edu
Abstract
Branch-and-bound is a widely used... | 5495 |@word trial:1 version:4 twelfth:1 open:2 termination:1 pieter:1 hsieh:1 pick:1 harder:1 recursively:2 reduction:2 liblinear:2 liu:1 contains:2 score:6 configuration:1 daniel:1 tuned:2 ours:3 past:2 err:2 current:10 yet:2 must:1 parsing:2 john:1 fn:3 realistic:1 partition:2 informative:1 dechter:1 cheap:1 designed... |
4,966 | 5,496 | Convex Deep Learning via Normalized Kernels
?
Ozlem
Aslan
Dept of Computing Science
University of Alberta, Canada
ozlem@cs.ualberta.ca
Xinhua Zhang
Machine Learning Group
NICTA and ANU
xizhang@nicta.com.au
Dale Schuurmans
Dept of Computing Science
University of Alberta, Canada
dale@cs.ualberta.ca
Abstract
Deep learn... | 5496 |@word version:1 polynomial:3 norm:2 replicate:1 km:1 a02:1 crucially:1 tried:1 invoking:1 tr:39 accommodate:1 harder:1 initial:2 substitution:1 interestingly:1 current:1 com:1 recovered:2 yet:1 chu:1 must:7 written:2 parsing:1 devin:1 subsequent:1 hajnal:1 designed:1 treating:1 update:5 aside:1 stationary:3 gener... |
4,967 | 5,497 | A Block-Coordinate Descent Approach for
Large-scale Sparse Inverse Covariance Estimation
Eran Treister??
Computer Science, Technion, Israel
and Earth and Ocean Sciences, UBC
Vancouver, BC, V6T 1Z2, Canada
eran@cs.technion.ac.il
Javier Turek?
Department of Computer Science
Technion, Israel Institute of Technology
Tech... | 5497 |@word determinant:4 version:2 inversion:1 seems:1 norm:1 nd:1 seek:1 covariance:29 natsoulis:1 hsieh:3 tr:5 initial:1 contains:1 series:1 denoting:1 bc:1 outperforms:2 existing:3 current:1 z2:1 ka:4 activation:2 toh:1 numerical:3 partition:2 enables:1 treating:6 drop:1 update:10 accordingly:1 core:1 completeness:... |
4,968 | 5,498 | New Rules for Domain Independent
Lifted MAP Inference
Happy Mittal, Prasoon Goyal
Dept. of Comp. Sci. & Engg.
I.I.T. Delhi, Hauz Khas
New Delhi, 110016, India
Vibhav Gogate
Dept. of Comp. Sci.
Univ. of Texas Dallas
Richardson, TX 75080, USA
Parag Singla
Dept. of Comp. Sci. & Engg.
I.I.T. Delhi, Hauz Khas
New Delhi, 1... | 5498 |@word exploitation:1 version:6 inversion:1 polynomial:3 open:2 harder:1 reduction:1 substitution:1 series:1 contains:3 daniel:1 fa8750:1 existing:9 recovered:2 com:2 current:1 gmail:1 conjunctive:1 written:1 engg:2 remove:2 plot:2 v:4 braz:2 leaf:1 website:1 intelligence:4 amir:2 mln:64 beginning:1 ith:2 core:1 c... |
4,969 | 5,499 | An Integer Polynomial Programming Based
Framework for Lifted MAP Inference
Somdeb Sarkhel, Deepak Venugopal
Computer Science Department
The University of Texas at Dallas
{sxs104721,dxv021000}@utdallas.edu
Parag Singla
Department of CSE
I.I.T. Delhi
parags@cse.iitd.ac.in
Vibhav Gogate
Computer Science Department
The U... | 5499 |@word polynomial:18 open:1 vldb:1 heuristically:3 closure:1 decomposition:7 mention:1 reduction:2 ilps:1 bootstrapped:1 fa8750:2 existing:5 recovered:1 z2:5 si:2 chicago:1 partition:1 drop:1 treating:1 update:2 v:2 braz:1 intelligence:1 amir:1 mln:58 core:1 caveat:1 equi:1 cse:2 node:2 five:2 mathematical:2 dn:2 ... |
4,970 | 55 | 164
MATHEMATICAL ANALYSIS OF LEARNING BEHAVIOR
OF NEURONAL MODELS
By
JOHN Y. CHEUNG
MASSOUD OMIDVAR
SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
UNIVERSITY OF OKLAHOMA
NORMAN, OK 73019
Presented to the IEEE Conference on "Neural Information Processing SystemsNatural and Synthetic," Denver, November ~12, 198... | 55 |@word neurophysiology:1 determinant:1 verify:3 classical:4 polynomial:1 norman:2 hence:4 analytically:1 objective:1 radius:1 occurs:1 hu:1 simulation:7 primary:2 rt:1 traditional:1 during:1 recurrence:1 please:1 lou:1 boundedness:1 simulated:1 initial:2 omidvar:3 contains:1 complete:1 adjusted:1 past:2 mm:1 conside... |
4,971 | 550 | English Alphabet Recognition
with Telephone Speech
Mark Fanty, Ronald A. Cole and Krist Roginski
Center for Spoken Language Understanding
Oregon Graduate Institute of Science and Technology
19600 N.W. Von Neumann Dr., Beaverton, OR 97006
Abstract
A recognition system is reported which recognizes names spelled over th... | 550 |@word proportion:2 instruction:1 closure:2 initial:2 series:3 score:5 contains:1 past:2 current:2 comparing:1 must:2 ronald:1 speakerindependent:1 designed:2 discrimination:1 selected:3 short:3 record:1 provides:1 node:3 location:4 burst:5 profound:1 consists:5 combine:1 manner:1 frequently:1 automatically:1 littl... |
4,972 | 5,500 | Positive Curvature and Hamiltonian Monte Carlo
Simon Rubinstein-Salzedo?
Susan Holmes
Department of Statistics
Stanford University
{cseiler,simonr}@stanford.edu, susan@stat.stanford.edu
Christof Seiler
Abstract
The Jacobi metric introduced in mathematical physics can be used to analyze
Hamiltonian Monte Carlo (HMC). I... | 5500 |@word briefly:1 version:1 mri:1 polynomial:1 norm:2 d2:1 cos2:2 simulation:9 covariance:4 jacob:1 pick:2 initial:1 current:1 tackling:1 dx:3 must:2 reminiscent:1 written:1 john:1 portuguese:1 numerical:5 shape:2 enables:2 plot:4 atlas:2 stationary:3 selected:1 nq:5 plane:1 xk:1 hamiltonian:16 smith:1 i100:1 alexa... |
4,973 | 5,501 | Bayes-Adaptive Simulation-based Search
with Value Function Approximation
Arthur Guez?,1,2
?
Nicolas Heess2
aguez@google.com
1
David Silver2
Gatsby Unit, UCL
2
Peter Dayan1
Google DeepMind
Abstract
Bayes-adaptive planning offers a principled solution to the explorationexploitation trade-off under model uncerta... | 5501 |@word h:2 exploitation:2 version:7 briefly:1 advantageous:1 nd:1 adrian:1 pieter:1 simulation:46 crucially:1 tried:1 covariance:1 p0:3 dramatic:1 recursively:2 reduction:1 initial:3 configuration:1 inefficiency:1 outperforms:2 past:3 existing:2 current:13 com:1 discretization:5 yet:2 guez:2 must:4 readily:1 wiewi... |
4,974 | 5,502 | Altitude Training:
Strong Bounds for Single-Layer Dropout
Stefan Wager? , William Fithian? , Sida Wang? , and Percy Liang?,?
Departments of Statistics? and Computer Science?
Stanford University, Stanford, CA-94305, USA
{swager, wfithian}@stanford.edu, {sidaw, pliang}@cs.stanford.edu
Abstract
Dropout training, original... | 5502 |@word mild:1 worsens:1 bigram:1 triggs:1 open:1 simulation:2 incurs:1 solid:1 harder:2 reduction:1 contains:1 score:6 wj2:5 document:47 err:26 intriguing:1 must:1 john:1 additive:2 enables:1 minmin:1 designed:1 v:1 generative:29 half:3 intelligence:2 advancement:1 amir:1 indicative:3 mccallum:2 short:2 lr:1 blei:... |
4,975 | 5,503 | Simultaneous Model Selection and Optimization
through Parameter-free Stochastic Learning
Francesco Orabona?
Yahoo! Labs
New York, USA
francesco@orabona.com
Abstract
Stochastic gradient descent algorithms for training linear and kernel predictors
are gaining more and more importance, thanks to their scalability. While... | 5503 |@word version:1 polynomial:1 stronger:1 norm:6 seems:3 replicate:1 open:1 hu:1 pick:1 sgd:8 tuned:3 rkhs:6 neeman:1 past:1 scovel:1 com:1 luo:1 yet:3 written:1 chicago:1 designed:1 update:4 selected:1 advancement:2 core:2 boosting:1 math:2 zhang:1 differential:1 viable:1 ik:2 prove:5 khk:6 theoretically:1 x0:6 ne... |
4,976 | 5,504 | On the Statistical Consistency of Plug-in Classifiers
for Non-decomposable Performance Measures
Harikrishna Narasimhan? , Rohit Vaish? , Shivani Agarwal
Department of Computer Science and Automation
Indian Institute of Science, Bangalore ? 560012, India
{harikrishna, rohit.vaish, shivani}@csa.iisc.ernet.in
Abstract
W... | 5504 |@word mild:2 h:7 repository:2 version:3 cpe:12 proportion:2 suitably:4 seek:2 crucially:1 decomposition:1 covariance:1 minus:1 liu:1 contains:4 score:1 outperforms:1 ts2:13 assigning:3 written:1 kdd:2 intelligence:2 short:1 chua:1 zhang:1 mathematical:1 dn:6 constructed:1 enterprise:1 prove:2 expected:3 pkdd:1 gr... |
4,977 | 5,505 | Exponential Concentration of a Density Functional
Estimator
Shashank Singh
Statistics & Machine Learning Departments
Carnegie Mellon University
Pittsburgh, PA 15213
sss1@andrew.cmu.edu
Barnab?as P?oczos
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
bapoczos@cs.cmu.edu
Abstract
We analyze... | 5505 |@word cmi:12 neurophysiology:1 sss1:1 version:1 norm:5 nd:2 open:1 calculus:1 boundedness:1 liu:1 dx:19 john:1 remove:1 kandasamy:1 intelligence:2 xk:22 boosting:2 mcdiarmid:5 zhang:1 mathematical:3 become:2 ik:1 scholkopf:1 prove:5 hellinger:1 manner:2 peng:1 ica:1 expected:1 p1:77 dist:1 multi:1 company:2 littl... |
4,978 | 5,506 | Deconvolution of High Dimensional Mixtures via
Boosting, with Application to Diffusion-Weighted
MRI of Human Brain
Charles Y. Zheng
Department of Statistics
Stanford University
Stanford, CA 94305
snarles@stanford.edu
Franco Pestilli
Department of Psychological and Brain Sciences
Indiana University, Bloomington, IN 474... | 5506 |@word mild:1 version:1 mri:5 f32:1 norm:1 clts:1 suitably:1 simulation:10 seek:1 eng:1 solid:5 initial:1 configuration:3 series:2 contains:1 hereafter:1 existing:2 current:1 discretization:12 dx:1 additive:1 partition:4 enables:2 plot:1 progressively:2 update:1 cult:1 isotropic:1 oneto:1 provides:1 boosting:14 lo... |
4,979 | 5,507 | Bayesian Nonlinear Support Vector Machines and
Discriminative Factor Modeling
Ricardo Henao, Xin Yuan and Lawrence Carin
Department of Electrical and Computer Engineering
Duke University, Durham, NC 27708
{r.henao,xin.yuan,lcarin}@duke.edu
Abstract
A new Bayesian formulation is developed for nonlinear support vector ... | 5507 |@word version:4 proportion:1 loading:2 logit:3 seek:1 tried:1 simplifying:1 covariance:12 moment:1 configuration:1 liu:2 score:7 document:1 ours:1 outperforms:2 existing:1 current:1 elliptical:4 surprising:1 written:3 readily:1 fn:27 subsequent:1 shape:1 drop:1 update:4 n0:1 v:8 prohibitive:2 metabolism:1 filtere... |
4,980 | 5,508 | Optimizing F-Measures by Cost-Sensitive Classification
Shameem A. Puthiya Parambath, Nicolas Usunier, Yves Grandvalet
Universit?e de Technologie de Compi`egne ? CNRS, Heudiasyc UMR 7253
Compi`egne, France
{sputhiya,nusunier,grandval}@utc.fr
Abstract
We present a theoretical analysis of F -measures for binary, multicl... | 5508 |@word version:4 seems:1 nd:2 open:2 closure:2 hsieh:1 solid:1 harder:1 liblinear:2 reduction:7 liu:1 series:2 score:24 selecting:1 tuned:2 outperforms:1 existing:1 horvitz:1 discretization:2 mari:1 yet:2 written:5 must:2 numerical:1 kdd:1 enables:1 designed:2 v:3 half:1 intelligence:2 egne:2 lr:12 characterizatio... |
4,981 | 5,509 | Analysis of Learning from
Positive and Unlabeled Data
Marthinus C. du Plessis
The University of Tokyo
Tokyo, 113-0033, Japan
christo@ms.k.u-tokyo.ac.jp
Gang Niu
Baidu Inc.
Beijing, 100085, China
niugang@baidu.com
Masashi Sugiyama
The University of Tokyo
Tokyo, 113-0033, Japan
sugi@k.u-tokyo.ac.jp
Abstract
Learning a... | 5509 |@word version:2 proportion:2 duda:1 nd:1 twelfth:1 necessity:1 substitution:1 selecting:2 com:1 comparing:1 assigning:2 written:1 must:1 john:2 numerical:1 wx:1 v:12 discrimination:1 half:1 selected:3 intelligence:2 inspection:1 direct:1 baidu:2 incorrect:1 marthinus:1 indeed:1 expected:5 p1:6 ming:1 becomes:3 pr... |
4,982 | 551 | VISIT: A Neural Model of Covert Visual
Attention
Subutai AhmadSiemens Research and Development,
ZFE ST SN6, Otto-Hahn Ring 6,
8000 Munich 83, Germany.
ahmad~bsUD4Gztivax.siemens.eom
Abstract
Visual attention is the ability to dynamically restrict processing to a subset
of the visual field. Researchers have long argue... | 551 |@word briefly:1 lobe:1 abou:1 pick:1 attended:2 mention:1 tr:1 carry:1 extrastriate:1 configuration:1 contains:2 exclusively:1 current:6 anne:1 activation:4 yet:1 assigning:1 conjunctive:1 must:1 readily:1 chicago:1 shape:1 motor:1 remove:1 update:1 implying:1 alone:1 leaf:1 guess:1 rch:1 provides:3 location:18 dr... |
4,983 | 5,510 | Feature Cross-Substitution in Adversarial
Classification
Bo Li and Yevgeniy Vorobeychik
Electrical Engineering and Computer Science
Vanderbilt University
{bo.li.2,yevgeniy.vorobeychik}@vanderbilt.edu
Abstract
The success of machine learning, particularly in supervised settings, has led to
numerous attempts to apply it... | 5510 |@word kong:1 repository:2 middle:2 stronger:1 open:4 linearized:3 accounting:2 pavel:1 natsoulis:1 dramatic:2 thereby:2 reduction:17 initial:1 substitution:19 contains:2 score:3 efficacy:1 liu:1 daniel:1 lichman:1 bc:4 document:1 outperforms:3 horvitz:1 surprising:1 si:2 import:1 must:2 john:1 realistic:1 partiti... |
4,984 | 5,511 | Large-Margin Convex Polytope Machine
Alex Kantchelian Michael Carl Tschantz Ling Huang?
Peter L. Bartlett Anthony D. Joseph J. D. Tygar
UC Berkeley ? {akant|mct|bartlett|adj|tygar}@cs.berkeley.edu
?
Datavisor ? ling.huang@datavisor.com
Abstract
We present the Convex Polytope Machine (CPM), a novel non-linear learning... | 5511 |@word achievable:1 norm:1 nd:1 decomposition:1 wiggling:2 pick:1 sgd:10 solid:2 configuration:3 contains:1 score:1 disparity:2 united:1 tuned:1 outperforms:1 existing:1 current:3 com:1 adj:1 comparing:1 savage:1 beygelzimer:2 assigning:3 must:1 john:2 realistic:1 additive:1 wx:3 kdd:2 enables:1 drop:1 treating:1 ... |
4,985 | 5,512 | A Boosting Framework on Grounds of Online
Learning
Tofigh Naghibi, Beat Pfister
Computer Engineering and Networks Laboratory
ETH Zurich, Switzerland
naghibi@tik.ee.ethz.ch, pfister@tik.ee.ethz.ch
Abstract
By exploiting the duality between boosting and online learning, we present a
boosting framework which proves to b... | 5512 |@word version:16 briefly:2 norm:17 seems:1 gradual:1 linearized:1 mention:1 versatile:1 hunting:1 minmax:2 ftrl:1 bradley:1 nt:1 surprising:1 additive:1 enables:1 update:22 greedy:1 warmuth:1 accordingly:1 vanishing:1 chiang:1 draft:1 boosting:76 successive:1 unbounded:1 dn:1 constructed:1 direct:2 anyboost:1 pro... |
4,986 | 5,513 | Multi-Resolution Cascades for Multiclass Object
Detection
Nuno Vasconcelos
Statistical Visual Computing Laboratory
University of California, San Diego
nuno@ucsd.edu
Mohammad Saberian
Yahoo! Labs
saberian@yahoo-inc.com
Abstract
An algorithm for learning fast multiclass object detection cascades is introduced.
It produ... | 5513 |@word illustrating:1 version:3 middle:1 eliminating:2 gradual:2 reduction:1 initial:1 contains:2 score:1 bootstrapped:1 current:2 com:1 comparing:1 assigning:4 must:9 written:1 visible:1 partition:2 enables:1 designed:1 update:3 v:4 alone:1 discrimination:1 selected:1 guess:1 device:1 greedy:1 intelligence:4 core... |
4,987 | 5,514 | Multi-Class Deep Boosting
Vitaly Kuznetsov
Courant Institute
251 Mercer Street
New York, NY 10012
Mehryar Mohri
Courant Institute & Google Research
251 Mercer Street
New York, NY 10012
Umar Syed
Google Research
76 Ninth Avenue
New York, NY 10011
vitaly@cims.nyu.edu
mohri@cims.nyu.edu
usyed@google.com
Abstract
We... | 5514 |@word mild:1 version:4 briefly:1 norm:4 suitably:1 seek:1 crucially:1 bn:2 score:2 selecting:2 past:2 existing:1 outperforms:3 current:1 com:1 luo:3 yet:1 written:3 numerical:1 additive:5 v:1 greedy:3 selected:5 devising:1 leaf:8 warmuth:5 record:1 provides:2 boosting:28 iterates:1 node:2 simpler:1 zhang:2 along:... |
4,988 | 5,515 | Robust Logistic Regression and Classification
Huan Xu
ME Department
National University of Singapore
mpexuh@nus.edu.sg
Jiashi Feng
EECS Department & ICSI
UC Berkeley
jshfeng@berkeley.edu
Shuicheng Yan
ECE Department
National University of Singapore
eleyans@nus.edu.sg
Shie Mannor
EE Department
Technion
shie@ee.techn... | 5515 |@word norm:1 pieter:1 shuicheng:1 simulation:4 covariance:1 solid:1 boundedness:1 mpexuh:1 series:1 score:1 chervonenkis:1 outperforms:1 comparing:1 nt:2 yet:1 readily:1 additive:1 remove:3 plot:2 half:1 intelligence:1 cook:2 guess:1 warmuth:1 ith:1 lr:62 manfred:1 provides:2 mannor:3 contribute:1 unbounded:2 alo... |
4,989 | 5,516 | Spectral Methods for Indian Buffet Process Inference
Hsiao-Yu Fish Tung
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
Alexander J. Smola
Machine Learning Department
Carnegie Mellon University and Google
Pittsburgh, PA 15213
Abstract
The Indian Buffet Process is a versatile statistical t... | 5516 |@word version:1 polynomial:5 suitably:1 tensorial:4 d2:1 decomposition:7 covariance:3 pick:1 versatile:1 carry:1 reduction:2 initial:1 liu:1 contains:3 moment:25 zij:5 nonexistent:1 daniel:2 offering:1 existing:1 recovered:1 comparing:2 ka:2 surprising:1 si:3 reminiscent:1 additive:5 realistic:1 j1:2 pertinent:1 ... |
4,990 | 5,517 | Spectral Methods for Supervised Topic Models
Yining Wang?
Jun Zhu?
Machine Learning Department, Carnegie Mellon University, yiningwa@cs.cmu.edu
?
Dept. of Comp. Sci. & Tech.; Tsinghua National TNList Lab; State Key Lab of Intell. Tech. & Sys.,
Tsinghua University, dcszj@mail.tsinghua.edu.cn
?
Abstract
Supervised topi... | 5517 |@word mild:1 trial:1 version:2 polynomial:1 norm:8 laurence:1 km:2 simulation:1 decomposition:24 thereby:1 tnlist:1 mcauley:1 reduction:1 moment:22 liu:1 contains:5 score:6 selecting:1 document:39 outperforms:1 existing:1 recovered:4 written:2 parsing:1 j1:2 treating:1 designed:1 update:6 alone:1 generative:1 pro... |
4,991 | 5,518 | Spectral Learning of Mixture of Hidden Markov
Models
[
?
Y. Cem Subakan
, Johannes Traa] , Paris Smaragdis[,],\
Department of Computer Science, University of Illinois at Urbana-Champaign
]
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
\
Adobe Systems, Inc.
{subakan2, traa... | 5518 |@word trial:2 repository:2 middle:1 unaltered:1 polynomial:2 proportion:1 seems:1 km:1 covariance:2 decomposition:5 pick:1 moment:11 initial:4 series:2 lichman:1 daniel:1 o2:3 existing:2 recovered:2 ka:5 john:1 enables:1 plot:1 v:3 stationary:5 prohibitive:1 discovering:1 item:1 isotropic:1 yuting:1 zhang:1 along... |
4,992 | 5,519 | Multi-Scale Spectral Decomposition of Massive
Graphs
Si Si?
Department of Computer Science
University of Texas at Austin
ssi@cs.utexas.edu
Donghyuk Shin?
Department of Computer Science
University of Texas at Austin
dshin@cs.utexas.edu
Inderjit S. Dhillon
Department of Computer Science
University of Texas at Austin
in... | 5519 |@word kulis:1 version:6 manageable:1 polynomial:1 termination:8 ajj:1 propagate:1 decomposition:19 q1:1 pick:1 recursively:2 initial:3 liu:1 eigensolvers:5 contains:2 interestingly:1 outperforms:5 existing:3 current:1 ka:4 si:3 concatenate:1 partition:9 informative:1 drop:1 v:2 leaf:1 item:5 propack:8 core:14 mat... |
4,993 | 5,520 | Spectral Clustering of Graphs with the Bethe Hessian
Alaa Saade
Laboratoire de Physique Statistique, CNRS UMR 8550
?
Ecole
Normale Superieure, 24 Rue Lhomond Paris 75005
Florent Krzakala?
Sorbonne Universit?es, UPMC Univ Paris 06
Laboratoire de Physique Statistique, CNRS UMR 8550
?
Ecole
Normale Superieure, 24 Rue Lho... | 5520 |@word deformed:3 determinant:1 version:1 middle:3 polynomial:1 norm:3 proportion:1 disk:1 open:4 carry:1 kappen:1 anthropological:1 moment:2 reduction:1 initial:1 ecole:2 denoting:1 interestingly:2 neeman:2 outperforms:3 paramagnetic:6 com:1 must:1 numerical:4 informative:10 gv:2 guess:1 vanishing:1 detecting:3 p... |
4,994 | 5,521 | Permutation Diffusion Maps (PDM) with Application
to the Image Association Problem in Computer Vision
Deepti Pachauri? , Risi Kondor? , Gautam Sargur? , Vikas Singh??
?
Dept. of Computer Sciences, University of Wisconsin?Madison
?
Dept. of Biostatistics & Medical Informatics, University of Wisconsin?Madison
?
Dept. of... | 5521 |@word briefly:1 kondor:3 middle:1 norm:2 seems:2 nonsensical:1 seitz:2 seek:1 decomposition:1 q1:4 tr:1 harder:1 carry:1 initial:3 contains:1 fragment:2 selecting:1 efficacy:1 score:2 series:1 kahl:1 existing:4 must:5 written:3 chicago:1 numerical:1 informative:2 timestamps:2 realistic:1 partition:1 shape:2 visib... |
4,995 | 5,522 | Low-Rank Time-Frequency Synthesis
Matthieu Kowalski?
Laboratoire des Signaux et Syst`emes
(CNRS, Sup?elec & Universit?e Paris-Sud)
Gif-sur-Yvette, France
kowalski@lss.supelec.fr
C?edric F?evotte
Laboratoire Lagrange
(CNRS, OCA & Universit?e de Nice)
Nice, France
cfevotte@unice.fr
Abstract
Many single-channel signal ... | 5522 |@word middle:2 version:1 inversion:3 norm:1 open:2 cml:2 decomposition:7 inpainting:1 mysore:1 accommodate:1 edric:1 series:1 denoting:5 mmse:5 imaginary:1 current:3 activation:1 yet:2 written:3 must:1 fn:5 underly:1 additive:2 designed:1 treating:1 update:3 generative:7 fewer:1 selected:1 short:6 completeness:2 ... |
4,996 | 5,523 | A State-Space Model for Decoding Auditory
Attentional Modulation from MEG in a
Competing-Speaker Environment
Sahar Akram1,2 , Jonathan Z. Simon1,2,3 , Shihab Shamma1,2 , and Behtash Babadi1,2
1
Department of Electrical and Computer Engineering,
2
Institute for Systems Research, 3 Department of Biology
University of Ma... | 5523 |@word trial:32 middle:1 version:1 inversion:1 approved:1 nd:1 c0:5 logit:3 m100:2 hu:1 simulation:6 seek:2 decomposition:2 accounting:1 irb:1 attended:7 pick:1 reduction:2 initial:1 series:4 hereafter:1 denoting:4 suppressing:1 subjective:2 existing:1 outperforms:1 current:3 realistic:1 informative:1 shape:1 plot... |
4,997 | 5,524 | Efficient Structured Matrix Rank Minimization
Adams Wei Yu? , Wanli Ma? , Yaoliang Yu? , Jaime G. Carbonell? , Suvrit Sra?
School of Computer Science, Carnegie Mellon University?
Max Planck Institute for Intelligent Systems?
{weiyu, mawanli, yaoliang, jgc}@cs.cmu.edu, suvrit@tuebingen.mpg.de
Abstract
We study the prob... | 5524 |@word erate:1 briefly:1 achievable:1 norm:16 scalably:1 linearized:2 covariance:4 decomposition:1 inpainting:1 carry:1 initial:2 liu:4 efficacy:1 outperforms:1 existing:3 ka:6 current:1 recovered:5 comparing:1 mari:1 must:1 written:1 readily:1 numerical:1 partition:1 padmm:7 enables:1 cheap:1 remove:1 drop:2 plot... |
4,998 | 5,525 | Ef?cient Minimax Signal Detection on Graphs
Jing Qian
Division of Systems Engineering
Boston University
Brookline, MA 02446
jingq@bu.edu
Venkatesh Saligrama
Department of Electrical and Computer Engineering
Boston University
Boston, MA 02215
srv@bu.edu
Abstract
Several problems such as network intrusion, community d... | 5525 |@word version:2 suitably:1 heuristically:1 p0:8 invoking:1 arti:3 eld:1 tr:1 tabulate:1 denoting:2 outperforms:1 existing:5 nally:1 current:1 virus:1 incidence:2 yet:1 written:2 must:1 realize:1 partition:1 shape:61 remove:1 plot:1 v:1 intelligence:4 fewer:3 cult:1 huo:1 characterization:3 provides:4 node:35 math... |
4,999 | 5,526 | Signal Aggregate Constraints in Additive Factorial
HMMs, with Application to Energy Disaggregation
Mingjun Zhong, Nigel Goddard, Charles Sutton
School of Informatics
University of Edinburgh
United Kingdom
{mzhong,nigel.goddard,csutton}@inf.ed.ac.uk
Abstract
Blind source separation problems are difficult because they ... | 5526 |@word version:2 polynomial:1 open:1 iki:3 outlook:1 electronics:1 configuration:1 series:5 ntc:10 united:1 initial:2 tuned:1 disaggregation:31 comparing:1 com:1 si:6 yet:1 must:1 evans:2 realistic:2 additive:8 hoping:1 plot:1 unidentifiability:1 intelligence:2 selected:2 mccallum:1 affair:1 appliance:29 tahoe:1 s... |
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