Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
4,300 | 4,892 | Lasso Screening Rules via Dual Polytope Projection
Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye
Computer Science and Engineering
Arizona State University, Tempe, AZ 85287
{jie.wang.ustc, jiayu.zhou, peter.wonka, jieping.ye}@asu.edu
Abstract
Lasso is a widely used regression technique to find sparse representations. ... | 4892 |@word mild:1 version:5 mri:1 stronger:1 norm:1 turlach:1 nd:1 open:1 grey:2 decomposition:1 pg:2 pick:2 mention:2 liu:2 contains:1 score:1 series:5 document:1 mmse:1 outperforms:2 existing:3 must:1 readily:1 numerical:1 happen:1 kpf:1 kdd:1 remove:3 kv1:1 interpretable:1 half:1 asu:1 selected:1 kyk:2 beginning:1 ... |
4,301 | 4,893 | A Kernel Test for Three-Variable Interactions
Dino Sejdinovic, Arthur Gretton
Gatsby Unit, CSML, UCL, UK
{dino.sejdinovic, arthur.gretton}@gmail.com
Wicher Bergsma
Department of Statistics, LSE, UK
w.p.bergsma@lse.ac.uk
Abstract
We introduce kernel nonparametric tests for Lancaster three-variable interaction
and for t... | 4893 |@word determinant:1 inversion:1 polynomial:1 norm:9 stronger:1 nd:3 tedious:1 km:2 calculus:1 covariance:4 thereby:1 tr:2 moment:8 yxx:1 rkhs:19 jyv:2 outperforms:2 com:1 z2:2 exy:2 gmail:1 universality:1 readily:2 tot:11 additive:3 partition:8 plot:4 v:2 prohibitive:1 tillman:1 short:1 detecting:4 provides:1 cha... |
4,302 | 4,894 | More Effective Distributed ML via a Stale
Synchronous Parallel Parameter Server
?Qirong Ho, ?James Cipar, ?Henggang Cui, ?Jin Kyu Kim, ?Seunghak Lee,
?Phillip B. Gibbons, ?Garth A. Gibson, ?Gregory R. Ganger, ?Eric P. Xing
?School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
?Electrical and Comp... | 4894 |@word msr:1 version:5 proportion:1 nd:2 c0:6 strong:1 cipar:3 vldb:3 overwritten:1 queensland:1 decomposition:4 p0:2 sgd:6 stateless:1 asks:1 tr:1 blade:1 initial:1 cyclic:4 configuration:3 document:2 franklin:1 outperforms:1 existing:2 bradley:1 current:2 com:3 comparing:1 parameter1:1 soules:1 yet:2 must:5 conf... |
4,303 | 4,895 | Learning with Invariance via Linear Functionals on
Reproducing Kernel Hilbert Space
Yee Whye Teh
Wee Sun Lee
Xinhua Zhang
Machine Learning Research Group Department of Computer Science Department of Statistics
National ICT Australia and ANU National University of Singapore University of Oxford
y.w.teh@stats.ox.ac.uk
l... | 4895 |@word repository:2 inversion:1 polynomial:6 norm:10 seems:1 open:1 r:2 tried:1 covariance:1 pick:1 tr:1 klk:1 boundedness:3 lichman:1 rkhs:26 interestingly:1 bhattacharyya:1 existing:2 current:1 com:1 discretization:1 nt:1 dx:2 must:7 written:1 kdd:1 cheap:1 analytic:1 update:1 discrimination:1 v:8 instantiate:1 ... |
4,304 | 4,896 | Learning Kernels Using
Local Rademacher Complexity
Corinna Cortes
Google Research
76 Ninth Avenue
New York, NY 10011
corinna@google.com
Marius Kloft?
Courant Institute &
Sloan-Kettering Institute
251 Mercer Street
New York, NY 10012
mkloft@cims.nyu.edu
Mehryar Mohri
Courant Institute &
Google Research
251 Mercer Stre... | 4896 |@word version:2 stronger:2 norm:10 unif:5 km:23 seek:1 decomposition:2 thereby:1 tr:4 profit:1 series:2 selecting:2 denoting:1 existing:6 com:1 update:1 alone:1 half:1 selected:2 core:2 short:1 detecting:1 org:3 simpler:1 yuan:1 consists:1 introduce:1 indeed:1 expected:1 p1:1 frequently:1 multi:11 considering:1 s... |
4,305 | 4,897 | Inverse Density as an Inverse Problem:
the Fredholm Equation Approach
Qichao Que, Mikhail Belkin
Department of Computer Science and Engineering
The Ohio State University
{que,mbelkin}@cse.ohio-state.edu
Abstract
We address the problem of estimating the ratio pq where p is a density function
and q is another density, ... | 4897 |@word h:13 trial:1 version:7 inversion:1 norm:26 harder:1 contains:1 selecting:1 denoting:1 rkhs:16 ours:1 err:2 comparing:1 dx:5 written:1 must:1 malized:1 resampling:9 half:7 provides:1 cse:1 five:1 along:1 c2:8 direct:3 become:1 prove:1 introduce:1 theoretically:1 planning:1 multi:1 brain:1 kpp:1 td:3 encourag... |
4,306 | 4,898 | Regression-tree Tuning in a Streaming Setting
Samory Kpotufe?
Toyota Technological Institute at Chicago?
firstname@ttic.edu
Francesco Orabona?
Toyota Technological Institute at Chicago
francesco@orabona.com
Abstract
We consider the problem of maintaining the data-structures of a partition-based
regression procedure ... | 4898 |@word version:3 polynomial:1 interleave:1 stronger:1 open:1 simulation:2 decomposition:1 pick:4 reduction:1 initial:2 contains:1 interestingly:1 existing:1 current:1 com:1 nt:7 discretization:1 beygelzimer:1 fn:34 chicago:2 partition:15 subsequent:1 update:8 leaf:1 guess:8 c2:2 symposium:1 prove:5 consists:3 comb... |
4,307 | 4,899 | Graphical Models for Inference with Missing Data
Karthika Mohan
Judea Pearl
Dept. of Computer Science
Dept. of Computer Science
Univ. of California, Los Angeles Univ. of California, Los Angeles
Los Angeles, CA 90095
Los Angeles, CA 90095
karthika@cs.ucla.edu
judea@cs.ucla.edu
Jin Tian
Dept. of Computer Science
Iowa S... | 4899 |@word briefly:1 eliminating:1 proportion:1 stronger:1 indiscriminate:1 simulation:1 covariance:1 decomposition:7 q1:1 thereby:2 solid:1 accommodate:1 necessity:1 substitution:1 contains:4 exclusively:1 series:1 pub:2 daniel:1 mcar:15 longitudinal:2 existing:1 recovered:2 com:1 must:1 written:1 duffield:1 dechter:... |
4,308 | 49 | 505
CONNECTING TO THE PAST
Bruce A. MacDonald, Assistant Professor
Knowledge Sciences Laboratory, Computer Science Department
The University of Calgary, 2500 University Drive NW
Calgary, Alberta T2N IN4
ABSTRACT
Recently there has been renewed interest in neural-like processing systems, evidenced for example in the tw... | 49 |@word version:3 briefly:5 seems:2 nd:1 seek:1 sensed:1 simulation:5 simplifying:1 pressure:1 thereby:1 cleary:1 t2n:2 andreae:15 renewed:1 past:3 reaction:1 current:4 blank:1 activation:11 must:1 john:1 predetermined:4 enables:2 motor:2 intelligence:3 selected:2 item:1 leamed:1 indefinitely:2 provides:1 math:1 inpu... |
4,309 | 490 | Networks for the Separation of Sources
that are Superimposed and Delayed
John C. Platt
Federico Faggin
Synaptics, Inc.
2860 Zanker Road, Suite 206
San Jose, CA 95134
ABSTRACT
We have created new networks to unmix signals which have been
mixed either with time delays or via filtering. We first show that
a subset of th... | 490 |@word version:1 seems:2 tried:1 solid:1 interestingly:1 hearn:1 must:1 john:1 additive:1 realistic:1 update:2 ith:1 short:1 filtered:3 mathematical:1 glover:1 incorrect:1 introduce:1 roughly:2 nor:1 linearity:1 lowest:1 minimizes:3 developed:1 suite:1 ti:2 platt:4 understood:1 path:7 approximately:1 plus:2 mateo:1... |
4,310 | 4,900 | Non-strongly-convex smooth stochastic
approximation with convergence rate O(1/n)
Eric Moulines
LTCI
Telecom ParisTech, Paris, France
eric.moulines@enst.fr
Francis Bach
INRIA - Sierra Project-team
Ecole Normale Sup?erieure, Paris, France
francis.bach@ens.fr
Abstract
We consider the stochastic approximation problem wh... | 4900 |@word worsens:1 middle:3 proportion:1 stronger:2 norm:2 hyv:1 nemirovsky:1 covariance:5 sgd:18 tr:1 harder:3 moment:3 initial:3 ecole:1 ours:1 interestingly:1 existing:2 current:2 comparing:1 must:1 fn:7 remove:1 plot:12 update:1 juditsky:2 stationary:5 iterates:4 provides:2 successive:2 mathematical:2 along:1 re... |
4,311 | 4,901 | Message Passing Inference with Chemical Reaction
Networks
Nils Napp
Ryan Prescott Adams
Wyss Institute for Biologically Inspired Engineering
Harvard University
Cambridge, MA 02138
School of Engineering and Applied Sciences
Harvard University
Cambridge, MA 02138
nnapp@wyss.harvard.edu
rpa@seas.harvard.edu
Abstract... | 4901 |@word version:3 simulation:12 p0:6 initial:1 cyclic:2 series:1 tuned:1 genetic:1 reaction:90 z2:5 tackling:1 written:1 parsing:1 nanoscale:2 john:2 numerical:1 subsequent:1 enables:1 designed:2 plot:2 update:2 v:1 half:2 leaf:4 device:4 fewer:1 cook:1 jongmin:1 tertiary:1 colored:1 provides:2 node:21 ron:1 simple... |
4,312 | 4,902 | Information-theoretic lower bounds for distributed
statistical estimation with communication constraints
Yuchen Zhang1
John C. Duchi1
Michael I. Jordan1,2
Martin J. Wainwright1,2
1
Department of Electrical Engineering and Computer Science and 2 Department of Statistics
University of California, Berkeley
Berkeley, CA 94... | 4902 |@word version:1 compression:1 achievable:5 logmm:1 nd:3 dekel:1 reduction:6 initial:1 zij:1 past:3 wainwrig:1 current:1 comparing:1 nt:5 luo:3 chu:1 must:7 john:1 numerical:4 inspection:1 minskii:1 characterization:2 quantized:3 node:1 location:5 provides:2 completeness:1 org:2 math:1 zhang:3 along:1 constructed:... |
4,313 | 4,903 | PAC-Bayes-Empirical-Bernstein Inequality
Yevgeny Seldin
Queensland University of Technology
UC Berkeley
yevgeny.seldin@gmail.com
Ilya Tolstikhin
Computing Centre
Russian Academy of Sciences
iliya.tolstikhin@gmail.com
Abstract
We present a PAC-Bayes-Empirical-Bernstein inequality. The inequality is based
on a combina... | 4903 |@word repository:2 version:2 open:1 queensland:1 pick:2 moment:2 necessity:1 substitution:2 outperforms:1 existing:1 com:2 comparing:1 contextual:1 gmail:2 john:7 ronald:1 numerical:2 fn:5 subsequent:1 joy:1 plane:1 provides:4 org:1 dn:2 c2:8 shorthand:2 combine:2 fitting:1 expected:21 roughly:2 behavior:3 examin... |
4,314 | 4,904 | Regularized M -estimators with nonconvexity:
Statistical and algorithmic theory for local optima
Martin J. Wainwright
Departments of Statistics and EECS
University of California, Berkeley
Berkeley, CA 94720
wainwrig@stat.berkeley.edu
Po-Ling Loh
Department of Statistics
University of California, Berkeley
Berkeley, CA... | 4904 |@word trial:1 illustrating:1 version:6 polynomial:1 stronger:2 norm:3 c0:7 simulation:6 solid:1 boundedness:1 zij:2 past:1 wainwrig:1 existing:1 err:12 comparing:2 subsequent:1 additive:4 numerical:1 plot:12 update:3 depict:5 implying:1 core:2 weierstrass:1 provides:4 iterates:2 clarified:1 successive:1 org:1 zha... |
4,315 | 4,905 | More data speeds up training time in learning
halfspaces over sparse vectors
Amit Daniely
Department of Mathematics
The Hebrew University
Jerusalem, Israel
Nati Linial
School of CS and Eng.
The Hebrew University
Jerusalem, Israel
Shai Shalev-Shwartz
School of CS and Eng.
The Hebrew University
Jerusalem, Israel
Abst... | 4905 |@word polynomial:3 stronger:3 open:2 crucially:1 eng:2 harder:1 reduction:2 contains:1 err:1 yet:1 intriguing:1 must:2 realistic:1 j1:5 show1:1 half:2 item:6 xk:8 core:1 provides:1 completeness:1 ron:1 preference:4 constructed:2 symposium:1 focs:1 prove:5 consists:1 indeed:2 hardness:13 rapid:1 themselves:1 frequ... |
4,316 | 4,906 | Convex Calibrated Surrogates for Low-Rank Loss
Matrices with Applications to Subset Ranking Losses
Harish G. Ramaswamy
Shivani Agarwal
Computer Science & Automation Computer Science & Automation
Indian Institute of Science
Indian Institute of Science
harish gurup@csa.iisc.ernet.in
shivani@csa.iisc.ernet.in
Ambuj Tewar... | 4906 |@word wenxin:1 judgement:3 nd:1 arti:1 liu:2 score:21 denoting:1 document:20 nt:1 si:1 written:3 must:1 john:1 cant:1 designed:3 mackey:1 half:1 intelligence:1 yr:1 item:1 cult:3 reciprocal:1 short:1 boosting:1 node:2 preference:4 zhang:4 mathematical:1 direct:1 consists:4 prove:1 combine:1 manner:1 pairwise:5 in... |
4,317 | 4,907 | On the Relationship Between Binary Classification,
Bipartite Ranking, and Binary Class Probability
Estimation
Harikrishna Narasimhan Shivani Agarwal
Department of Computer Science and Automation
Indian Institute of Science, Bangalore 560012, India
{harikrishna,shivani}@csa.iisc.ernet.in
Abstract
We investigate the rel... | 4907 |@word cpe:88 mild:1 repository:2 version:2 flach:1 covariance:2 thres:3 minus:1 reduction:1 contains:1 score:11 selecting:3 document:2 spambase:3 existing:3 current:1 must:3 john:1 kdd:1 plot:2 v:1 fewer:1 rudin:2 provides:1 boosting:2 preference:1 herbrich:1 zhang:1 mathematical:2 dn:4 er0:11 constructed:7 incor... |
4,318 | 4,908 | From Bandits to Experts:
A Tale of Domination and Independence
Noga Alon
Tel-Aviv University, Israel
nogaa@tau.ac.il
Nicol`o Cesa-Bianchi
Universit`a degli Studi di Milano, Italy
nicolo.cesa?bianchi@unimi.it
Claudio Gentile
University of Insubria, Italy
claudio.gentile@uninsubria.it
Yishay Mansour
Tel-Aviv Universi... | 4908 |@word kong:1 nd:1 open:1 pick:2 incurs:2 tr:1 harder:2 uncovered:1 selecting:2 united:1 tuned:1 ours:1 past:4 current:4 comparing:1 si:27 yet:2 follower:4 must:2 john:1 subsequent:1 partition:1 shape:1 greedy:5 selected:1 warmuth:2 beginning:2 core:2 manfred:1 characterization:4 mannor:3 node:5 boosting:1 prefere... |
4,319 | 4,909 | Eluder Dimension and the Sample Complexity of
Optimistic Exploration
Benjamin Van Roy
Stanford University
Stanford, CA 94305
bvr@stanford.edu
Daniel Russo
Stanford University
Stanford, CA 94305
djrusso@stanford.edu
Abstract
This paper considers the sample complexity of the multi-armed bandit with dependencies among ... | 4909 |@word exploitation:2 norm:2 stronger:4 advantageous:1 simplifying:1 decomposition:4 boundedness:1 contains:1 selecting:1 chervonenkis:2 daniel:1 ours:1 past:1 contextual:7 discretization:1 beygelzimer:1 must:2 informative:1 enables:1 designed:1 update:2 intelligence:3 selected:3 provides:5 constructed:2 predecess... |
4,320 | 491 | SINGLE NEURON MODEL: RESPONSE TO WEAK
MODULATION IN THE PRESENCE OF NOISE
A. R. Bu/,ara and E. W. Jaco6,
Naval Ocean Syat.em.a Cenw, Materials Reaean:h Branch, San Diego, CA 92129
F.Mou
Physics Dept.., Univ. of Missouri, St. Louis, MO 63121
ABSTRACT
We consider a noisy bist.able single neuron model driven by a period... | 491 |@word middle:2 sharpens:1 suitably:1 d2:4 simulation:10 jacob:12 lowfrequency:1 solid:1 reduction:1 series:3 correspondin:1 cort:1 reaction:1 current:1 paramagnetic:2 bta:1 activation:1 dx:3 must:1 readily:4 transcendental:1 additive:9 periodically:2 numerical:4 realistic:1 heir:1 mandell:2 rinzel:2 plot:3 v:1 sta... |
4,321 | 4,910 | Adaptive Market Making via Online Learning
Jacob Abernethy?
Computer Science and Engineering
University of Michigan
jabernet@umich.edu
Satyen Kale
IBM T. J. Watson Research Center
sckale@us.ibm.com
Abstract
We consider the design of strategies for market making in an exchange. A market
maker generally seeks to profit... | 4910 |@word version:2 middle:1 achievable:1 chakraborty:2 heterogeneously:1 willing:2 seek:1 simulation:1 jacob:1 simplifying:1 accounting:1 profit:14 mention:1 initial:2 offload:1 series:1 liquid:2 pt0:1 offering:1 past:2 outperforms:1 current:7 com:1 discretization:1 yet:1 must:1 additive:1 hoping:1 drop:1 update:7 a... |
4,322 | 4,911 | Submodular Optimization with Submodular Cover
and Submodular Knapsack Constraints
Jeff Bilmes
Department of Electrical Engineering
University of Washington
bilmes@u.washington.edu
Rishabh Iyer
Department of Electrical Engineering
University of Washington
rkiyer@u.washington.edu
Abstract
We investigate two new optimi... | 4911 |@word private:1 version:9 polynomial:3 c0:1 semidifferential:1 bicriteria:1 selecting:1 document:2 current:1 surprising:2 si:13 additive:1 designed:1 greedy:23 short:1 filtered:1 draft:1 provides:4 location:4 simpler:6 constructed:1 become:3 introduce:2 privacy:2 theoretically:1 lov:1 indeed:1 expected:1 hardness... |
4,323 | 4,912 | How to Hedge an Option Against an Adversary:
Black-Scholes Pricing is Minimax Optimal
Jacob Abernethy
University of Michigan
jabernet@umich.edu
Peter L. Bartlett
University of California at Berkeley
and Queensland University of Technology
bartlett@cs.berkeley.edu
Rafael M. Frongillo
Microsoft Research
raf@cs.berkeley... | 4912 |@word briefly:1 version:1 stronger:1 replicate:1 open:1 calculus:4 queensland:1 jacob:1 invoking:1 profit:2 recursively:1 moment:1 initial:2 current:8 surprising:1 additive:1 informative:1 predetermined:2 update:1 guess:2 warmuth:1 cursory:1 boosting:1 earnings:1 gec:4 differential:2 symposium:1 prove:6 intricate... |
4,324 | 4,913 | Small-Variance Asymptotics for Hidden Markov
Models
Anirban Roychowdhury, Ke Jiang, Brian Kulis
Department of Computer Science and Engineering
The Ohio State University
roychowdhury.7@osu.edu, {jiangk,kulis}@cse.ohio-state.edu
Abstract
Small-variance asymptotics provide an emerging technique for obtaining scalable
com... | 4913 |@word kulis:5 version:1 briefly:1 confirms:1 seek:2 covariance:1 initial:2 contains:1 score:3 series:3 existing:8 comparing:1 si:1 written:3 must:3 additive:1 designed:1 update:5 v:1 stationary:1 generative:4 selected:4 beginning:1 blei:1 provides:1 cse:1 bijection:2 org:2 simpler:1 along:6 beta:1 consists:1 comb... |
4,325 | 4,914 | The Total Variation on Hypergraphs - Learning on
Hypergraphs Revisited
Matthias Hein, Simon Setzer, Leonardo Jost and Syama Sundar Rangapuram
Department of Computer Science
Saarland University
Abstract
Hypergraphs allow one to encode higher-order relationships in data and are thus a
very flexible modeling tool. Curre... | 4914 |@word trial:1 version:1 norm:1 seems:1 vldb:1 zelnik:1 recursively:1 carry:3 liu:1 contains:4 ours:3 outperforms:1 existing:2 current:2 incidence:1 mushroom:4 written:4 fn:1 numerical:1 partition:8 v:1 prohibitive:1 math:1 revisited:1 node:1 c6:4 zhang:2 lce:5 saarland:1 c2:1 paragraph:1 kiwiel:1 introduce:3 pair... |
4,326 | 4,915 | Using multiple samples to learn mixture
models
Jason Lee?
Stanford University
Stanford, USA
jdl17@stanford.edu
Ran Gilad-Bachrach
Microsoft Research
Redmond, USA
rang@microsoft.com
Rich Caruana
Microsoft Research
Redmond, USA
rcaruana@microsoft.com
Abstract
In the mixture models problem it is assumed that there are ... | 4915 |@word mild:1 trial:2 polynomial:4 nd:1 d2:13 sepa:1 recursively:2 moment:4 initial:1 series:1 contains:1 selecting:3 genetic:1 document:4 outperforms:2 err:1 current:1 com:2 comparing:1 john:1 realistic:1 j1:1 dive:1 designed:2 grass:1 generative:2 leaf:7 greedy:1 selected:1 record:2 blei:1 node:3 location:2 cons... |
4,327 | 4,916 | Approximate Inference in Continuous
Determinantal Point Processes
Raja Hafiz Affandi1 , Emily B. Fox2 , and Ben Taskar2
1
2
University of Pennsylvania, rajara@wharton.upenn.edu
University of Washington, {ebfox@stat,taskar@cs}.washington.edu
Abstract
Determinantal point processes (DPPs) are random point processes wel... | 4916 |@word determinant:1 middle:1 polynomial:1 seems:3 norm:2 nd:1 open:1 mehta:1 d2:1 decomposition:3 covariance:3 concise:1 thereby:1 nystr:20 recursively:2 configuration:2 denoting:2 document:1 reine:1 freitas:1 current:1 arkk:1 dx:5 must:2 bd:1 determinantal:11 vere:1 fn:1 numerical:1 tilted:1 plot:2 interpretable... |
4,328 | 4,917 | Actor-Critic Algorithms for Risk-Sensitive MDPs
Prashanth L.A.
INRIA Lille - Team SequeL
Mohammad Ghavamzadeh?
INRIA Lille - Team SequeL & Adobe Research
Abstract
In many sequential decision-making problems we may want to manage risk by
minimizing some measure of variability in rewards in addition to maximizing a
st... | 4917 |@word briefly:1 open:1 r:10 simulation:5 prasad:1 p0:4 q1:1 mention:1 initial:5 celebrated:1 configuration:1 current:1 si:2 written:1 plot:2 update:20 fund:1 v:3 stationary:2 mannor:4 along:1 differential:6 prove:1 manner:2 x0:68 ascend:1 expected:5 planning:1 spsa:22 multi:1 bellman:4 discounted:32 td:9 curse:1 ... |
4,329 | 4,918 | Learning from Limited Demonstrations
Beomjoon Kim
School of Computer Science
McGill University
Montreal, Quebec, Canada
Amir-massoud Farahmand
School of Computer Science
McGill University
Montreal, Quebec, Canada
Joelle Pineau
School of Computer Science
McGill University
Montreal, Quebec, Canada
Doina Precup
School... | 4918 |@word mild:2 trial:3 version:2 trialand:1 achievable:1 norm:3 reused:1 open:1 simulation:3 rgb:1 pressure:1 pressed:2 minus:1 reduction:2 initial:7 contains:1 rkhs:5 outperforms:2 current:5 com:1 written:1 must:1 realistic:3 shape:3 hofmann:1 motor:2 designed:1 v:2 alone:1 greedy:4 fewer:1 intelligence:1 stationa... |
4,330 | 4,919 | Distributed Exploration in Multi-Armed Bandits
Eshcar Hillel
Yahoo Labs, Haifa
eshcar@yahoo-inc.com
Zohar Karnin
Yahoo Labs, Haifa
zkarnin@yahoo-inc.com
Tomer Koren?
Technion ? Israel Inst. of Technology
tomerk@technion.ac.il
Ronny Lempel
Yahoo Labs, Haifa
rlempel@yahoo-inc.com
Oren Somekh
Yahoo Labs, Haifa
orens@... | 4919 |@word version:3 eliminating:1 dekel:1 open:1 pick:1 thereby:1 tr:3 venkatasubramanian:2 configuration:1 contains:1 liu:2 denoting:1 past:1 com:4 must:3 explorative:1 numerical:1 v:1 implying:1 half:1 prohibitive:1 website:1 selected:2 mannor:3 node:3 revisited:1 successive:1 elango:1 chakrabarti:1 prove:5 privacy... |
4,331 | 492 | Fast Learning with Predictive Forward Models
Carlos Brody?
Dept. of Computer Science
lIMAS, UNAM
Mexico D.F. 01000
Mexico.
e-mail: carlos@hope. caltech. edu
Abstract
A method for transforming performance evaluation signals distal both in
space and time into proximal signals usable by supervised learning algorithms, p... | 492 |@word trial:5 interleave:1 simulation:1 crucially:1 jacob:14 thereby:1 solid:2 interestingly:1 current:3 surprising:1 yet:1 must:3 eleven:1 device:1 beginning:1 along:1 become:1 differential:1 combine:2 overhead:2 introduce:1 expected:3 elman:1 td:1 curse:1 project:2 provided:1 what:4 transformation:3 differentiat... |
4,332 | 4,920 | Dimension-Free Exponentiated Gradient
Francesco Orabona
Toyota Technological Institute at Chicago
Chicago, USA
francesco@orabona.com
Abstract
I present a new online learning algorithm that extends the exponentiated gradient
framework to infinite dimensional spaces. My analysis shows that the algorithm is
implicitly a... | 4920 |@word briefly:1 version:2 achievable:1 norm:27 seems:1 open:1 hu:7 boundedness:1 contains:1 series:1 recovered:1 com:1 surprising:1 must:2 chicago:2 additive:1 designed:2 update:5 v:1 warmuth:1 provides:1 mathematical:1 direct:1 differential:1 prove:9 consists:1 introductory:1 introduce:5 indeed:3 behavior:1 nor:... |
4,333 | 4,921 | Generalizing Analytic Shrinkage for Arbitrary
Covariance Structures
Daniel Bartz
Department of Computer Science
TU Berlin, Berlin, Germany
daniel.bartz@tu-berlin.de
?
Klaus-Robert Muller
TU Berlin, Berlin, Germany
Korea University, Korea, Seoul
klaus-robert.mueller@tu-berlin.de
Abstract
Analytic shrinkage is a statis... | 4921 |@word kong:1 repository:2 version:1 inversion:1 trial:1 stronger:2 nd:1 simulation:2 covariance:37 prial:4 reduction:1 moment:6 configuration:1 lichman:1 daniel:3 outperforms:4 anne:3 written:3 ronald:1 visible:1 additive:1 underly:1 analytic:19 christian:1 drop:1 intelligence:1 leaf:1 ntrain:2 yi1:2 short:1 cave... |
4,334 | 4,922 | Robust Spatial Filtering with Beta Divergence
Wojciech Samek1,4
1
Duncan Blythe1,4
1,2
?
Klaus-Robert Muller
Motoaki Kawanabe3
Machine Learning Group, Berlin Institute of Technology (TU Berlin), Berlin, German
2
Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
3
ATR Brain Information ... | 4922 |@word trial:39 determinant:2 version:1 inversion:1 proportion:1 d2:1 hyv:1 simulation:1 grk:1 covariance:18 eng:3 tr:3 solid:2 necessity:1 initial:1 series:3 contains:1 daniel:1 outperforms:3 imaginary:1 wd:2 dx:9 written:1 must:1 informative:1 analytic:1 motor:11 update:3 stationary:6 cue:1 selected:3 intelligen... |
4,335 | 4,923 | B IG & Q UIC: Sparse Inverse Covariance Estimation
for a Million Variables
Cho-Jui Hsieh, M?aty?as A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar
Department of Computer Science
University of Texas at Austin
{cjhsieh,sustik,inderjit,pradeepr}@cs.utexas.edu
Russell A. Poldrack
Department of Psychology and Neurobiology... | 4923 |@word kulis:1 determinant:6 version:1 briefly:1 mri:1 seems:1 norm:2 cingulate:1 suitably:1 covariance:18 hsieh:3 decomposition:2 pick:1 dramatic:1 tr:5 recursively:1 carry:1 reduction:1 initial:2 liu:1 selecting:2 interestingly:1 outperforms:1 current:2 recovered:1 rish:1 luo:1 toh:1 mst:1 numerical:1 partition:... |
4,336 | 4,924 | Speeding up Permutation Testing in Neuroimaging ?
Chris Hinrichs? Vamsi K. Ithapu? Qinyuan Sun? Sterling C. Johnson?? Vikas Singh?
?
William S. Middleton Memorial VA Hospital
?
University of Wisconsin?Madison
{hinrichs,vamsi}@cs.wisc.edu {qsun28}@wisc.edu
{scj}@medicine.wisc.edu {vsingh}@biostat.wisc.edu
http://pa... | 4924 |@word mild:3 trial:6 version:3 norm:3 proportion:1 seek:1 decomposition:2 contraction:1 covariance:4 carry:2 contains:1 series:1 united:1 offering:1 denoting:1 mmse:1 longitudinal:1 existing:1 recovered:7 si:1 yet:2 scatter:2 must:5 bd:12 moo:1 numerical:1 shape:4 analytic:2 designed:1 treating:1 sponsored:1 plot... |
4,337 | 4,925 | Robust Multimodal Graph Matching:
Sparse Coding Meets Graph Matching
Marcelo Fiori
Universidad de la
Rep?ublica, Uruguay
mfiori@fing.edu.uy
Joshua Vogelstein
Duke University
Durham, NC 27708
jovo@math.duke.edu
Pablo Sprechmann
Duke University
Durham, NC 27708
pablo.sprechmann@duke.edu
Pablo Mus?e
Universidad de la
R... | 4925 |@word version:10 polynomial:2 norm:5 hu:1 linearized:4 covariance:12 tr:8 solid:3 versatile:1 harder:1 liu:1 series:2 outperforms:3 craddock:2 comparing:1 yet:1 written:1 realistic:1 numerical:1 informative:1 atlas:3 update:4 newest:1 intelligence:4 fewer:1 nervous:1 short:5 math:1 bijection:1 node:10 firstly:1 c... |
4,338 | 4,926 | Deep Fisher Networks
for Large-Scale Image Classification
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
Visual Geometry Group, University of Oxford
{karen,vedaldi,az}@robots.ox.ac.uk
Abstract
As massively parallel computations have become broadly available with modern
GPUs, deep architectures trained on very large d... | 4926 |@word cnn:10 briefly:1 version:1 compression:1 norm:6 nd:7 c0:1 triggs:1 d2:3 seek:1 covariance:4 q1:3 mammal:1 reduction:17 configuration:4 contains:1 score:9 batista:1 document:1 outperforms:1 comparing:1 guez:1 readily:2 gpu:2 devin:1 additive:1 subsequent:1 designed:2 v:7 generative:2 prohibitive:2 greedy:1 c... |
4,339 | 4,927 | Sinkhorn Distances:
Lightspeed Computation of Optimal Transport
Marco Cuturi
Graduate School of Informatics, Kyoto University
mcuturi@i.kyoto-u.ac.jp
Abstract
Optimal transport distances are a fundamental family of distances for probability
measures and histograms of features. Despite their appealing theoretical prop... | 4927 |@word h:1 version:1 norm:3 replicate:1 villani:4 seems:1 open:1 d2:1 tried:1 jacob:2 p0:1 pick:1 minus:3 boundedness:1 carry:1 celebrated:1 contains:3 selecting:1 tuned:1 ours:1 franklin:3 comparing:1 jaynes:2 yet:1 written:2 gpu:7 numerical:1 cheap:1 zaid:1 update:4 v:2 prohibitive:1 de1:1 core:1 qjk:14 nearness... |
4,340 | 4,928 | Understanding variable importances
in forests of randomized trees
Gilles Louppe, Louis Wehenkel, Antonio Sutera and Pierre Geurts
Dept. of EE & CS, University of Li`ege, Belgium
{g.louppe, l.wehenkel, a.sutera, p.geurts}@ulg.ac.be
Abstract
Despite growing interest and practical use in various scientific areas, variabl... | 4928 |@word illustrating:1 version:1 seems:1 proportion:1 stronger:1 confirms:4 simulation:1 decomposition:12 thereby:1 tr:3 reduction:2 wrapper:1 liu:2 configuration:1 selecting:1 interestingly:1 dubourg:1 current:1 nt:6 yet:1 john:2 realistic:1 subsequent:1 partition:2 visible:1 informative:6 plot:4 alone:5 intellige... |
4,341 | 4,929 | Learning and using language via recursive pragmatic
reasoning about other agents
Nathaniel J. Smith?
University of Edinburgh
Noah D. Goodman
Stanford University
Michael C. Frank
Stanford University
Abstract
Language users are remarkably good at making inferences about speakers? intentions in context, and children l... | 4929 |@word trial:1 private:1 version:2 stronger:4 proportion:1 simulation:6 pick:1 dramatic:1 shot:6 recursively:2 moment:1 contains:1 subjective:1 existing:1 current:5 contextual:3 yet:2 intriguing:2 must:9 john:1 informative:1 shape:1 cheap:11 wanted:1 remove:2 update:2 depict:1 infant:2 cue:1 instantiate:1 guess:5 ... |
4,342 | 493 | Some Approximation Properties of Projection
Pursuit Learning Networks
Ying Zhao Christopher G. Atkeson
The Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
This paper will address an important question in machine learning: What
kind of network architectures work be... | 493 |@word version:1 polynomial:16 nd:3 hu:1 seek:1 cla:1 tr:1 n8:2 wd:1 od:3 dx:1 moo:5 numerical:1 analytic:1 intelligence:1 fewer:1 math:1 node:5 sigmoidal:6 belt:1 hermite:1 c2:1 loll:1 fitting:1 huber:4 ra:4 examine:1 jlt:1 spherical:6 curse:8 jm:3 becomes:1 provided:4 underlying:5 proceeding6:1 what:3 kind:2 deve... |
4,343 | 4,930 | Model Selection for High-Dimensional Regression
under the Generalized Irrepresentability Condition
Andrea Montanari
Stanford University
Stanford, CA 94305
montanar@stanford.edu
Adel Javanmard
Stanford University
Stanford, CA 94305
adelj@stanford.edu
Abstract
In the high-dimensional regression model a response variab... | 4930 |@word cu:2 version:1 stronger:2 norm:3 c0:4 hu:1 integrative:1 bn:17 covariance:15 contains:2 denoting:1 comparing:1 must:3 readily:1 resampling:1 selected:2 lr:1 characterization:2 provides:1 clarified:1 zhang:1 c2:9 prove:9 kuj:2 manner:1 introduce:4 peng:1 javanmard:3 indeed:2 andrea:1 cand:4 p1:7 roughly:2 mu... |
4,344 | 4,931 | Confidence Intervals and Hypothesis Testing for
High-Dimensional Statistical Models
Andrea Montanari
Stanford University
Stanford, CA 94305
montanar@stanford.edu
Adel Javanmard
Stanford University
Stanford, CA 94305
adelj@stanford.edu
Abstract
Fitting high-dimensional statistical models often requires the use of non... | 4931 |@word mri:1 version:2 norm:9 suitably:1 integrative:1 bn:13 covariance:11 decomposition:1 decorrelate:1 concise:1 necessity:2 contains:2 denoting:1 current:1 comparing:1 com:1 assigning:1 must:2 realize:1 numerical:2 plot:5 resampling:1 selected:1 vanishing:1 record:2 rntot:2 characterization:3 detecting:1 provid... |
4,345 | 4,932 | Compressive Feature Learning
Robert West
Department of Computer Science
Stanford University
west@cs.stanford.edu
Hristo S. Paskov
Department of Computer Science
Stanford University
hpaskov@cs.stanford.edu
Trevor J. Hastie
Department of Statistics
Stanford University
hastie@stanford.edu
John C. Mitchell
Department o... | 4932 |@word middle:1 version:3 bigram:3 compression:38 advantageous:1 norm:3 d2:3 seek:1 decomposition:2 elisseeff:1 thereby:1 harder:1 mcauley:1 reduction:1 celebrated:1 series:2 fragment:1 selecting:2 punishes:1 efficacy:1 liu:3 document:38 bc:1 prefix:2 ours:1 rightmost:1 existing:1 current:1 recovered:1 comparing:2... |
4,346 | 4,933 | Pass-Efficient Unsupervised Feature Selection
Haim Schweitzer
Department of Computer Science
The University of Texas at Dallas
HSchweitzer@utdallas.edu
Crystal Maung
Department of Computer Science
The University of Texas at Dallas
Crystal.Maung@gmail.com
Abstract
The goal of unsupervised feature selection is to iden... | 4933 |@word repository:2 cu:1 norm:3 bf:13 reused:1 termination:2 km:2 overwritten:1 decomposition:3 pavel:1 q1:1 mlk:2 reduction:5 initial:3 liu:1 series:1 contains:1 selecting:2 woodruff:2 current:1 com:1 comparing:1 skipping:3 gmail:1 john:2 numerical:4 additive:1 kdd:2 enables:1 cheap:1 update:3 intelligence:1 sele... |
4,347 | 4,934 | Better Approximation and Faster Algorithm Using
the Proximal Average
Yaoliang Yu
Department of Computing Science, University of Alberta, Edmonton AB T6G 2E8, Canada
yaoliang@cs.ualberta.ca
Abstract
It is a common practice to approximate ?complicated? functions with more
friendly ones.
In large-scale machine learning ... | 4934 |@word proceeded:1 middle:1 version:4 polynomial:1 norm:6 calculus:1 semicontinuous:1 tried:1 decomposition:1 pg:18 disappointingly:1 series:2 selecting:1 lucet:2 interestingly:1 kx0:2 current:1 comparing:1 yet:2 must:1 readily:1 numerical:1 cheap:1 remove:1 v:2 rudin:1 amir:1 accordingly:1 xk:1 iterates:1 complet... |
4,348 | 4,935 | Polar Operators for Structured Sparse Estimation
Xinhua Zhang
Machine Learning Research Group
National ICT Australia and ANU
xinhua.zhang@anu.edu.au
Yaoliang Yu and Dale Schuurmans
Department of Computing Science, University of Alberta
Edmonton, Alberta T6G 2E8, Canada
{yaoliang,dale}@cs.ualberta.ca
Abstract
Structu... | 4935 |@word cu:1 version:1 norm:24 seek:1 tried:1 decomposition:1 pg:3 pick:1 sepulchre:1 reduction:11 wrapper:1 liu:4 contains:3 series:1 ktv:6 renewed:1 psj:1 existing:1 mishra:1 current:4 com:1 incidence:1 recovered:2 must:4 john:1 enables:1 remove:1 plot:1 update:6 v:3 greedy:2 selected:1 plane:2 xk:1 iterates:4 pr... |
4,349 | 4,936 | On the Linear Convergence of the Proximal Gradient
Method for Trace Norm Regularization
Ke Hou, Zirui Zhou, Anthony Man?Cho So
Department of Systems Engineering & Engineering Management
The Chinese University of Hong Kong
Shatin, N. T., Hong Kong
{khou,zrzhou,manchoso}@se.cuhk.edu.hk
Zhi?Quan Luo
Department of Electri... | 4936 |@word kong:2 polynomial:2 norm:32 nd:1 open:3 decomposition:5 simplifying:1 thereby:1 reduction:1 series:1 existing:1 ka:7 comparing:1 optim:1 luo:5 toh:1 readily:1 hou:1 additive:1 numerical:3 plot:1 xk:5 characterization:1 iterates:2 math:4 zhang:3 ik:3 prove:4 introductory:1 polyhedral:2 introduce:1 indeed:3 r... |
4,350 | 4,937 | Accelerating Stochastic Gradient Descent using
Predictive Variance Reduction
Rie Johnson
RJ Research Consulting
Tarrytown NY, USA
Tong Zhang
Baidu Inc., Beijing, China
Rutgers University, New Jersey, USA
Abstract
Stochastic gradient descent is popular for large scale optimization but has slow
convergence asymptotical... | 4937 |@word mild:1 version:2 advantageous:1 hsieh:2 pick:2 sgd:72 kwm:1 minus:1 reduction:13 initial:1 practiced:1 tuned:8 past:1 ka:1 com:1 activation:1 drop:1 update:16 half:1 selected:1 indicative:2 provides:4 consulting:1 iterates:1 node:2 toronto:1 org:1 simpler:5 zhang:11 baidu:1 kak22:1 prove:3 introductory:1 in... |
4,351 | 4,938 | Accelerated Mini-Batch Stochastic Dual Coordinate
Ascent
Shai Shalev-Shwartz
School of Computer Science and Engineering
Hebrew University, Jerusalem, Israel
Tong Zhang
Department of Statistics
Rutgers University, NJ, USA
Abstract
Stochastic dual coordinate ascent (SDCA) is an effective technique for solving
regulariz... | 4938 |@word middle:1 version:5 achievable:1 polynomial:1 norm:4 nd:5 dekel:5 vldb:1 pick:2 sgd:10 venkatasubramanian:1 bradley:2 danny:3 gpu:1 john:3 partition:1 enables:1 update:2 depict:1 bickson:3 intelligence:1 provides:1 node:34 zhang:11 mathematical:2 dn:1 prove:1 privacy:1 introduce:1 theoretically:1 indeed:2 ha... |
4,352 | 4,939 | Estimation, Optimization, and Parallelism when
Data is Sparse
H. Brendan McMahan2
Google, Inc.2
Seattle, WA 98103
mcmahan@google.com
John C. Duchi1,2
Michael I. Jordan1
University of California, Berkeley1
Berkeley, CA 94720
{jduchi,jordan}@eecs.berkeley.edu
Abstract
We study stochastic optimization problems when the... | 4939 |@word middle:2 version:2 stronger:3 suitably:1 p0:14 attainable:1 sgd:5 moment:1 initial:2 contains:1 selecting:1 tuned:1 document:1 current:1 com:1 savage:1 attracted:1 must:1 john:1 numerical:1 subsequent:2 benign:1 plot:4 update:15 juditsky:1 selected:1 complementing:1 ubuntu:1 inspection:1 core:2 characteriza... |
4,353 | 494 | Learning in Feedforward Networks with Nonsmooth
Functions
Nicholas J. Redding?
Information Technology Division
Defence Science and Tech. Org.
P.O. Box 1600 Salisbury
Adelaide SA 5108 Australia
T.Downs
Intelligent Machines Laboratory
Dept of Electrical Engineering
University of Queensland
Brisbane Q 4072 Australia
Ab... | 494 |@word version:1 norm:10 calculus:2 simulation:1 queensland:2 bn:1 accommodate:1 series:1 current:3 wd:1 must:1 john:3 numerical:1 happen:1 plot:1 xex:1 half:3 accordingly:1 plane:1 steepest:3 short:1 provides:2 math:1 lx:1 org:1 sigmoidal:1 simpler:1 mathematical:2 along:8 contacted:1 become:1 vjk:1 adk:2 overhead... |
4,354 | 4,940 | Linear Convergence with Condition Number
Independent Access of Full Gradients
Lijun Zhang Mehrdad Mahdavi Rong Jin
Department of Computer Science and Engineering
Michigan State University, East Lansing, MI 48824, USA
{zhanglij,mahdavim,rongjin}@msu.edu
Abstract
For smooth and strongly convex optimizations,
the optima... | 4940 |@word polynomial:1 interleave:1 norm:2 stronger:1 reduction:1 initial:1 series:1 current:2 egd:2 written:1 john:1 remove:1 drop:1 update:4 juditsky:2 beginning:1 short:1 core:1 math:1 zhang:3 mathematical:2 become:1 fitting:1 introductory:1 lansing:1 frequently:1 decreasing:1 becomes:1 provided:2 bounded:4 kind:1... |
4,355 | 4,941 | Mixed Optimization for Smooth Functions
Mehrdad Mahdavi
Lijun Zhang
Rong Jin
Department of Computer Science and Engineering, Michigan State University, MI, USA
{mahdavim,zhanglij,rongjin}@msu.edu
Abstract
It is well known that the optimal
convergence rate for stochastic optimization of
?
smooth functions is O(1/ T ),... | 4941 |@word briefly:1 stronger:2 norm:6 dekel:1 open:2 git:8 nemirovsky:1 sgd:10 moment:1 initial:1 series:1 drop:1 update:4 juditsky:1 implying:1 selected:1 beginning:3 ith:1 iterates:1 math:1 zhang:4 mathematical:1 direct:1 fitting:1 introductory:1 introduce:3 examine:1 growing:1 byrd:1 solver:1 becomes:1 begin:1 pro... |
4,356 | 4,942 | Stochastic Convex Optimization with
Multiple Objectives
Mehrdad Mahdavi
Michigan State University
Tianbao Yang
NEC Labs America, Inc
Rong Jin
Michigan State University
mahdavim@cse.msu.edu
tyang@nec-labs.com
rongjin@cse.msu.edu
Abstract
In this paper, we are interested in the development of efficient algorithms ... | 4942 |@word mild:1 trial:11 exploitation:1 norm:1 stronger:1 open:2 bining:1 d2:4 seek:1 covariance:1 mention:1 boundedness:1 accommodate:1 reduction:3 existing:1 current:1 com:1 designed:1 update:4 juditsky:1 selected:1 leaf:1 ith:2 provides:2 mannor:1 cse:2 complication:1 lipchitz:1 mathematical:1 direct:1 consists:1... |
4,357 | 4,943 | Data-driven Distributionally Robust Polynomial
Optimization
Martin Mevissen
IBM Research?Ireland
martmevi@ie.ibm.com
Emanuele Ragnoli
IBM Research?Ireland
eragnoli@ie.ibm.com
Jia Yuan Yu
IBM Research?Ireland
jy@osore.ca
Abstract
We consider robust optimization for polynomial optimization problems where the
uncertai... | 4943 |@word kgk:1 version:1 polynomial:46 norm:5 seems:1 open:1 pressure:7 harder:1 blade:1 reduction:1 moment:12 series:3 contains:1 com:2 comparing:1 optim:1 scatter:1 readily:1 fn:1 partition:1 plot:1 selected:2 short:1 math:4 node:13 mannor:1 gx:1 lipchitz:1 zhang:1 mathematical:3 nodal:1 constructed:6 c2:3 yuan:1 ... |
4,358 | 4,944 | Multiscale Dictionary Learning for
Estimating Conditional Distributions
Francesca Petralia
Department of Genetics and Genomic Sciences
Icahn School of Medicine at Mt Sinai
New York, NY 10128, U.S.A.
francesca.petralia@mssm.edu
Joshua Vogelstein
Child Mind Institute
Department of Statistical Science
Duke University
Du... | 4944 |@word mri:3 middle:4 villani:1 nd:2 seek:1 carolina:2 simulation:17 decomposition:7 jacob:1 pg:1 elisseeff:1 pulse:1 solid:1 recursively:1 reduction:3 series:2 denoting:1 outperforms:4 nowlan:1 yet:1 numerical:3 partition:15 informative:1 shape:1 plot:2 depict:4 update:3 msb:24 intelligence:1 greedy:1 generative:... |
4,359 | 4,945 | On the Sample Complexity of Subspace Learning
Guillermo D. Canas
Massachussetss Institute of Technology
guilledc@mit.edu
Alessandro Rudi
Robotics Brain and Cognitive Science
Istituto Italiano di Tecnologia
alessandro.rudi@iit.it
Lorenzo Rosasco
Universita? degli Studi di Genova, LCSL,
Massachusetts Institute of Tech... | 4945 |@word h:2 trial:1 briefly:1 inversion:2 polynomial:6 norm:2 stronger:1 version:2 closure:1 covariance:16 decomposition:4 q1:1 tr:1 reduction:5 moment:3 contains:3 rkhs:3 ours:1 interestingly:1 past:4 existing:2 outperforms:1 dx:1 must:3 written:1 numerical:4 drop:4 plot:2 alone:1 implying:1 core:1 provides:1 comp... |
4,360 | 4,946 | Least Informative Dimensions
Fabian H. Sinz
Department for Neuroethology
Eberhard Karls University T?ubingen
fabee@epagoge.de
Anna St?ockl
Department for Functional Zoology
Lund University, Sweden
Anna.Stockl@biol.lu.se
Jan Grewe
Department for Neuroethology
Eberhard Karls University T?ubingen
jan.grewe@uni-tuebingen... | 4946 |@word h:12 trial:1 neurophysiology:1 version:1 inversion:1 middle:2 norm:2 seems:1 nd:1 covariance:8 decomposition:5 electroreceptors:1 thereby:1 tr:3 solid:1 harder:1 ipm:3 carry:2 reduction:5 contains:5 tuned:1 rkhs:2 ours:1 current:1 comparing:6 must:2 refines:1 informative:44 treating:1 v:1 prohibitive:2 sele... |
4,361 | 4,947 | Blind Calibration in Compressed Sensing using
Message Passing Algorithms
?
Christophe Schulke
Univ Paris Diderot, Sorbonne Paris Cit?e,
ESPCI and CNRS UMR 7083
Paris 75005, France
Francesco Caltagirone
Institut de Physique Th?eorique
CEA Saclay and CNRS URA 2306
91191 Gif-sur-Yvette, France
Florent Krzakala
ENS and ... | 4947 |@word version:2 pw:3 seems:1 grey:1 d2:1 simulation:1 simplifying:1 contains:1 denoting:1 amp:43 outperforms:3 multiuser:3 recovered:5 com:1 written:2 numerical:7 subsequent:1 pertinent:1 designed:1 update:1 plane:3 gribonval:2 provides:1 math:1 node:4 location:1 compressible:1 org:1 simpler:2 zhang:1 become:2 sy... |
4,362 | 4,948 | Estimating LASSO Risk and Noise Level
Mohsen Bayati
Stanford University
bayati@stanford.edu
Murat A. Erdogdu
Stanford University
erdogdu@stanford.edu
Andrea Montanari
Stanford University
montanar@stanford.edu
Abstract
We study the fundamental problems of variance and risk estimation in high dimensional statistical ... | 4948 |@word version:2 briefly:2 nd:1 seek:1 simulation:8 covariance:5 tr:5 solid:1 moment:1 bai:2 series:1 selecting:1 amp:17 outperforms:1 existing:1 numerical:4 enables:1 stationary:1 selected:3 characterization:1 provides:2 ct07:3 draft:1 zhang:1 along:2 replication:3 prove:1 combine:1 javanmard:1 roughly:1 behavior... |
4,363 | 4,949 | A Graphical Transformation for Belief Propagation:
Maximum Weight Matchings and Odd-Sized Cycles
Jinwoo Shin
Andrew E. Gelfand ?
Department of Electrical Engineering
Department of Computer Science
Theoretical Division &
Korea Advanced Institute of Science and Technology
University of California, Irvine
Center f... | 4949 |@word eliminating:1 polynomial:3 termination:1 initial:1 configuration:4 recovered:1 current:4 nt:2 readily:1 koetter:1 j1:2 designed:3 update:4 n0:10 bickson:1 half:1 leaf:2 intelligence:3 plane:12 manfred:1 provides:1 math:1 node:2 successive:1 mtj:1 mathematical:1 c2:2 constructed:2 focs:1 prove:3 consists:3 e... |
4,364 | 495 | Best-First Model Merging for
Dynamic Learning and Recognition
Stephen M. Omohundro
International Computer Science Institute
1947 CenteJ' Street, Suite 600
Berkeley, California 94704
Abstract
"Best-first model merging" is a general technique for dynamically
choosing the structure of a neural or related architecture whi... | 495 |@word briefly:1 dramatic:1 tr:1 shot:1 reduction:1 chervonenkis:1 tuned:1 interestingly:1 must:2 partition:1 predetermined:1 hypothesize:1 aside:1 v:1 discovering:1 fewer:1 leaf:4 plane:2 nearness:1 probablity:1 provides:2 location:1 successive:1 hyperplanes:1 simpler:1 five:1 along:1 constructed:3 direct:1 consis... |
4,365 | 4,950 | Sensor Selection in High-Dimensional
Gaussian Trees with Nuisances
Jonathan P. How
MIT LIDS
jhow@mit.edu
Daniel Levine
MIT LIDS
dlevine@mit.edu
Abstract
We consider the sensor selection problem on multivariate Gaussian distributions
where only a subset of latent variables is of inferential interest. For pairs of vert... | 4950 |@word exploitation:1 inversion:7 nd:2 confirms:1 seek:1 decomposition:14 covariance:1 pg:10 incurs:2 reduction:1 liu:1 score:5 selecting:2 daniel:1 denoting:1 loeliger:1 assigning:1 must:2 john:1 subsequent:2 partition:2 informative:3 j1:1 additive:2 update:1 v:2 greedy:15 selected:3 leaf:3 half:1 parameterizatio... |
4,366 | 4,951 | ?-Optimality for Active Learning on Gaussian
Random Fields
Yifei Ma
Machine Learning Department
Carnegie Mellon University
yifeim@cs.cmu.edu
Roman Garnett
Computer Science Department
University of Bonn
rgarnett@uni-bonn.de
Jeff Schneider
Robotics Institute
Carnegie Mellon University
schneide@cs.cmu.edu
Abstract
A co... | 4951 |@word luk:2 cu:1 middle:1 inversion:1 compression:1 proportion:4 laurence:1 c0:1 open:1 simulation:1 covariance:10 citeseer:2 pick:1 tr:3 carry:1 reduction:10 selecting:1 denoting:1 genetic:1 outperforms:6 comparing:2 written:1 john:2 partition:1 happen:1 cheap:1 plot:1 update:4 maxv:1 v:2 greedy:24 selected:2 in... |
4,367 | 4,952 | Bayesian optimization explains human active search
Ali Borji
Department of Computer Science
USC, Los Angeles, 90089
borji@usc.edu
Laurent Itti
Departments of Neuroscience and Computer Science
USC, Los Angeles, 90089
itti@usc.edu
Abstract
Many real-world problems have complicated objective functions. To optimize
such... | 4952 |@word trial:32 exploitation:5 version:1 eliminating:1 polynomial:7 seems:1 approved:1 nd:6 middle:1 human2:4 open:1 termination:1 calculus:1 zilinskas:1 seek:1 tried:1 mockus:1 covariance:1 irb:1 pavel:1 pick:2 thereby:1 series:1 score:6 loc:1 genetic:2 tuned:1 interestingly:3 freitas:1 blank:2 yet:1 must:1 najem... |
4,368 | 4,953 | Latent Structured Active Learning
Wenjie Luo
TTI Chicago
wenjie.luo@ttic.edu
Alexander G. Schwing
ETH Zurich
aschwing@inf.ethz.ch
Raquel Urtasun
TTI Chicago
rurtasun@ttic.edu
Abstract
In this paper we present active learning algorithms in the context of structured
prediction problems. To reduce the amount of labeli... | 4953 |@word kohli:1 pw:2 norm:1 anthrax:1 decomposition:1 pick:1 dramatic:1 edema:1 reduction:3 initial:5 configuration:2 score:1 selecting:2 hoiem:2 denoting:1 existing:3 coactive:2 current:2 comparing:1 contextual:1 luo:2 si:3 yet:1 parsing:2 readily:1 chicago:2 partition:1 informative:3 hofmann:1 hypothesize:1 plot:... |
4,369 | 4,954 | Low-Rank Matrix and Tensor Completion via
Adaptive Sampling
Akshay Krishnamurthy
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15213
akshaykr@cs.cmu.edu
Aarti Singh
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
aartisingh@cs.cmu.edu
Abstract
We study low rank matr... | 4954 |@word version:2 middle:2 briefly:1 polynomial:1 norm:9 open:1 confirms:1 simulation:3 decomposition:5 nystr:1 recursively:1 series:1 selecting:1 ours:4 outperforms:1 existing:5 comparing:1 nt:12 yet:1 luis:1 readily:2 john:1 concatenate:1 sanjiv:1 informative:3 enables:1 remove:1 plot:5 implying:1 fewer:2 item:1 ... |
4,370 | 4,955 | Adaptive Submodular Maximization in Bandit Setting
Victor Gabillon
Branislav Kveton
Zheng Wen
INRIA Lille - team SequeL
Technicolor Labs
Electrical Engineering Department
Villeneuve d?Ascq, France
Palo Alto, CA
Stanford University
victor.gabillon@inria.fr branislav.kveton@technicolor.com zhengwen@stanford.edu
Brian Er... | 4955 |@word exploitation:1 polynomial:1 asks:1 recursively:1 score:2 daniel:1 ours:3 past:2 outperforms:2 current:1 com:2 yet:1 written:3 ronald:1 shape:1 designed:2 update:1 intelligence:3 selected:5 greedy:16 item:70 core:1 node:1 preference:11 org:1 five:1 mathematical:1 become:2 incorrect:1 prove:7 introduce:1 hard... |
4,371 | 4,956 | Auditing: Active Learning with
Outcome-Dependent Query Costs
Sivan Sabato
Microsoft Research New England
sivan.sabato@microsoft.com
Anand D. Sarwate
TTI-Chicago
asarwate@ttic.edu
Nathan Srebro
Technion-Israel Institute of Technology and TTI-Chicago
nati@ttic.edu
Abstract
We propose a learning setting in which unlab... | 4956 |@word version:8 achievable:1 polynomial:2 open:3 d2:3 seek:1 mention:1 reduction:4 selecting:2 mag:4 chervonenkis:2 tuned:1 horvitz:1 existing:1 err:50 current:1 com:1 beygelzimer:3 protection:1 must:2 refines:1 chicago:2 remove:1 atlas:1 v:2 greedy:5 selected:1 intelligence:1 warmuth:1 detecting:1 multiset:3 pro... |
4,372 | 4,957 | Buy-in-Bulk Active Learning
Jaime Carbonell
Language Technologies Institute,
Carnegie Mellon University
jgc@cs.cmu.edu
Liu Yang
Machine Learning Department,
Carnegie Mellon University
liuy@cs.cmu.edu
Abstract
In many practical applications of active learning, it is more cost-effective to request labels in large batc... | 4957 |@word version:5 achievable:1 chakraborty:1 km:3 incurs:1 asks:1 thereby:1 reduction:1 liu:1 series:1 comparing:1 beygelzimer:1 dx:3 must:2 realistic:1 atlas:2 designed:2 update:6 v:2 discrimination:1 yi1:1 prespecified:1 record:1 provides:1 coarse:1 c22:1 zhang:1 c2:17 direct:3 become:1 overhead:1 inside:1 indeed... |
4,373 | 4,958 | Active Learning for Probabilistic Hypotheses Using
the Maximum Gibbs Error Criterion
Nguyen Viet Cuong
Wee Sun Lee
Nan Ye
Department of Computer Science
National University of Singapore
{nvcuong,leews,yenan}@comp.nus.edu.sg
Kian Ming A. Chai
Hai Leong Chieu
DSO National Laboratories, Singapore
{ckianmin,chaileon}@dso.o... | 4958 |@word version:12 nd:2 p0:23 reduction:8 initial:1 electronics:1 contains:1 score:6 selecting:7 daniel:2 denoting:2 document:6 current:3 z2:1 si:3 readily:1 john:1 partition:3 christian:2 update:6 greedy:18 selected:18 leaf:2 intelligence:1 mccallum:2 beginning:1 sys:1 pc0:1 yuxin:1 node:2 org:1 simpler:1 height:3... |
4,374 | 4,959 | Marginals-to-Models Reducibility
Michael Kearns
University of Pennsylvania
mkearns@cis.upenn.edu
Tim Roughgarden
Stanford University
tim@cs.stanford.edu
Abstract
We consider a number of classical and new computational problems regarding
marginal distributions, and inference in models specifying a full joint distribu... | 4959 |@word polynomial:50 stronger:1 norm:1 termination:1 seek:1 reduction:18 mkearns:1 series:1 selecting:1 ati:1 surprising:1 partition:8 happen:1 intelligence:1 record:1 completeness:1 provides:3 unbounded:1 khachiyan:1 prove:4 rife:1 ray:1 manner:1 pairwise:10 lov:1 notably:1 upenn:1 behavior:1 detects:1 automatica... |
4,375 | 496 | Constrained Optimization Applied to the
Parameter Setting Problem for Analog Circuits
David Kirk, Kurt Fleischer, Lloyd Watts~ Alan Barr
Computer Graphics 350-74
California Institute of Technology
Pasadena, CA 91125
Abstract
We use constrained optimization to select operating parameters for two
circuits: a simple 3-t... | 496 |@word nd:1 open:1 tr:1 solid:1 initial:1 tuned:1 document:1 kurt:1 current:4 must:2 john:1 numerical:3 shape:1 cheap:1 v:1 implying:1 device:2 beginning:1 record:1 provides:1 attack:1 mathematical:1 along:2 constructed:1 become:2 behavioral:1 manner:1 introduce:1 expected:3 behavior:16 oscilloscope:2 examine:1 fre... |
4,376 | 4,960 | Learning Chordal Markov Networks by
Constraint Satisfaction
Jukka Corander??
University of Helsinki
Finland
Tomi Janhunen??
Aalto University
Finland
Jussi Rintanen???
Aalto University
Finland
Henrik Nyman?
? Akademi University
Abo
Finland
Johan Pensar?
? Akademi University
Abo
Finland
Abstract
We investigate the p... | 4960 |@word torsten:2 version:4 polynomial:2 giudici:2 c0:22 open:2 adrian:1 biere:3 tried:2 accounting:1 reduction:2 initial:1 cyclic:1 contains:1 score:12 configuration:2 daniel:1 genetic:1 denoting:1 existing:3 chordal:14 anne:1 si:10 assigning:1 conjunctive:1 written:2 readily:1 must:4 john:1 chu:1 ronald:1 romero:... |
4,377 | 4,961 | Bayesian Estimation of Latently-grouped Parameters
in Undirected Graphical Models
David Page
Dept of BMI, University of Wisconsin
Madison, WI 53706
page@biostat.wisc.edu
Jie Liu
Dept of CS, University of Wisconsin
Madison, WI 53706
jieliu@cs.wisc.edu
Abstract
In large-scale applications of undirected graphical model... | 4961 |@word sba:35 replicate:3 unif:5 hyv:2 simulation:8 bn:2 contraction:2 covariance:1 contrastive:9 citeseer:1 accommodate:2 initial:2 liu:3 score:2 existing:2 current:7 comparing:1 assigning:2 partition:1 informative:2 remove:6 update:11 resampling:1 intelligence:1 accordingly:1 menendez:1 geyer:1 record:1 filtered... |
4,378 | 4,962 | On Sampling from the Gibbs Distribution with
Random Maximum A-Posteriori Perturbations
Tamir Hazan
University of Haifa
Subhransu Maji
TTI Chicago
Tommi Jaakkola
CSAIL, MIT
Abstract
In this paper we describe how MAP inference can be used to sample efficiently
from Gibbs distributions. Specifically, we provide means f... | 4962 |@word kohli:1 h:1 middle:3 polynomial:3 seek:1 tr:1 recursively:1 bai:1 configuration:9 series:3 liu:1 interestingly:1 recovered:1 comparing:3 current:2 readily:1 parsing:1 determinantal:2 chicago:1 partition:40 happen:1 analytic:1 plot:1 intelligence:2 devising:1 imitate:1 plane:2 accepting:1 tarlow:1 provides:4... |
4,379 | 4,963 | EDML for Learning Parameters in
Directed and Undirected Graphical Models
Khaled S. Refaat, Arthur Choi, Adnan Darwiche
Computer Science Department
University of California, Los Angeles
{krefaat,aychoi,darwiche}@cs.ucla.edu
Abstract
EDML is a recently proposed algorithm for learning parameters in Bayesian networks. It... | 4963 |@word cu:1 version:3 radim:1 tedious:2 adnan:6 jointree:3 seek:1 initial:6 liu:1 uma:1 daniel:1 genetic:1 bc:3 interestingly:1 current:1 comparing:1 assigning:1 yet:1 must:2 john:2 subsequent:1 partition:3 numerical:1 confirming:1 update:7 stationary:21 intelligence:2 fewer:1 parameterization:1 provides:1 math:1 ... |
4,380 | 4,964 | Projecting Ising Model Parameters for Fast Mixing
Xianghang Liu
NICTA, The University of New South Wales
xianghang.liu@nicta.com.au
Justin Domke
NICTA, The Australian National University
justin.domke@nicta.com.au
Abstract
Inference in general Ising models is difficult, due to high treewidth making treebased algorith... | 4964 |@word mild:1 trial:1 polynomial:3 norm:19 stronger:1 johansson:1 unif:3 seek:1 simulation:1 decomposition:5 pick:1 liu:2 configuration:6 contains:1 zij:4 selecting:3 freitas:1 bradley:1 current:2 com:2 comparing:1 must:2 john:1 partition:1 informative:1 drop:1 plot:1 update:5 v:1 stationary:5 implying:1 amir:1 xk... |
4,381 | 4,965 | Embed and Project:
Discrete Sampling with Universal Hashing
Stefano Ermon, Carla P. Gomes
Dept. of Computer Science
Cornell University
Ithaca NY 14853, U.S.A.
Ashish Sabharwal
IBM Watson Research Ctr.
Yorktown Heights
NY 10598, U.S.A.
Bart Selman
Dept. of Computer Science
Cornell University
Ithaca NY 14853, U.S.A.
... | 4965 |@word version:1 briefly:2 polynomial:1 chakraborty:2 nd:1 open:2 heuristically:1 bn:1 accounting:1 harder:2 reduction:2 configuration:9 series:1 paw:27 daniel:1 tuned:1 past:2 outperforms:2 existing:1 discretization:9 michal:1 si:2 scatter:1 conjunctive:1 givry:1 fn:1 partition:4 plot:1 bart:4 hash:19 v:6 fewer:2... |
4,382 | 4,966 | Learning Stochastic Inverses
?
Andreas Stuhlmuller
Brain and Cognitive Sciences
MIT
Jessica Taylor
Department of Computer Science
Stanford University
Noah D. Goodman
Department of Psychology
Stanford University
Abstract
We describe a class of algorithms for amortized inference in Bayesian networks.
In this setting,... | 4966 |@word seems:1 simulation:2 bn:1 citeseer:1 brightness:2 offload:1 past:3 current:1 z2:3 comparing:1 yet:1 must:2 john:1 dechter:1 realistic:1 remove:1 designed:1 resampling:1 generative:1 leaf:4 intelligence:3 haario:3 provides:2 node:39 five:1 along:1 direct:1 descendant:1 yuan:2 combine:1 introduce:1 x0:4 peot:... |
4,383 | 4,967 | Approximate Gaussian process inference for the drift
of stochastic differential equations
Andreas Ruttor
Computer Science, TU Berlin
andreas.ruttor@tu-berlin.de
Philipp Batz
Computer Science, TU Berlin
philipp.batz@tu-berlin.de
Manfred Opper
Computer Science, TU Berlin
manfred.opper@tu-berlin.de
Abstract
We introdu... | 4967 |@word version:2 inversion:1 polynomial:6 norm:1 seems:1 hu:1 d2:1 linearized:2 tried:1 covariance:2 p0:6 edric:1 contains:5 series:1 selecting:1 denoting:1 past:2 discretization:4 dx:9 attracted:1 written:1 dw1:1 john:1 additive:1 realistic:1 numerical:1 sdes:4 stationary:1 xk:7 core:2 record:1 manfred:6 recomput... |
4,384 | 4,968 | Online Learning of Nonparametric Mixture Models
via Sequential Variational Approximation
Dahua Lin
Toyota Technological Institute at Chicago
dhlin@ttic.edu
Abstract
Reliance on computationally expensive algorithms for inference has been limiting
the use of Bayesian nonparametric models in large scale applications. To... | 4968 |@word proportion:4 seek:1 tried:1 splitmerge:1 q1:1 recursively:1 configuration:3 series:1 contains:2 score:1 document:11 existing:4 written:1 readily:1 john:2 explorative:1 chicago:1 partition:7 remove:3 drop:1 plot:1 update:10 progressively:3 advancement:1 blei:9 provides:2 characterization:1 location:1 zhang:1... |
4,385 | 4,969 | Memoized Online Variational Inference for
Dirichlet Process Mixture Models
Michael C. Hughes and Erik B. Sudderth
Department of Computer Science, Brown University, Providence, RI 02912
mhughes@cs.brown.edu, sudderth@cs.brown.edu
Abstract
Variational inference algorithms provide the most effective framework for largesc... | 4969 |@word nkb:3 proportion:2 km:1 crucially:1 covariance:9 datagenerating:1 configuration:4 series:1 exclusively:1 contains:1 selecting:1 tuned:1 document:1 outperforms:1 existing:2 current:7 elliptical:1 ka:5 yet:2 scatter:1 must:1 written:2 additive:2 happen:1 informative:1 subsequent:1 remove:4 plot:6 drop:1 updat... |
4,386 | 4,970 | Regret based Robust Solutions for
Uncertain Markov Decision Processes
Asrar Ahmed
Singapore Management University
masrara@smu.edu.sg
Pradeep Varakantham
Singapore Management University
pradeepv@smu.edu.sg
Yossiri Adulyasak
Massachusetts Institute of Technology
yossiri@smart.mit.edu
Patrick Jaillet
Massachusetts Inst... | 4970 |@word h:2 polynomial:1 d2:1 seek:1 propagate:1 p0:2 pick:1 selecting:1 daniel:1 existing:4 savage:1 yet:1 must:1 john:1 remove:1 update:2 greedy:7 selected:1 intelligence:4 wolfram:1 provides:2 mannor:2 c2:1 prove:1 introduce:3 expected:9 planning:3 multi:2 actual:1 armed:2 considering:1 increasing:1 provided:7 u... |
4,387 | 4,971 | Improved and Generalized Upper Bounds on
the Complexity of Policy Iteration
Bruno Scherrer
Inria, Villers-l`es-Nancy, F-54600, France
Universit?e de Lorraine, LORIA, UMR 7503, Vandoeuvre-l`es-Nancy, F-54506, France
bruno.scherrer@inria.fr
Abstract
Given a Markov Decision Process (MDP) with n states and m actions per
s... | 4971 |@word version:1 polynomial:8 norm:4 open:3 condon:1 contraction:6 incurs:1 lorraine:1 existing:1 must:1 remove:1 designed:2 update:1 stationary:4 greedy:4 xk:3 core:1 provides:1 math:1 along:2 direct:2 ik:6 prove:1 indeed:4 expected:3 nor:1 bellman:2 discounted:5 decomposed:1 increasing:1 provided:1 notation:3 su... |
4,388 | 4,972 | Efficient Exploration and Value Function
Generalization in Deterministic Systems
Zheng Wen
Stanford University
zhengwen@stanford.edu
Benjamin Van Roy
Stanford University
bvr@stanford.edu
Abstract
We consider the problem of reinforcement learning over episodes of a finitehorizon deterministic system and as a solution ... | 4972 |@word exploitation:4 briefly:2 polynomial:6 c0:2 open:1 q1:2 incurs:1 initial:2 contains:2 selecting:1 daniel:1 denoting:1 past:2 current:2 import:1 must:1 john:4 realize:1 ronald:3 numerical:2 subsequent:3 partition:3 wiewiora:1 designed:2 update:4 greedy:1 selected:2 intelligence:3 short:1 provides:2 math:1 sig... |
4,389 | 4,973 | Aggregating Optimistic Planning Trees for Solving
Markov Decision Processes
Gunnar Kedenburg
INRIA Lille - Nord Europe / idalab GmbH
gunnar.kedenburg@inria.fr
Rapha?l Fonteneau
University of Li?ge / INRIA Lille - Nord Europe
raphael.fonteneau@ulg.ac.be
R?mi Munos
INRIA Lille - Nord Europe / Microsoft Research New Eng... | 4973 |@word middle:2 simulation:1 initial:4 ingersoll:1 series:2 contains:2 denoting:1 past:1 current:2 si:1 attracted:1 realize:1 numerical:5 camacho:1 intelligence:3 generative:1 selected:3 leaf:16 half:1 plane:1 beginning:1 short:1 mgl:1 node:12 teytaud:1 rollout:1 along:1 constructed:4 direct:1 differential:2 sympo... |
4,390 | 4,974 | Online Learning in Episodic Markovian Decision
Processes by Relative Entropy Policy Search
Alexander Zimin
Institute of Science and Technology Austria
alexander.zimin@ist.ac.at
Gergely Neu
INRIA Lille ? Nord Europe
gergely.neu@gmail.com
Abstract
We study the problem of online learning in finite episodic Markov decis... | 4974 |@word version:2 norm:1 dekel:2 open:2 decomposition:1 q1:5 recursively:2 ftrl:2 selecting:1 daniel:2 current:2 com:1 gmail:1 numerical:1 unichain:3 enables:1 drop:2 update:4 bart:2 stationary:7 v:1 selected:2 leaf:1 intelligence:2 warmuth:1 accordingly:1 xk:17 core:1 num:1 provides:2 math:1 mannor:1 simpler:2 mat... |
4,391 | 4,975 | Online Learning in Markov Decision Processes with
Adversarially Chosen Transition Probability
Distributions
Peter L. Bartlett
UC Berkeley and QUT
bartlett@eecs.berkeley.edu
Yasin Abbasi-Yadkori
Queensland University of Technology
yasin.abbasiyadkori@qut.edu.au
Varun Kanade
UC Berkeley
vkanade@eecs.berkeley.edu
Yevgen... | 4975 |@word trial:1 exploitation:1 version:1 polynomial:5 norm:1 stronger:1 seems:1 suitably:1 queensland:2 decomposition:1 covariance:1 pick:1 harder:1 reduction:5 contains:2 selecting:1 interestingly:1 com:1 nt:4 gmail:1 must:2 ronald:1 subsequent:1 update:2 n0:9 v:1 stationary:3 selected:2 warmuth:1 beginning:2 ith:... |
4,392 | 4,976 | Online Learning of Dynamic Parameters
in Social Networks
Shahin Shahrampour 1
Alexander Rakhlin 2
Ali Jadbabaie 1
2
Department of Electrical and Systems Engineering, Department of Statistics
University of Pennsylvania
Philadelphia, PA 19104 USA
1
{shahin,jadbabai}@seas.upenn.edu 2 rakhlin@wharton.upenn.edu
1
Abstract
... | 4976 |@word mild:3 private:5 version:1 norm:1 dekel:1 seek:1 decomposition:8 mention:1 minus:1 tr:10 boundedness:1 reduction:3 initial:1 series:5 selecting:1 denoting:2 interestingly:1 past:1 existing:1 outperforms:1 current:1 comparing:1 si:1 attracted:1 conforming:1 must:2 realistic:1 enables:1 update:12 fund:1 alone... |
4,393 | 4,977 | Modeling Overlapping Communities with
Node Popularities
Prem Gopalan1 , Chong Wang2 , and David M. Blei1
1
Department of Computer Science, Princeton University, {pgopalan,blei}@cs.princeton.edu
2
Machine Learning Department, Carnegie Mellon University, {chongw}@cs.cmu.edu
Abstract
We develop a probabilistic approach ... | 4977 |@word logit:3 twelfth:1 simplifying:1 substitution:1 contains:1 amp:31 outperforms:2 current:1 com:2 comparing:1 lang:1 must:3 written:1 plot:1 update:16 generative:1 prohibitive:1 selected:1 steepest:1 papadopoulos:1 colored:2 blei:6 provides:1 detecting:1 node:113 liberal:1 simpler:2 five:1 mathematical:1 diffe... |
4,394 | 4,978 | A Scalable Approach to Probabilistic Latent Space
Inference of Large-Scale Networks
Junming Yin
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
junmingy@cs.cmu.edu
Qirong Ho
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
qho@cs.cmu.edu
Eric P. Xing
School of Com... | 4978 |@word trial:1 version:1 briefly:1 open:13 closure:1 d2:1 simulation:1 pick:1 dramatic:1 reduction:2 initial:1 configuration:8 contains:1 pub:1 existing:3 comparing:1 si:19 yet:1 must:1 informative:3 kdd:1 shape:1 plot:6 update:13 v:3 stationary:1 generative:4 fewer:1 parameterization:3 core:1 junming:2 ptm:26 ble... |
4,395 | 4,979 | Relevance Topic Model for Unstructured Social
Group Activity Recognition
Fang Zhao
Yongzhen Huang
Liang Wang
Tieniu Tan
Center for Research on Intelligent Perception and Computing
Institute of Automation, Chinese Academy of Sciences
{fang.zhao,yzhuang,wangliang,tnt}@nlpr.ia.ac.cn
Abstract
Unstructured social group ac... | 4979 |@word r:3 tried:1 contrastive:3 tr:13 plentiful:2 wedding:6 series:1 contains:1 document:4 outperforms:3 existing:1 visible:4 partition:1 hofmann:1 enables:1 update:1 discrimination:1 generative:1 accordingly:1 short:1 lr:6 blei:1 detecting:1 provides:1 pascanu:1 toronto:1 firstly:1 height:1 rc:1 direct:3 consist... |
4,396 | 498 | Adaptive Soft Weight Tying
using Gaussian Mixtures
Steven J. Nowlan
Geoffrey E. Hinton
Computational Neuroscience Laboratory
The Salk Institute, P.O . Box 5800
San Diego, CA 92186-5800
Department of Computer Science
. Uni versi ty of Toran to
Toronto, Canada M5S lA4
Abstract
One way of simplifying neural networks ... | 498 |@word version:2 llsed:1 proportion:4 nd:2 simulation:8 simplifying:1 pressure:3 tr:2 minus:1 veigend:3 initial:5 complexit:1 series:5 configuration:2 lapedes:2 usillg:1 current:1 wd:1 nt:1 nowlan:9 lang:2 si:1 must:1 lue:1 oldest:1 ial:1 compo:4 plaut:2 toronto:2 ional:1 five:2 along:1 istical:1 ect:1 consists:1 f... |
4,397 | 4,980 | Streaming Variational Bayes
Tamara Broderick,
Nicholas Boyd, Andre Wibisono, Ashia C. Wilson
University of California, Berkeley
{tab@stat, nickboyd@eecs, wibisono@eecs, ashia@stat}.berkeley.edu
Michael I. Jordan
University of California, Berkeley
jordan@cs.berkeley.edu
Abstract
We present SDA-Bayes, a framework for (... | 4980 |@word version:4 norm:1 seems:1 nd:1 proportionality:1 seek:1 tried:1 dramatic:1 recursively:1 moment:2 initial:1 exclusively:1 score:1 document:30 interestingly:1 fa8750:1 rightmost:1 past:1 current:2 comparing:1 wd:12 assigning:1 must:4 readily:2 reminiscent:1 written:1 plot:2 designed:1 update:17 aside:2 genera... |
4,398 | 4,981 | Scalable Inference for Logistic-Normal Topic Models
Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng and Bo Zhang
State Key Lab of Intelligent Tech. & Systems; Tsinghua National TNList Lab;
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
{chenjf10,wangzi10}@mails.tsinghua.edu.cn;
{dc... | 4981 |@word repository:1 version:3 proportion:3 nd:27 d2:1 confirms:1 simulation:1 vldb:1 covariance:1 p0:3 pg:15 tr:1 tnlist:1 series:1 efficacy:1 contains:3 lichman:1 document:27 existing:5 wd:2 comparing:1 written:1 subsequent:1 additive:1 partition:1 designed:1 intelligence:2 discovering:1 selected:1 leaf:2 rudin:1... |
4,399 | 4,982 | When Are Overcomplete Topic Models Identifiable?
Uniqueness of Tensor Tucker Decompositions
with Structured Sparsity
Daniel Hsu
Columbia University
New York, NY
djhsu@cs.columbia.edu
Animashree Anandkumar
University of California
Irvine, CA
a.anandkumar@uci.edu
Sham Kakade
Microsoft Research
Cambridge, MA
skakade@mi... | 4982 |@word faculty:1 version:10 proportion:4 norm:1 decomposition:20 contraction:1 jafarpour:1 moment:33 daniel:2 document:4 recovered:2 com:1 whp:3 comparing:1 dx:1 john:3 j1:7 informative:1 enables:1 device:1 core:1 blei:1 provides:1 characterization:3 node:10 location:1 successive:2 phylogenetic:1 lathauwer:1 persi... |
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