Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
5,500 | 5,979 | Time-Sensitive Recommendation From
Recurrent User Activities
Nan Du , Yichen Wang , Niao He? , Le Song
College of Computing, Georgia Tech
?
H. Milton Stewart School of Industrial & System Engineering, Georgia Tech
dunan@gatech.edu, yichen.wang@gatech.edu, nhe6@gatech.edu
lsong@cc.gatech.edu
Abstract
By making pe... | 5979 |@word mild:1 cox:3 middle:1 norm:9 nd:1 d2:1 seek:1 simulation:1 invoking:1 weekday:1 pick:1 pressed:1 kz1:3 ld:1 moment:5 liu:1 contains:1 tuned:1 document:1 interestingly:1 past:7 existing:3 current:1 contextual:1 z2:13 ka:4 comparing:1 com:3 vere:1 readily:2 numerical:1 kdd:4 analytic:2 cheap:2 zaid:1 designed... |
5,501 | 598 | A Hybrid Neural Net System for
State-of-the-Art Continuous Speech Recognition
Y. Zhao
BBN Systems and Technologies
Cambridge, MA 02138
G. Zavaliagkos
Northeastern University
Boston MA 02115
R. Schwartz
BBN Systems and Technologies
Cambridge, MA 02138
J. Makhoul
BBN Systems and Technologies
Cambridge, MA 02138
Abstr... | 598 |@word version:2 nd:1 gish:1 covariance:1 mention:3 reduction:2 initial:2 score:25 selecting:3 transfonn:3 current:1 elliptical:3 si:1 yet:2 speakerindependent:1 designed:1 alone:2 tenn:2 selected:1 ebf:10 core:1 provides:2 rescoring:7 bmt:1 toronto:1 sigmoidal:1 five:3 constructed:1 incorrect:3 combine:2 roughly:1... |
5,502 | 5,980 | Parallel Recursive Best-First AND/OR Search for
Exact MAP Inference in Graphical Models
Akihiro Kishimoto
IBM Research, Ireland
Radu Marinescu
IBM Research, Ireland
Adi Botea
IBM Research, Ireland
akihirok@ie.ibm.com
radu.marinescu@ie.ibm.com
adibotea@ie.ibm.com
Abstract
The paper presents and evaluates the powe... | 5980 |@word version:1 open:1 korf:1 decomposition:2 concise:1 recursively:2 contains:1 hereafter:1 selecting:1 genetic:2 icga:2 current:6 com:3 yet:3 dechter:4 numerical:1 partition:1 enables:1 hypothesize:1 plot:2 update:4 v:2 intelligence:12 selected:1 core:5 andbound:1 record:3 pointer:1 completeness:1 boosting:1 no... |
5,503 | 5,981 | Bounding the Cost of Search-Based Lifted Inference
Vibhav Gogate
University of Texas At Dallas
800 W Campbell Rd, Richardson, TX 75080
vibhav.gogate@utdallas.edu
David Smith
University of Texas At Dallas
800 W Campbell Rd, Richardson, TX 75080
dbs014200@utdallas.edu
Abstract
Recently, there has been growing interest... | 5981 |@word cu:1 version:1 pw:1 polynomial:6 closure:1 decomposition:15 citeseer:2 recursively:1 functor:3 reduction:4 initial:2 substitution:6 contains:8 daniel:1 denoting:1 fa8750:1 current:4 com:1 must:8 dechter:2 partition:17 christian:1 update:1 v:12 intelligence:10 leaf:17 fewer:1 nq:3 selected:1 mln:9 braz:1 xk:... |
5,504 | 5,982 | Efficient Learning by Directed Acyclic Graph For
Resource Constrained Prediction
Joseph Wang
Department of Electrical
& Computer Engineering
Boston University,
Boston, MA 02215
joewang@bu.edu
Kirill Trapeznikov
Systems & Technology Research
Woburn, MA 01801
kirill.trapeznikov@
stresearch.com
Venkatesh Saligrama
Depar... | 5982 |@word polynomial:4 nd:2 km:1 ld:6 reduction:2 initial:1 series:3 contains:1 document:1 outperforms:1 existing:1 current:6 com:1 comparing:1 beygelzimer:2 yet:1 informative:1 enables:1 cheap:1 remove:2 update:1 v:4 greedy:7 leaf:8 generative:1 selected:4 intelligence:4 bolukbasi:1 provides:1 iterates:1 node:50 zha... |
5,505 | 5,983 | Estimating Jaccard Index with Missing Observations:
A Matrix Calibration Approach
Wenye Li
Macao Polytechnic Institute
Macao SAR, China
wyli@ipm.edu.mo
Abstract
The Jaccard index is a standard statistics for comparing the pairwise similarity between data samples. This paper investigates the problem of estimating a Ja... | 5983 |@word norm:6 duda:1 rajaraman:1 seek:1 tried:1 decomposition:2 covariance:1 commute:2 tr:1 ipm:1 initial:5 series:2 pub:1 existing:1 comparing:1 yet:1 hoboken:1 john:3 numerical:1 designed:1 fund:1 implying:1 prohibitive:1 iterates:1 toronto:1 successive:1 firstly:3 mathematical:1 c2:3 symposium:1 polyhedral:1 th... |
5,506 | 5,984 | Sample Efficient Path Integral Control under
Uncertainty
Yunpeng Pan, Evangelos A. Theodorou, and Michail Kontitsis
Autonomous Control and Decision Systems Laboratory
Institute for Robotics and Intelligent Machines
School of Aerospace Engineering
Georgia Institute of Technology, Atlanta, GA 30332
{ypan37,evangelos.the... | 5984 |@word trial:26 briefly:1 seek:2 linearized:2 propagate:1 covariance:5 tr:5 kappen:5 moment:2 inefficiency:1 series:1 initial:1 rkhs:1 ours:2 outperforms:1 existing:5 current:2 optim:1 si:1 dx:3 bd:5 additive:1 numerical:2 analytic:5 sdes:2 enables:2 update:9 intelligence:2 parameterization:5 samplingbased:1 recor... |
5,507 | 5,985 | Efficient Thompson Sampling for Online
Matrix-Factorization Recommendation
Jaya Kawale, Hung Bui, Branislav Kveton
Adobe Research
San Jose, CA
{kawale, hubui, kveton}@adobe.com
Long Tran Thanh
University of Southampton
Southampton, UK
ltt08r@ecs.soton.ac.uk
Sanjay Chawla
Qatar Computing Research Institute, Qatar
Univ... | 5985 |@word exploitation:3 version:2 norm:1 nd:1 d2:1 crucially:1 covariance:1 p0:2 sgd:7 initial:3 qatar:2 series:1 renewed:1 outperforms:1 existing:3 freitas:1 current:7 com:3 contextual:2 wd:3 discretization:1 recovered:1 michal:2 subsequent:1 realistic:1 shape:1 designed:3 update:16 resampling:1 stationary:1 genera... |
5,508 | 5,986 | Parallelizing MCMC with Random Partition Trees
Xiangyu Wang
Dept. of Statistical Science
Duke University
xw56@stat.duke.edu
Fangjian Guo
Dept. of Computer Science
Duke University
guo@cs.duke.edu
Katherine A. Heller
Dept. of Statistical Science
Duke University
kheller@stat.duke.edu
David B. Dunson
Dept. of Statistic... | 5986 |@word trial:1 illustrating:1 repository:1 seems:1 c0:7 unif:1 covariance:1 forestry:1 solid:2 recursively:2 reduction:2 inefficiency:1 contains:1 liu:2 lichman:1 series:1 existing:3 com:2 manuel:1 written:1 ronald:1 numerical:1 partition:33 happen:1 visible:1 shape:1 plot:2 update:1 resampling:6 half:1 selected:1... |
5,509 | 5,987 | Fast Lifted MAP Inference via Partitioning
Somdeb Sarkhel
The University of Texas at Dallas
Parag Singla
I.I.T. Delhi
Vibhav Gogate
The University of Texas at Dallas
Abstract
Recently, there has been growing interest in lifting MAP inference algorithms for
Markov logic networks (MLNs). A key advantage of these lift... | 5987 |@word version:1 polynomial:2 open:1 heuristically:4 d2:1 closure:2 vldb:1 bn:1 decomposition:4 reduction:2 inefficiency:1 contains:2 sherali:1 bootstrapped:1 fa8750:1 existing:4 z2:2 yet:1 assigning:1 dechter:1 refines:2 partition:67 drop:1 treating:1 update:3 plot:2 v:3 greedy:2 intelligence:13 yr:4 braz:1 mln:5... |
5,510 | 5,988 | Active Learning from Weak and Strong Labelers
Chicheng Zhang
UC San Diego
chichengzhang@ucsd.edu
Kamalika Chaudhuri
UC San Diego
kamalika@eng.ucsd.edu
Abstract
An active learner is given a hypothesis class, a large set of unlabeled examples and
the ability to interactively query labels to an oracle of a subset of th... | 5988 |@word middle:1 pw:2 dekel:1 tedious:1 open:1 eng:1 sgd:1 initial:2 contains:1 ours:5 existing:1 err:3 current:7 com:1 comparing:1 beygelzimer:3 realistic:5 happen:1 cant:2 cheap:1 atlas:1 n0:2 v:1 alone:4 generative:1 accordingly:1 beginning:1 provides:4 coarse:1 math:1 simpler:1 zhang:4 direct:2 incorrect:7 ijcv... |
5,511 | 5,989 | Learnability of Influence in Networks
Harikrishna Narasimhan
David C. Parkes
Yaron Singer
Harvard University, Cambridge, MA 02138
hnarasimhan@seas.harvard.edu, {parkes, yaron}@seas.harvard.edu
Abstract
We show PAC learnability of influence functions for three common influence models, namely, the Linear Threshold (LT)... | 5989 |@word mild:1 private:1 faculty:1 achievable:1 polynomial:18 norm:1 seek:3 crucially:1 pick:1 lakshmanan:1 reduction:3 initial:6 celebrated:1 series:1 contains:3 selecting:1 chervonenkis:1 ours:2 yni:1 existing:2 err:5 current:1 nt:1 surprising:1 manuel:4 activation:4 written:1 luis:1 fn:1 subsequent:1 kdd:4 v:1 i... |
5,512 | 599 | Harmonic Grammars
for Formal Languages
Paul Smolensky
Department of Computer Science &
Institute of Cognitive Science
U ni versity of Colorado
Boulder, Colorado 80309-0430
Abstract
Basic connectionist principles imply that grammars should take the
form of systems of parallel soft constraints defining an optimization
... | 599 |@word cu:3 version:2 twelfth:2 decomposition:1 minus:1 autosegmental:1 recursively:2 contains:1 fragment:3 activation:4 written:1 chicago:2 numerical:2 enables:1 designed:1 half:1 leaf:1 intelligence:3 generative:1 ith:1 core:1 mental:2 node:51 contribute:3 preference:1 complication:1 mathematical:3 c2:6 introduce... |
5,513 | 5,990 | A Pseudo-Euclidean Iteration for Optimal Recovery
in Noisy ICA
James Voss
The Ohio State University
vossj@cse.ohio-state.edu
Mikhail Belkin
The Ohio State University
mbelkin@cse.ohio-state.edu
Luis Rademacher
The Ohio State University
lrademac@cse.ohio-state.edu
Abstract
Independent Component Analysis (ICA) is a po... | 5990 |@word mild:1 norm:1 hyv:3 hu:3 covariance:8 decomposition:12 versatile:1 carry:1 contains:1 outperforms:2 existing:4 recovered:13 ka:1 si:1 reminiscent:2 luis:1 john:1 numerical:1 additive:10 predetermined:1 designed:5 treating:1 update:5 maxv:1 fewer:2 vanishing:1 deflationary:2 provides:2 cse:3 bopt:5 accessed:... |
5,514 | 5,991 | Differentially Private Subspace Clustering
Yining Wang, Yu-Xiang Wang and Aarti Singh
Machine Learning Department, Carnegie Mellon Universty, Pittsburgh, USA
{yiningwa,yuxiangw,aarti}@cs.cmu.edu
Abstract
Subspace clustering is an unsupervised learning problem that aims at grouping
data points into multiple ?clusters?... | 5991 |@word trial:2 private:34 version:4 polynomial:1 norm:7 seems:1 open:1 d2:5 seek:1 simulation:2 covariance:3 jacob:1 tr:1 liu:1 ours:1 past:1 existing:1 outperforms:1 recovered:1 bradley:1 protection:1 si:9 written:2 numerical:2 plot:4 update:4 n0:1 mitrokotsa:1 generative:3 intelligence:4 guess:1 plane:5 smith:2 ... |
5,515 | 5,992 | Compressive spectral embedding: sidestepping the
SVD
Upamanyu Madhow
madhow@ece.ucsb.edu
ECE Department, UC Santa Barbara
Dinesh Ramasamy
dineshr@ece.ucsb.edu
ECE Department, UC Santa Barbara
Abstract
Spectral embedding based on the Singular Value Decomposition (SVD) is a
widely used ?preprocessing? step in many lear... | 5992 |@word briefly:1 version:1 polynomial:24 norm:13 decomposition:4 commute:4 mention:1 nystr:5 ld:1 reduction:7 celebrated:1 eigensolvers:2 efficacy:1 score:1 document:1 ours:2 suppressing:1 comparing:2 dx:9 written:1 must:1 nanoscale:1 numerical:5 subsequent:1 additive:1 kdd:1 designed:1 plot:2 drop:1 sponsored:1 i... |
5,516 | 5,993 | Generalization in Adaptive Data Analysis and
Holdout Reuse?
Cynthia Dwork
Microsoft Research
Toniann Pitassi
University of Toronto
Vitaly Feldman
IBM Almaden Research Center?
Omer Reingold
Samsung Research America
Moritz Hardt
Google Research
Aaron Roth
University of Pennsylvania
Abstract
Overfitting is the bane of... | 5993 |@word economically:1 private:16 version:4 achievable:1 stronger:3 reused:5 simulation:1 crucially:1 elisseeff:1 pick:5 necessity:1 plentiful:1 series:1 tuned:1 ka:1 yet:1 john:1 subsequent:3 happen:2 partition:1 predetermined:1 enables:1 designed:1 plot:1 n0:1 selected:7 smith:2 short:3 provides:2 boosting:1 toro... |
5,517 | 5,994 | Online F-Measure Optimization
R?obert Busa-Fekete
Department of Computer Science
University of Paderborn, Germany
busarobi@upb.de
Bal?azs Sz?or?enyi
Technion, Haifa, Israel /
MTA-SZTE Research Group on
Artificial Intelligence, Hungary
szorenyibalazs@gmail.com
?
Krzysztof Dembczynski
Institute of Computing Science
Poz... | 5994 |@word mild:1 repository:1 version:2 seems:2 stronger:2 r:1 decomposition:1 score:23 efficacy:1 tuned:2 ours:1 interestingly:1 current:5 com:1 gmail:1 yet:2 written:5 intriguing:1 treating:1 plot:1 progressively:1 update:4 aside:1 farkas:1 intelligence:1 vanishing:1 short:1 num:4 provides:2 math:1 ofo:24 kdda:2 dn... |
5,518 | 5,995 | A Market Framework for Eliciting Private Data
Bo Waggoner
Harvard SEAS
bwaggoner@fas.harvard.edu
Rafael Frongillo
University of Colorado
raf@colorado.edu
Jacob Abernethy
University of Michigan
jabernet@umich.edu
Abstract
We propose a mechanism for purchasing information from a sequence of participants. The particip... | 5995 |@word private:16 version:3 polynomial:1 norm:2 proportion:1 jacob:4 covariance:3 paid:1 profit:6 minus:1 initial:2 rkhs:7 rightmost:1 past:1 existing:1 current:6 si:1 yet:1 dx:1 must:4 partition:1 dive:1 designed:1 treating:1 update:16 intelligence:2 prohibitive:1 parameterization:1 provides:1 authority:2 contrib... |
5,519 | 5,996 | Optimal Ridge Detection using Coverage Risk
Christopher R. Genovese
Department of Statistics
Carnegie Mellon University
genovese@stat.cmu.edu
Yen-Chi Chen
Department of Statistics
Carnegie Mellon University
yenchic@andrew.cmu.edu
Larry Wasserman
Department of Statistics
Carnegie Mellon University
larry@stat.cmu.edu
... | 5996 |@word mild:1 version:1 middle:4 norm:2 urb:1 simulation:5 bn:19 decomposition:1 pick:1 solid:1 contains:2 selecting:1 ours:1 document:1 dx:2 john:1 mesh:3 fn:9 evans:1 remove:1 plot:3 half:1 selected:2 intelligence:1 provides:3 recompute:1 draft:1 location:1 revisited:1 centerline:1 along:1 h4:2 symposium:1 prove... |
5,520 | 5,997 | Fast Distributed k-Center Clustering with Outliers on
Massive Data
Gustavo Malkomes, Matt J. Kusner, Wenlin Chen
Department of Computer Science and Engineering
Washington University in St. Louis
St. Louis, MO 63130
{luizgustavo,mkusner,wenlinchen}@wustl.edu
Kilian Q. Weinberger
Department of Computer Science
Cornell Un... | 5997 |@word version:2 open:1 km:4 seek:1 rgb:1 eng:1 bahmani:2 reduction:3 contains:2 uncovered:1 selecting:1 document:2 outperforms:5 mishra:1 yet:1 dx:13 must:12 sergei:2 partition:6 kdd:2 remove:2 plot:2 maxv:2 v:1 greedy:5 selected:2 guess:1 xk:1 pvldb:2 core:1 dysphonia:1 contribute:1 node:1 location:1 five:2 alon... |
5,521 | 5,998 | Orthogonal NMF through Subspace Exploration
Megasthenis Asteris
The University of Texas at Austin
megas@utexas.edu
Dimitris Papailiopoulos
University of California, Berkeley
dimitrisp@berkeley.edu
Alexandros G. Dimakis
The University of Texas at Austin
dimakis@austin.utexas.edu
Abstract
Orthogonal Nonnegative Matri... | 5998 |@word trial:2 repository:2 version:2 polynomial:8 norm:1 compression:2 nd:1 termination:2 underperform:1 km:11 seek:5 decomposition:2 covariance:1 accommodate:1 configuration:1 contains:3 liu:1 lichman:2 daniel:1 document:4 outperforms:4 existing:7 current:1 discretization:1 assigning:1 must:3 readily:3 john:1 st... |
5,522 | 5,999 | Fast Classification Rates for High-dimensional
Gaussian Generative Models
Tianyang Li
Adarsh Prasad
Department of Computer Science, UT Austin
{lty,adarsh,pradeepr}@cs.utexas.edu
Pradeep Ravikumar
Abstract
We consider the problem of binary classification when the covariates conditioned
on the each of the response val... | 5999 |@word trial:1 version:2 norm:2 c0:4 seek:1 prasad:1 simulation:2 covariance:12 hsieh:1 delgado:1 liblinear:1 liu:6 series:3 ecole:1 existing:1 manuel:1 yet:1 chu:1 written:2 glm2:1 discrimination:1 v:1 generative:25 jiashun:2 caveat:2 simpler:1 zhang:1 mathematical:1 along:1 c2:2 direct:3 initiative:1 yuan:1 barr... |
5,523 | 6 | 775
A NEURAL-NETWORK SOLUTION TO THE CONCENTRATOR
ASSIGNNlENT PROBLEM
Gene A. Tagliarini
Edward W. Page
Department of Computer Science, Clemson University, Clemson, SC
29634-1906
ABSTRACT
Networks of simple analog processors having neuron-like properties have
been employed to compute good solutions to a variety of op... | 6 |@word soc:1 implemented:1 effect:2 met:1 assigned:5 arrangement:1 symmetric:2 quantity:1 integrative:1 tji:1 simulation:6 illustrated:4 added:1 diagonal:2 link:2 mapped:2 simulated:3 capacity:10 assign:1 sci:3 evaluate:1 complete:1 summation:1 yij:1 l1:1 current:4 modeled:3 hall:1 relationship:1 normal:3 assigning:3... |
5,524 | 60 | 278
THE HOPFIELD MODEL WITH MULTI-LEVEL NEURONS
Michael Fleisher
Department of Electrical Engineering
Technion - Israel Institute of Technology
Haifa 32000, Israel
ABSTRACT
The Hopfield neural network. model for associative memory is generalized. The generalization
replaces two state neurons by neurons taking a ric... | 60 |@word graded:1 private:1 middle:1 evolution:1 concentrate:1 objective:1 question:1 symmetric:3 correct:1 quantity:1 enm:2 added:1 diagonal:4 sgn:3 exhibit:1 recurrence:2 boundedness:3 mapped:1 coincides:1 moment:1 sci:2 generalized:2 contains:1 generalization:5 capacity:12 outer:4 me:1 denoting:1 tn:2 strictly:2 l1... |
5,525 | 600 | A Neural Network that Learns to Interpret
Myocardial Planar Thallium Scintigrams
Charles Rosenberg, Ph.D:
Jacob Erel, M.D.
Department of Computer Science
Hebrew University
Jerusalem, Israel
Department of Cardiology
Sapir Medical Center
Meir General Hospital
Kfar Saba, Israel
Henri Atlan, M.D., PhD.
Department of B... | 600 |@word mild:6 proportion:1 jacob:1 eng:1 thereby:1 initial:1 score:8 karger:1 current:2 anterior:3 cad:3 activation:1 yet:2 must:2 numerical:1 happen:1 christian:1 discrimination:1 v:2 pursued:1 selected:2 spec:1 intelligence:1 inspection:1 oblique:2 record:1 mental:1 detecting:1 ire:2 location:2 ofbackpropagation:... |
5,526 | 6,000 | Predtron: A Family of Online Algorithms for General
Prediction Problems
Prateek Jain
Microsoft Research, INDIA
prajain@microsoft.com
Nagarajan Natarajan
University of Texas at Austin, USA
naga86@cs.utexas.edu
Ambuj Tewari
University of Michigan, Ann Arbor, USA
tewaria@umich.edu
Abstract
Modern prediction problems ar... | 6000 |@word version:3 judgement:2 seems:1 norm:24 stronger:1 nd:1 confirms:1 simulation:3 p0:13 tr:1 minus:1 ld:1 reduction:1 score:20 selecting:1 rkhs:1 ours:2 document:14 existing:4 com:1 comparing:1 jaz:1 yet:1 readily:1 john:1 additive:1 drop:1 designed:1 update:2 plot:2 v:2 intelligence:1 selected:2 ith:2 lr:4 pro... |
5,527 | 6,001 | On the Optimality of Classifier Chain for
Multi-label Classification
Weiwei Liu
Ivor W. Tsang?
Centre for Quantum Computation and Intelligent Systems
University of Technology, Sydney
liuweiwei863@gmail.com, ivor.tsang@uts.edu.au
Abstract
To capture the interdependencies between labels in multi-label classification pro... | 6001 |@word pcc:4 norm:1 nd:10 hu:1 hsieh:1 liblinear:2 initial:1 liu:2 contains:2 selecting:1 daniel:1 document:2 outperforms:3 com:1 nt:5 luo:1 si:5 gmail:1 attracted:1 written:2 must:4 john:4 greedy:13 half:1 metabolism:1 selected:1 website:1 intelligence:4 ith:1 reciprocal:2 provides:1 boosting:1 cse:1 toronto:1 fi... |
5,528 | 6,002 | Smooth Interactive Submodular Set Cover
Yisong Yue
California Institute of Technology
yyue@caltech.edu
Bryan He
Stanford University
bryanhe@stanford.edu
Abstract
Interactive submodular set cover is an interactive variant of submodular set cover
over a hypothesis class of submodular functions, where the goal is to sat... | 6002 |@word exploitation:4 middle:1 version:33 laurence:2 termination:2 simulation:3 r:1 prasad:1 rivera:1 reduction:4 liu:1 score:1 efficacy:1 daniel:1 document:1 current:1 comparing:1 contextual:2 manuel:1 must:9 additive:1 kdd:5 n0:1 v:2 greedy:7 instantiate:2 selected:1 item:4 discovering:1 intelligence:2 payman:1 ... |
5,529 | 6,003 | Tractable Bayesian Network Structure Learning with
Bounded Vertex Cover Number
Janne H. Korhonen
Helsinki Institute for Information Technology HIIT
Department of Computer Science
University of Helsinki
janne.h.korhonen@helsinki.fi
Pekka Parviainen
Helsinki Institute for Information Technology HIIT
Department of Compute... | 6003 |@word cu:4 version:1 polynomial:19 seems:1 nd:1 open:1 crucially:1 bn:1 decomposition:5 harder:1 reduction:3 necessity:1 liu:1 contains:3 score:24 selecting:1 ilps:2 current:1 invitation:1 must:4 partition:1 implying:3 intelligence:12 leaf:1 selected:2 malone:1 amir:1 plane:1 core:11 provides:1 node:43 contribute... |
5,530 | 6,004 | Secure Multi-party Differential Privacy
Peter Kairouz1
Sewoong Oh2
Pramod Viswanath1
1
Department of Electrical & Computer Engineering
2
Department of Industrial & Enterprise Systems Engineering
University of Illinois Urbana-Champaign
Urbana, IL 61801, USA
{kairouz2,swoh,pramodv}@illinois.edu
Abstract
We study the pro... | 6004 |@word private:26 version:4 eliminating:1 stronger:2 norm:1 nd:1 suitably:1 prasad:1 eng:1 dishonest:1 series:1 denoting:1 existing:2 recovered:1 protection:2 surprising:2 written:1 john:1 wx:4 guess:1 amir:1 xk:3 smith:2 record:4 mental:1 provides:2 characterization:1 lending:1 node:1 completeness:2 attack:3 kair... |
5,531 | 6,005 | Adaptive Stochastic Optimization: From Sets to Paths
Zhan Wei Lim
David Hsu
Wee Sun Lee
Department of Computer Science, National University of Singapore
{limzhanw,dyhsu,leews}@comp.nus.edu.sg
Abstract
Adaptive stochastic optimization (ASO) optimizes an objective function adaptively under uncertainty. It plays a crucia... | 6005 |@word trial:3 version:14 polynomial:8 laurence:1 termination:1 grey:1 simulation:2 seek:1 p0:5 pg:1 pick:3 reduction:14 contains:1 selecting:2 daniel:2 o2:5 steiner:1 current:2 rish:1 beygelzimer:1 pothesis:1 must:2 subsequent:1 partition:3 informative:8 plot:2 v:2 greedy:6 selected:3 half:4 item:11 intelligence:... |
5,532 | 6,006 | Learning Structured Densities via Infinite
Dimensional Exponential Families
Mladen Kolar
University of Chicago
mkolar@chicagobooth.edu
Siqi Sun
TTI Chicago
siqi.sun@ttic.edu
Jinbo Xu
TTI Chicago
jinbo.xu@gmail.com
Abstract
Learning the structure of a probabilistic graphical models is a well studied problem in the m... | 6006 |@word mild:3 faculty:1 polynomial:1 norm:12 d2:1 hyv:3 simulation:4 covariance:1 p0:16 tr:1 liu:4 series:1 score:18 rkhs:7 nonparanormal:6 outperforms:1 existing:3 current:1 jinbo:2 com:1 gmail:1 dx:8 written:1 chicago:5 partition:1 additive:3 designed:1 drop:1 fund:1 v:1 intelligence:2 mccallum:1 fpr:1 provides:... |
5,533 | 6,007 | Lifelong Learning with Non-i.i.d. Tasks
Christoph H. Lampert
IST Austria
Klosterneuburg, Austria
chl@ist.ac.at
Anastasia Pentina
IST Austria
Klosterneuburg, Austria
apentina@ist.ac.at
Abstract
In this work we aim at extending the theoretical foundations of lifelong learning.
Previous work analyzing this scenario is ... | 6007 |@word multitask:1 advantageous:1 paredes:1 thereby:1 franois:1 contains:4 series:1 daniel:1 romera:1 past:1 existing:3 current:1 comparing:1 z2:1 worsening:1 si:11 yet:4 liva:1 john:3 subsequent:5 additive:1 stationary:3 half:1 intelligence:1 isotropic:1 provides:3 direct:1 become:1 learing:3 prove:4 consists:3 s... |
5,534 | 6,008 | Algorithms with Logarithmic or Sublinear Regret for
Constrained Contextual Bandits
Huasen Wu
University of California at Davis
hswu@ucdavis.edu
R. Srikant
University of Illinois at Urbana-Champaign
rsrikant@illinois.edu
Xin Liu
University of California at Davis
liu@cs.ucdavis.edu
Chong Jiang
University of Illinois a... | 6008 |@word trial:5 exploitation:4 briefly:1 version:3 seems:1 proportion:1 nd:1 open:3 simulation:1 accounting:1 incurs:1 liu:3 series:1 existing:1 current:3 contextual:42 comparing:3 yet:1 chu:1 attracted:1 numerical:1 partition:2 enables:1 update:1 implying:1 greedy:1 discovering:1 intelligence:3 beginning:2 provide... |
5,535 | 6,009 | From random walks to distances on
unweighted graphs
Tatsunori B. Hashimoto
MIT EECS
thashim@mit.edu
Yi Sun
MIT Mathematics
yisun@mit.edu
Tommi S. Jaakkola
MIT EECS
tommi@mit.edu
Abstract
Large unweighted directed graphs are commonly used to capture relations between entities. A fundamental problem in the analysis o... | 6009 |@word version:1 inversion:1 open:2 calculus:1 accounting:1 q1:1 commute:4 tr:1 catastrophically:1 carry:1 ours:2 outperforms:2 past:1 recovered:1 surprising:1 dx:2 kdd:2 analytic:1 designed:1 stationary:6 generative:2 implying:1 fewer:1 intelligence:2 isotropic:1 xk:3 smith:1 short:1 characterization:2 provides:1... |
5,536 | 601 | Learning Curves, Model Selection and
Complexity of Neural Networks
Noboru Murata
Department of IVIathematical Engineering and Information Physics
University of Tokyo, Tokyo 113, JAPAN
E-mail: mura~sat.t.u-tokyo.ac.jp
Shuji Yoshizawa
Dept. Mech. Info.
University of Tokyo
ShUll-ichi Amari
Dept. Math. Eng. and Info. Phy... | 601 |@word nd:1 eng:1 covariance:1 solid:2 initial:1 selecting:1 ka:1 import:1 written:1 afl:1 realize:1 j1:1 ial:1 provides:2 math:1 ional:1 quantit:1 direct:1 ect:1 calculable:1 prove:1 vad:1 ra:1 expected:1 roughly:1 behavior:1 dist:2 mechanic:1 multi:4 ol:1 td:1 automatically:1 actual:1 moreover:2 what:1 minimizes:... |
5,537 | 6,010 | Robust Regression via Hard Thresholding
Kush Bhatia? , Prateek Jain? , and Purushottam Kar??
?
Microsoft Research, India
?
Indian Institute of Technology Kanpur, India
{t-kushb,prajain}@microsoft.com, purushot@cse.iitk.ac.in
Abstract
We study the problem of Robust Least Squares Regression (RLSR) where several
respons... | 6010 |@word mild:2 version:1 norm:1 stronger:1 seems:1 r:4 simulation:1 covariance:1 accommodate:1 necessity:1 selecting:1 offering:1 tuned:1 amp:1 existing:7 current:11 com:1 surprising:1 protection:1 written:1 readily:2 john:3 additive:1 subsequent:1 numerical:1 cheap:1 plot:5 update:16 depict:1 v:1 alone:1 generativ... |
5,538 | 6,011 | Column Selection via Adaptive Sampling
Saurabh Paul
Global Risk Sciences, Paypal Inc.
saupaul@paypal.com
Malik Magdon-Ismail
CS Dept., Rensselaer Polytechnic Institute
magdon@cs.rpi.edu
Petros Drineas
CS Dept., Rensselaer Polytechnic Institute
drinep@cs.rpi.edu
Abstract
Selecting a good column (or row) subset of ma... | 6011 |@word norm:4 stronger:1 c0:17 confirms:1 tried:1 decomposition:5 invoking:2 tr:1 carry:2 moment:1 reduction:1 initial:4 contains:2 score:5 selecting:2 series:1 document:2 outperforms:2 existing:4 ka:47 com:1 current:1 comparing:4 surprising:1 rpi:3 additive:11 informative:1 remove:2 plot:2 selected:9 directory:1 ... |
5,539 | 6,012 | Multi-class SVMs: From Tighter Data-Dependent
Generalization Bounds to Novel Algorithms
? un
? Dogan
Ur
Microsoft Research
Cambridge CB1 2FB, UK
udogan@microsoft.com
Yunwen Lei
Department of Mathematics
City University of Hong Kong
yunwelei@cityu.edu.hk
Alexander Binder
ISTD Pillar
Singapore University of Technology ... | 6012 |@word mild:3 kong:1 polynomial:1 norm:26 pillar:1 dekel:1 hu:1 r:5 crucially:1 hsieh:1 tr:1 contains:2 document:1 past:1 existing:3 outperforms:3 current:1 com:1 guadarrama:1 beygelzimer:1 lang:1 yet:2 additive:1 hofmann:1 analytic:2 update:3 intelligence:1 oneto:1 boosting:1 zhang:2 rc:2 consists:1 manner:1 intr... |
5,540 | 6,013 | Optimal Linear Estimation under Unknown
Nonlinear Transform
Xinyang Yi
The University of Texas at Austin
yixy@utexas.edu
Zhaoran Wang
Princeton University
zhaoran@princeton.edu
Constantine Caramanis
The University of Texas at Austin
constantine@utexas.edu
Han Liu
Princeton University
hanliu@princeton.edu
Abstract
L... | 6013 |@word mild:4 msr:1 version:2 polynomial:4 achievable:1 open:1 seek:1 simulation:3 covariance:2 decomposition:1 thereby:1 tr:1 reduction:1 moment:21 liu:1 initial:2 hereafter:1 xinyang:1 existing:1 yet:1 perror:1 attracted:1 written:1 numerical:2 enables:2 lr:7 quantized:4 simpler:1 zhang:1 along:2 constructed:1 c... |
5,541 | 6,014 | Risk-Sensitive and Robust Decision-Making:
a CVaR Optimization Approach
Yinlam Chow
Stanford University
ychow@stanford.edu
Aviv Tamar
UC Berkeley
avivt@berkeley.edu
Shie Mannor
Technion
shie@ee.technion.ac.il
Marco Pavone
Stanford University
pavone@stanford.edu
Abstract
In this paper we address the problem of deci... | 6014 |@word trial:1 polynomial:1 instrumental:1 c0:10 yv0:1 simulation:2 contraction:6 p0:1 decomposition:5 recursively:2 initial:11 uncovered:1 selecting:2 daniel:1 yvt:3 rightmost:1 readily:2 john:1 stemming:1 numerical:3 happen:2 analytic:1 kv1:1 plot:3 update:3 fund:1 v:1 stationary:3 greedy:1 guess:3 parameterizat... |
5,542 | 6,015 | Learning with Incremental Iterative Regularization
Lorenzo Rosasco
DIBRIS, Univ. Genova, ITALY
LCSL, IIT & MIT, USA
lrosasco@mit.edu
Silvia Villa
LCSL, IIT & MIT, USA
Silvia.Villa@iit.it
Abstract
Within a statistical learning setting, we propose and study an iterative regularization algorithm for least squares define... | 6015 |@word h:1 kgk:10 repository:1 version:3 trial:1 norm:3 stronger:2 termination:1 closure:2 decomposition:3 attainable:3 pick:1 mention:2 t2n:2 series:1 rkhs:5 interestingly:2 err:2 recovered:2 ka:2 nt:2 optim:2 scovel:1 written:1 plot:2 juditsky:1 lr:2 iterates:6 boosting:3 provides:1 toronto:1 math:2 allerton:1 z... |
5,543 | 6,016 | No-Regret Learning in Bayesian Games
Vasilis Syrgkanis
Microsoft Research
New York, NY
vasy@microsoft.com
Jason Hartline
Northwestern University
Evanston, IL
hartline@northwestern.edu
? Tardos
Eva
Cornell University
Ithaca, NY
eva@cs.cornell.edu
Abstract
Recent price-of-anarchy analyses of games of complete informa... | 6016 |@word private:3 version:3 seems:1 stronger:1 pick:1 thereby:2 minus:1 inefficiency:2 mag:6 denoting:1 com:1 comparing:1 si:9 must:1 fn:2 subsequent:2 analytic:1 sponsored:1 ligett:1 bart:1 implying:1 greedy:2 congestion:1 selected:2 item:2 vanishing:4 filtered:1 coarse:31 provides:1 location:1 five:1 differential... |
5,544 | 6,017 | Sparse and Low-Rank Tensor Decomposition
Parikshit Shah
parikshit@yahoo-inc.com
Nikhil Rao
nikhilr@cs.utexas.edu
Gongguo Tang
gtang@mines.edu
Abstract
Motivated by the problem of robust factorization of a low-rank tensor, we study
the question of sparse and low-rank tensor decomposition. We present an efficient
com... | 6017 |@word mild:1 trial:2 version:1 norm:6 stronger:2 suitably:1 mith:1 tensorial:1 km:9 decomposition:47 contraction:30 moment:2 reduction:1 document:4 renewed:1 xand:1 com:1 recovered:4 current:1 si:2 must:4 numerical:3 subsequent:1 happen:1 plot:1 discrimination:1 greedy:2 nent:2 ith:2 compo:2 org:2 hah:3 empala:1 ... |
5,545 | 6,018 | Analysis of Robust PCA via Local Incoherence
Huishuai Zhang
Department of EECS
Syracuse University
Syracuse, NY 13244
hzhan23@syr.edu
Yi Zhou
Department of EECS
Syracuse University
Syracuse, NY 13244
yzhou35@syr.edu
Yingbin Liang
Department of EECS
Syracuse University
Syracuse, NY 13244
yliang06@syr.edu
Abstract
We ... | 6018 |@word trial:6 norm:22 proportion:1 c0:7 confirms:1 crucially:1 decomposition:10 klk:1 necessity:2 score:1 zij:1 ksk1:1 recovered:3 od:4 pcp:38 numerical:4 plot:7 v:2 selected:1 fewer:1 accordingly:1 record:1 characterization:3 provides:7 certificate:9 location:6 tahoe:1 zhang:2 five:1 constructed:1 symposium:1 qi... |
5,546 | 6,019 | Algorithmic Stability and Uniform Generalization
Ibrahim Alabdulmohsin
King Abdullah University of Science and Technology
Thuwal 23955, Saudi Arabia
ibrahim.alabdulmohsin@kaust.edu.sa
Abstract
One of the central questions in statistical learning theory is to determine the conditions under which agents can learn from ... | 6019 |@word trial:3 version:1 stronger:2 elisseeff:2 pick:1 mention:2 reduction:3 substitution:1 contains:2 series:4 chervonenkis:2 past:1 current:1 z2:2 intriguing:1 must:2 readily:1 partition:1 informative:1 alone:2 intelligence:1 warmuth:1 es:7 provides:2 mannor:1 ron:2 mathematical:2 direct:1 qualitative:1 prove:4 ... |
5,547 | 602 | Unsmearing Vistlal Motion:
Development of Long-Range
Horizolltal Intrinsic Conllections
Kevin E. Martin
Jonathan A. Marshall
Department of Computer Science, CB 3175, Sitterson Hall
University of North Carolina, Chapel Hill, NC 27599-3175, U.S.A.
Abstract
Human VlSlon systems integrate information nonlocally, across ... | 602 |@word neurophysiology:3 faculty:1 briefly:1 wiesel:3 horizonta:1 simulation:5 carolina:1 propagate:1 thereby:1 shading:1 initial:6 contains:1 nonlocally:3 tuned:2 coactive:2 activation:9 yet:2 profusion:4 reminiscent:1 subsequent:2 blur:1 shape:1 hypothesize:1 asymptote:1 progressively:1 stationary:3 alone:1 postn... |
5,548 | 6,020 | Mixing Time Estimation in Reversible Markov
Chains from a Single Sample Path
Daniel Hsu
Columbia University
Aryeh Kontorovich
Ben-Gurion University
Csaba Szepesv?ari
University of Alberta
djhsu@cs.columbia.edu
karyeh@cs.bgu.ac.il
szepesva@cs.ualberta.ca
Abstract
This article provides the first procedure for comp... | 6020 |@word version:2 inversion:2 achievable:2 norm:3 stronger:1 km:1 d2:2 pick:2 initial:6 celebrated:1 contains:1 liu:1 series:1 daniel:1 current:1 scovel:1 beygelzimer:1 must:2 additive:3 gurion:1 weyl:1 stationary:20 assurance:1 device:1 trapping:1 smith:3 short:2 paulin:2 provides:2 math:1 along:1 constructed:1 ar... |
5,549 | 6,021 | Efficient Compressive Phase Retrieval
with Constrained Sensing Vectors
Sohail Bahmani, Justin Romberg
School of Electrical and Computer Engineering.
Georgia Institute of Technology
Atlanta, GA 30332
{sohail.bahmani,jrom}@ece.gatech.edu
Abstract
We propose a robust and efficient approach to the problem of compressive ... | 6021 |@word trial:2 version:1 polynomial:1 norm:8 nd:1 c0:1 km:4 simulation:6 decomposition:1 thereby:1 bahmani:2 shechtman:2 selecting:1 ours:1 existing:1 current:1 must:1 ronald:1 additive:1 realistic:1 numerical:6 mordechai:1 christian:1 designed:1 v:2 greedy:2 selected:2 amir:1 provides:1 certificate:1 math:1 aller... |
5,550 | 6,022 | Unified View of Matrix Completion under General
Structural Constraints
Suriya Gunasekar
UT at Austin, USA
suriya@utexas.edu
Arindam Banerjee
UMN Twin Cities, USA
banerjee@cs.umn.edu
Joydeep Ghosh
UT at Austin, USA
ghosh@ece.utexas.edu
Abstract
Matrix completion problems have been widely studied under special low dim... | 6022 |@word briefly:1 norm:79 stronger:1 c0:20 d2:48 covariance:1 decomposition:2 simplifying:1 jacob:1 tr:14 tuned:1 existing:5 readily:1 additive:3 informative:1 unidentifiability:1 warmuth:1 provides:3 characterization:4 complication:1 simpler:1 along:2 c2:5 bd1:2 ik:1 edelman:1 consists:2 specialize:1 prove:1 combi... |
5,551 | 6,023 | Copeland Dueling Bandits
Masrour Zoghi
Informatics Institute
University of Amsterdam, Netherlands
m.zoghi@uva.nl
Zohar Karnin
Yahoo Labs
New York, NY
zkarnin@yahoo-inc.com
Shimon Whiteson
Department of Computer Science
University of Oxford, UK
shimon.whiteson@cs.ox.ac.uk
Maarten de Rijke
Informatics Institute
Univer... | 6023 |@word exploitation:1 faculty:1 version:2 eliminating:1 open:1 seek:2 invoking:1 pick:1 reduction:1 contains:2 score:18 selecting:1 past:1 existing:7 outperforms:2 savage:7 com:1 comparing:4 current:1 contextual:1 freitas:1 must:1 realistic:1 additive:2 numerical:1 informative:1 partition:2 kdd:2 hofmann:2 remove:... |
5,552 | 6,024 | Regret Lower Bound and Optimal Algorithm in
Finite Stochastic Partial Monitoring
Junpei Komiyama
The University of Tokyo
junpei@komiyama.info
Junya Honda
The University of Tokyo
honda@stat.t.u-tokyo.ac.jp
Hiroshi Nakagawa
The University of Tokyo
nakagawa@dl.itc.u-tokyo.ac.jp
Abstract
Partial monitoring is a general... | 6024 |@word exploitation:1 version:1 achievable:1 leighton:1 closure:2 willing:1 simulation:1 bn:1 decomposition:2 mention:1 necessity:2 liu:1 exclusively:1 selecting:2 outperforms:2 existing:3 past:1 current:2 si:18 yet:2 must:1 numerical:2 benign:3 christian:1 plot:1 update:1 bart:9 selected:3 warmuth:1 amir:1 beginn... |
5,553 | 6,025 | Online Learning for Adversaries with Memory:
Price of Past Mistakes
Elad Hazan
Princeton University
New York, USA
ehazan@cs.princeton.edu
Oren Anava
Technion
Haifa, Israel
oanava@tx.technion.ac.il
Shie Mannor
Technion
Haifa, Israel
shie@ee.technion.ac.il
Abstract
The framework of online learning with memory naturall... | 6025 |@word trial:1 compression:1 norm:3 proportion:1 dekel:3 km:4 seek:1 decomposition:4 profit:1 tr:1 series:10 contains:1 denoting:1 past:1 current:4 clements:1 yet:5 dx:1 must:1 john:2 additive:1 enables:1 designed:1 alone:1 generative:1 warmuth:1 short:5 manfred:1 mannor:2 readability:1 simpler:1 along:1 construct... |
5,554 | 6,026 | Revenue Optimization against
Strategic Buyers
Mehryar Mohri
Courant Institute of Mathematical Sciences
251 Mercer Street
New York, NY, 10012
? Medina?
Andr?es Munoz
Google Research
111 8th Avenue
New York, NY, 10011
Abstract
We present a revenue optimization algorithm for posted-price auctions when facing a buyer wit... | 6026 |@word exploitation:3 polynomial:1 achievable:2 leighton:8 seems:1 dekel:1 open:1 seek:7 attainable:1 thereby:1 harder:1 series:1 offering:3 past:1 current:1 comparing:1 contextual:2 surprising:1 discretization:3 must:3 adexchange:6 realistic:3 additive:1 enables:1 sponsored:1 update:1 discrimination:1 selected:4 ... |
5,555 | 6,027 | On Top-k Selection in Multi-Armed Bandits and
Hidden Bipartite Graphs
Wei Cao1
Jian Li1
Yufei Tao2
Zhize Li1
1
Tsinghua University 2 Chinese University of Hong Kong
1
{cao-w13@mails, lijian83@mail, zz-li14@mails}.tsinghua.edu.cn 2 taoyf@cse.cuhk.edu.hk
Abstract
This paper discusses how to efficiently choose from n un... | 6027 |@word kong:1 exploitation:1 version:2 stronger:1 seems:1 open:1 widom:1 crucially:1 kalyanakrishnan:4 paid:1 thereby:1 mention:1 solid:3 carry:1 reduction:9 series:1 selecting:1 outperforms:1 existing:2 yet:1 intriguing:1 must:8 attracted:1 additive:2 designed:1 drop:2 v:1 half:3 selected:1 guess:2 beginning:1 pv... |
5,556 | 6,028 | Improved Iteration Complexity Bounds of Cyclic
Block Coordinate Descent for Convex Problems
Ruoyu Sun?, Mingyi Hong? ?
Abstract
The iteration complexity of the block-coordinate descent (BCD) type algorithm
has been under extensive investigation. It was recently shown that for convex
problems the classical cyclic BCGD ... | 6028 |@word mild:1 norm:3 stronger:2 nd:1 open:1 decomposition:1 pg:4 klk:3 cyclic:30 series:1 existing:3 current:1 ka:1 luo:5 yet:2 must:1 belmont:1 update:3 stationary:1 xk:69 short:1 lr:6 contribute:1 ames:1 successive:2 mathematical:3 prove:3 consists:1 combine:3 introductory:1 inside:1 polyhedral:1 introduce:1 ind... |
5,557 | 6,029 | Cornering Stationary and Restless Mixing Bandits
with Remix-UCB
Liva Ralaivola
Q ARMA, LIF, CNRS
Aix Marseille University
F-13289 Marseille cedex 9, France
liva.ralaivola@lif.univ-mrs.fr
Julien Audiffren
CMLA
ENS Cachan, Paris Saclay University
94235 Cachan France
audiffren@cmla.ens-cachan.fr
Abstract
We study the re... | 6029 |@word eor:1 exploitation:8 eliminating:1 polynomial:1 nd:1 mention:1 liu:1 contains:1 selecting:1 ours:1 past:3 recovered:3 current:2 scovel:1 culprit:1 yet:1 liva:2 must:4 dx:1 distant:2 informative:1 designed:3 update:12 stationary:17 selected:5 provides:3 boosting:2 revisited:1 successive:3 org:1 quantit:1 mat... |
5,558 | 603 | Word Space
Hinrich Schiitze
Center for the Study of Language and Information
Ventura Hall
Stanford, CA 94305-4115
Abstract
Representations for semantic information about words are necessary for many applications of neural networks in natural language
processing. This paper describes an efficient, corpus-based method
... | 603 |@word manageable:1 squid:1 decomposition:6 uphold:1 carry:2 reduction:1 initial:1 karger:1 document:4 ocurring:1 synthesizer:1 assigning:2 scatter:1 riacs:1 cottrell:3 distant:2 alphanumeric:1 interpretable:1 hts:1 intelligence:1 selected:4 item:2 core:1 characterization:2 node:1 judith:1 five:1 scholtes:3 constru... |
5,559 | 6,030 | Fighting Bandits with a New Kind of Smoothness
Jacob Abernethy
University of Michigan
jabernet@umich.edu
Chansoo Lee
University of Michigan
chansool@umich.edu
Ambuj Tewari
University of Michigan
tewaria@umich.edu
Abstract
We provide a new analysis framework for the adversarial multi-armed bandit
problem. Using the n... | 6030 |@word exploitation:1 version:2 norm:4 stronger:1 open:1 rigged:1 calculus:1 forecaster:1 jacob:1 moment:1 initial:1 ftrl:3 series:1 selecting:1 interestingly:2 erven:2 com:3 nt:2 must:6 written:2 john:1 additive:2 happen:1 drop:1 designed:1 update:3 v:1 bart:4 resampling:4 guess:1 warmuth:3 parameterization:1 cor... |
5,560 | 6,031 | Asynchronous stochastic convex optimization:
the noise is in the noise and SGD don?t care
Sorathan Chaturapruek1
John C. Duchi2
Chris R?e1
1
2
Departments of Computer Science, Electrical Engineering, and 2 Statistics
Stanford University
Stanford, CA 94305
{sorathan,jduchi,chrismre}@stanford.edu
Abstract
We show that a... | 6031 |@word repository:1 briefly:1 version:2 stronger:1 replicate:1 proportion:2 simulation:1 covariance:6 sgd:1 tr:1 boundedness:1 moment:3 liu:2 series:1 lichman:1 document:2 current:2 com:1 john:1 numerical:3 designed:1 plot:6 update:12 juditsky:7 leaf:1 xk:22 ith:1 vanishing:1 short:1 core:56 iterates:4 math:1 nonc... |
5,561 | 6,032 | The Pareto Regret Frontier for Bandits
Tor Lattimore
Department of Computing Science
University of Alberta, Canada
tor.lattimore@gmail.com
Abstract
Given a multi-armed bandit problem it may be desirable to achieve a smallerthan-usual worst-case regret for some special actions. I show that the price for
such unbalanced... | 6032 |@word exploitation:1 briefly:1 version:1 achievable:2 seems:1 annoying:1 open:1 rigged:1 calculus:1 git:1 harder:1 liu:2 tuned:1 existing:1 com:1 gmail:1 must:2 subsequent:1 happen:1 v:1 intelligence:1 amir:1 ith:2 org:1 simpler:1 unbounded:1 c2:2 symposium:1 admirably:1 notably:1 expected:5 multi:5 alberta:1 com... |
5,562 | 6,033 | Online Learning with Gaussian Payoffs and Side
Observations
Yifan Wu1
1
Andr?as Gy?orgy2
Dept. of Computing Science
University of Alberta
{ywu12,szepesva}@ualberta.ca
2
Csaba Szepesv?ari1
Dept. of Electrical and Electronic Engineering
Imperial College London
a.gyorgy@imperial.ac.uk
Abstract
We consider a sequenti... | 6033 |@word innovates:1 exploitation:1 version:6 polynomial:1 replicate:1 dekel:1 open:1 d2:3 pick:1 tr:3 contains:2 selecting:2 kcr:1 existing:3 di2:8 michal:1 si:9 john:1 ctn:2 interpretable:2 bart:1 greedy:1 selected:3 intelligence:2 beginning:1 mannor:1 successive:1 ik:1 introduce:1 expected:9 hardness:5 ingenuity:... |
5,563 | 6,034 | Fast Rates for Exp-concave
Empirical Risk Minimization
Kfir Y. Levy
Technion
Haifa 32000, Israel
kfiryl@tx.technion.ac.il
Tomer Koren
Technion
Haifa 32000, Israel
tomerk@technion.ac.il
Abstract
We consider Empirical Risk Minimization (ERM) in the context of stochastic optimization with exp-concave and smooth losses?... | 6034 |@word mild:1 version:2 achievable:1 norm:18 stronger:1 open:4 km:1 bn:9 simplifying:1 elisseeff:6 tr:3 reduction:1 past:1 existing:2 must:1 enables:1 warmuth:4 affair:1 zhang:7 mathematical:1 stronglyconvex:1 prove:4 introduce:1 notably:1 indeed:4 expected:17 roughly:1 inspired:2 globally:1 actual:1 becomes:1 pro... |
5,564 | 6,035 | Adaptive Low-Complexity Sequential Inference for
Dirichlet Process Mixture Models
Theodoros Tsiligkaridis, Keith W. Forsythe
Massachusetts Institute of Technology, Lincoln Laboratory
Lexington, MA 02421 USA
ttsili@ll.mit.edu, forsythe@ll.mit.edu
Abstract
We develop a sequential low-complexity inference procedure for D... | 6035 |@word mild:1 trial:1 proportion:1 suitably:1 proportionality:1 covariance:5 recursively:2 initial:4 contains:3 outperforms:1 discretization:2 assigning:1 numerical:3 shape:3 designed:1 update:17 implying:2 greedy:5 prohibitive:1 intelligence:1 beginning:1 ith:1 lr:3 blei:3 provides:2 completeness:1 theodoros:1 ma... |
5,565 | 6,036 | Optimistic Gittins Indices
Eli Gutin
Operations Research Center, MIT
Cambridge, MA 02142
gutin@mit.edu
Vivek F. Farias
MIT Sloan School of Management
Cambridge, MA 02142
vivekf@mit.edu
Abstract
Starting with the Thomspon sampling algorithm, recent years have seen a resurgence of interest in Bayesian algorithms for th... | 6036 |@word trial:4 exploitation:1 briefly:1 version:3 achievable:3 cu:1 nd:1 seek:1 simulation:1 pick:2 carry:1 celebrated:4 series:4 offering:1 denoting:1 interestingly:2 outperforms:2 current:2 yet:1 must:1 john:2 numerical:1 j1:1 enables:1 drop:1 concert:1 update:1 plot:1 aside:1 alone:1 greedy:1 intelligence:1 ins... |
5,566 | 6,037 | Sub-sampled Newton Methods
with Non-uniform Sampling
Peng Xu? Jiyan Yang? Farbod Roosta-Khorasani? Christopher R?? Michael W. Mahoney?
? Stanford University
? University of California at Berkeley
pengxu@stanford.edu jiyan@stanford.edu farbod@icsi.berkeley.edu
chrismre@cs.stanford.edu mmahoney@stat.berkeley.edu
Abstra... | 6037 |@word worsens:1 repository:1 version:2 kulis:1 norm:16 stronger:2 nd:4 d2:6 simulation:1 decomposition:1 electronics:1 sociaux:1 series:2 score:32 lichman:1 liu:1 woodruff:1 daniel:1 initial:4 fa8750:2 ati:2 outperforms:1 existing:4 current:2 ka:1 naman:1 dx:1 written:4 readily:2 john:1 numerical:4 drop:1 update:... |
5,567 | 6,038 | Budgeted stream-based active learning
via adaptive submodular maximization
Kaito Fujii
Kyoto University
JST, ERATO, Kawarabayashi Large Graph Project
fujii@ml.ist.i.kyoto-u.ac.jp
Hisashi Kashima
Kyoto University
kashima@i.kyoto-u.ac.jp
Abstract
Active learning enables us to reduce the annotation cost by adaptively s... | 6038 |@word trial:1 exploitation:1 briefly:1 version:4 seems:1 initial:1 contains:5 series:1 selecting:12 offering:1 existing:10 current:2 comparing:1 com:1 beygelzimer:2 si:4 must:3 informative:1 kdd:1 enables:1 designed:2 greedy:3 selected:3 devising:2 item:58 intelligence:3 beginning:1 ith:1 marine:2 provides:3 math... |
5,568 | 6,039 | Sequential Neural Models with Stochastic Layers
Marco Fraccaro?
S?ren Kaae S?nderby?
Ulrich Paquet*
?
Technical University of Denmark
?
University of Copenhagen
*
Google DeepMind
Ole Winther??
Abstract
How can we efficiently propagate uncertainty in a latent state representation with
recurrent neural networks? This... | 6039 |@word seems:2 glue:1 reused:1 cleanly:1 dz1:2 propagate:1 covariance:2 recursively:2 initial:1 contains:1 series:1 past:3 existing:1 outperforms:1 freitas:1 com:2 activation:2 gpu:1 treating:1 plot:1 designed:1 polyphonic:10 stationary:2 generative:12 half:1 guess:1 parameterization:12 imitate:1 isotropic:1 fabiu... |
5,569 | 604 | Metamorphosis Networks:
An Alternative to Constructive Methods
Brian
v.
Bonnlander
Michael C. Mozer
Department of Computer Science &
Institute of Cognitive Science
University of Colorado
Boulder, CO 80309-0430
Abstract
Given a set oft raining examples, determining the appropriate number of free parameters is a chall... | 604 |@word version:1 simulation:6 pset:7 incurs:2 thereby:1 tr:1 initial:2 series:4 selecting:2 tuned:1 nowlan:4 activation:1 must:1 john:1 belmont:1 shape:1 girosi:2 update:3 mackey:3 fewer:2 coarse:1 location:5 toronto:2 five:1 height:2 oflocally:1 inside:1 multi:1 automatically:2 considering:1 increasing:2 begin:1 n... |
5,570 | 6,040 | Stochastic Gradient Methods for Distributionally
Robust Optimization with f -divergences
Hongseok Namkoong
Stanford University
hnamk@stanford.edu
John C. Duchi
Stanford University
jduchi@stanford.edu
Abstract
We develop efficient solution methods for a robust empirical risk minimization
problem designed to give calib... | 6040 |@word h:2 repository:1 briefly:1 norm:2 nd:1 simulation:1 forecaster:1 wexler:2 git:1 p0:1 sgd:4 ipm:1 initial:1 series:1 lichman:1 woodruff:1 tuned:2 document:2 suppressing:1 existing:1 current:1 com:1 nt:1 written:1 john:1 additive:1 designed:3 plot:4 update:10 juditsky:1 certificate:4 provides:5 completeness:2... |
5,571 | 6,041 | Optimal Tagging with Markov Chain Optimization
Nir Rosenfeld
School of Computer Science and Engineering
Hebrew University of Jerusalem
nir.rosenfeld@mail.huji.ac.il
Amir Globerson
The Blavatnik School of Computer Science
Tel Aviv University
gamir@post.tau.ac.il
Abstract
Many information systems use tags and keywords... | 6041 |@word trial:1 repository:1 polynomial:1 laurence:1 nd:1 closure:1 underperform:1 decomposition:6 lakshmanan:1 reduction:2 initial:4 series:1 exclusively:2 selecting:2 ours:4 rightmost:1 outperforms:3 existing:1 z2:11 assigning:1 crawling:1 must:1 readily:1 written:1 john:1 additive:1 informative:1 cheap:1 drop:1 ... |
5,572 | 6,042 | Learning to Communicate with
Deep Multi-Agent Reinforcement Learning
Jakob N. Foerster1,?
jakob.foerster@cs.ox.ac.uk
Nando de Freitas1,2,3
nandodefreitas@google.com
Yannis M. Assael1,?
yannis.assael@cs.ox.ac.uk
Shimon Whiteson1
shimon.whiteson@cs.ox.ac.uk
1
2
University of Oxford, United Kingdom
Canadian Institute... | 6042 |@word trial:3 private:2 achievable:1 norm:4 open:2 pick:1 thereby:2 united:1 selecting:1 document:4 yidqn:2 outperforms:2 current:3 com:2 analysed:1 activation:6 must:6 enables:1 treating:2 designed:1 plot:1 v:1 stationary:1 greedy:3 discovering:3 device:1 obsolete:1 selected:2 guess:1 generative:1 sukhbaatar:1 s... |
5,573 | 6,043 | Unified Methods for Exploiting
Piecewise Linear Structure in Convex Optimization
Tyler B. Johnson
University of Washington, Seattle
tbjohns@washington.edu
Carlos Guestrin
University of Washington, Seattle
guestrin@cs.washington.edu
Abstract
We develop methods for rapidly identifying important components of a convex
... | 6043 |@word version:3 pw:2 advantageous:1 c0:10 termination:1 simplifying:2 hsieh:1 versatile:1 reduction:1 liblinear:2 liu:1 contains:1 series:1 selecting:1 initial:1 interestingly:1 outperforms:2 existing:5 kx0:4 ka:1 comparing:1 task1:1 current:1 com:1 ndiaye:2 must:1 readily:1 enables:2 plot:2 progressively:1 v:1 i... |
5,574 | 6,044 | Minimizing Quadratic Functions in Constant Time
Kohei Hayashi
National Institute of Advanced Industrial Science and Technology
hayashi.kohei@gmail.com
Yuichi Yoshida
National Institute of Informatics and Preferred Infrastructure, Inc.
yyoshida@nii.ac.jp
Abstract
A sampling-based optimization method for quadratic func... | 6044 |@word trial:1 version:2 polynomial:2 norm:10 simulation:2 decomposition:1 sgd:1 nystr:7 series:1 nii:1 woodruff:1 ka:1 com:2 gmail:1 dx:5 yet:1 numerical:3 partition:8 predetermined:1 n0:4 greedy:1 prohibitive:1 instantiate:1 xk:3 yamada:3 infrastructure:1 provides:1 characterization:4 bijection:7 ron:2 org:1 mat... |
5,575 | 6,045 | Learning shape correspondence with
anisotropic convolutional neural networks
Davide Boscaini1 , Jonathan Masci1 , Emanuele Rodol`a1 , Michael Bronstein1,2,3
1
2
3
USI Lugano, Switzerland
Tel Aviv University, Israel
Intel, Israel
name.surname@usi.ch
Abstract
Convolutional neural networks have achieved extraordinary res... | 6045 |@word deformed:1 cnn:16 faculty:1 version:3 middle:1 kokkinos:1 disk:1 tried:1 dramatic:1 solid:1 shot:2 initial:2 contains:1 past:1 outperforms:5 qth:1 discretization:2 si:1 acnns:2 must:1 gpu:1 mesh:13 subsequent:1 distant:1 romero:1 shape:58 remove:1 drop:1 plane:5 isotropic:1 desktop:1 rodol:7 core:1 math:1 l... |
5,576 | 6,046 | Value Iteration Networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel
Dept. of Electrical Engineering and Computer Sciences, UC Berkeley
Abstract
We introduce the value iteration network (VIN): a fully differentiable neural network with a ?planning module? embedded within. VINs can learn to plan... | 6046 |@word trial:1 cnn:21 achievable:1 proportion:1 stronger:1 nd:1 pieter:1 simulation:2 propagate:1 sgd:1 outlook:1 harder:1 reduction:1 initial:3 configuration:2 contains:2 denoting:1 tuned:1 interestingly:1 fa8750:1 document:1 task1:1 outperforms:1 current:4 com:1 activation:2 si:2 yet:2 written:1 must:1 hypothesi... |
5,577 | 6,047 | Global Analysis of Expectation Maximization
for Mixtures of Two Gaussians
Ji Xu
Columbia University
jixu@cs.columbia.edu
Daniel Hsu
Columbia University
djhsu@cs.columbia.edu
Arian Maleki
Columbia University
arian@stat.columbia.edu
Abstract
Expectation Maximization (EM) is among the most popular algorithms for estima... | 6047 |@word version:2 polynomial:2 norm:2 confirms:1 covariance:4 decomposition:1 moment:3 initial:9 series:1 daniel:1 ka:3 wd:3 yet:1 attracted:1 shape:2 update:5 stationary:20 parameterization:1 isotropic:1 parametrization:1 provides:1 characterization:1 iterates:15 zhang:1 mathematical:3 along:1 dn:3 become:1 sympos... |
5,578 | 6,048 | Matrix Completion has No Spurious Local Minimum
Rong Ge
Duke University
308 Research Drive, NC 27708
rongge@cs.duke.edu.
Jason D. Lee
University of Southern California
3670 Trousdale Pkwy, CA 90089
jasonlee@marshall.usc.edu.
Tengyu Ma
Princeton University
35 Olden Street, NJ 08540
tengyu@cs.princeton.edu.
Abstract
M... | 6048 |@word version:6 polynomial:4 norm:13 stronger:1 nd:2 open:1 d2:1 km:1 decomposition:3 tr:1 harder:1 reduction:1 initial:2 liu:1 contains:1 daniel:1 luo:3 jns13:3 must:7 written:1 john:2 subsequent:1 stationary:2 implying:1 prohibitive:1 imitate:1 prize:2 characterization:1 simpler:1 zhang:1 mathematical:3 kvk2:1 ... |
5,579 | 6,049 | Bootstrap Model Aggregation for Distributed
Statistical Learning
Jun Han
Department of Computer Science
Dartmouth College
jun.han.gr@dartmouth.edu
Qiang Liu
Department of Computer Science
Dartmouth College
qiang.liu@dartmouth.edu
Abstract
In distributed, or privacy-preserving learning, we are often given a set of pr... | 6049 |@word mild:1 repository:1 seems:1 nd:1 dekel:2 simulation:1 covariance:2 shot:2 carry:1 reduction:8 liu:11 score:3 bootstrapped:3 outperforms:1 assigning:2 dx:1 chu:1 tot:1 additive:3 numerical:2 partition:2 generative:2 xk:2 huo:4 provides:1 location:1 simpler:1 zhang:7 mathematical:1 dn:7 c2:2 direct:1 ouput:1 ... |
5,580 | 605 | Nets with Unreliable Hidden Nodes Learn
Error-Correcting Codes
Stephen Judd
Paul W. Munro
Siemens Corporate Research
755 College Road East
Princeton NJ 08540
Department of Infonnation Science
University of Pittsburgh
Pittsburgh, PA 15260
jUdd@learning.siemens.com
munro@lis.pitt.edu
ABSTRACT
In a multi-layered neu... | 605 |@word trial:1 compression:3 open:1 simulation:1 fonn:1 thereby:1 com:1 activation:1 dx:1 must:1 cottrell:3 extensional:1 enables:1 half:4 selected:1 device:1 item:1 provides:1 node:16 attack:1 sigmoidal:1 mathematical:1 direct:1 consists:2 mask:1 expected:2 roughly:1 multi:1 increasing:2 begin:1 provided:1 kind:5 ... |
5,581 | 6,050 | Differential Privacy without Sensitivity
Kentaro Minami
The University of Tokyo
kentaro minami@mist.i.u-tokyo.ac.jp
Issei Sato
The University of Tokyo
sato@k.u-tokyo.ac.jp
Hiromi Arai
The University of Tokyo
arai@dl.itc.u-tokyo.ac.jp
Hiroshi Nakagawa
The University of Tokyo
nakagawa@dl.itc.u-tokyo.ac.jp
Abstract
Th... | 6050 |@word private:14 version:1 norm:1 villani:1 contraction:3 solid:1 boundedness:11 initial:1 contains:3 discretization:1 protection:2 yet:1 dx:1 stationary:1 smith:2 lr:5 provides:4 org:3 zhang:1 unbounded:7 dn:12 differential:60 focs:2 issei:1 consists:1 prove:3 manner:1 privacy:67 x0:2 roughly:2 behavior:1 examin... |
5,582 | 6,051 | Disentangling factors of variation in deep
representations using adversarial training
Michael Mathieu, Junbo Zhao, Pablo Sprechmann, Aditya Ramesh, Yann LeCun
719 Broadway, 12th Floor, New York, NY 10003
{mathieu, junbo.zhao, pablo, ar2922, yann}@cs.nyu.edu
Abstract
We introduce a conditional generative model for lea... | 6051 |@word kohli:1 eliminating:1 seems:1 pick:1 initial:2 contains:1 jimenez:2 ours:1 document:1 existing:1 greave:1 current:2 z2:1 diederik:2 written:1 ronan:1 realistic:4 informative:3 update:1 discrimination:1 aside:2 generative:24 alone:1 fewer:1 discovering:1 intelligence:1 alec:1 plane:1 ron:1 yuting:2 zhang:3 w... |
5,583 | 6,052 | Launch and Iterate: Reducing Prediction Churn
Q. Cormier
ENS Lyon
15 parvis Ren? Descartes
Lyon, France
quentin.cormier@ens-lyon.fr
M. Milani Fard, K. Canini, M. R. Gupta
Google Inc.
1600 Amphitheatre Parkway
Mountain View, CA 94043
{mmilanifard,canini,mayagupta}@google.com
Abstract
Practical applications of machine... | 6052 |@word trial:1 version:4 nd:1 cortez:1 elisseeff:2 harder:1 reduction:8 nomao:6 sociaux:1 series:2 initial:8 tuned:1 rkhs:1 longitudinal:1 dubourg:1 current:1 com:1 yet:1 must:4 portuguese:1 john:1 realistic:1 remove:1 drop:3 plot:3 hypothesize:1 v:1 stationary:5 intelligence:2 imitate:1 ith:1 provides:2 node:2 su... |
5,584 | 6,053 | Launch and Iterate: Reducing Prediction Churn
Q. Cormier
ENS Lyon
15 parvis Ren? Descartes
Lyon, France
quentin.cormier@ens-lyon.fr
M. Milani Fard, K. Canini, M. R. Gupta
Google Inc.
1600 Amphitheatre Parkway
Mountain View, CA 94043
{mmilanifard,canini,mayagupta}@google.com
Abstract
Practical applications of machine... | 6053 |@word trial:1 version:4 nd:1 cortez:1 elisseeff:2 harder:1 reduction:8 nomao:6 sociaux:1 series:2 initial:8 tuned:1 rkhs:1 longitudinal:1 dubourg:1 current:1 com:1 yet:1 must:4 portuguese:1 john:1 realistic:1 remove:1 drop:3 plot:3 hypothesize:1 v:1 stationary:5 intelligence:2 imitate:1 ith:1 provides:2 node:2 su... |
5,585 | 6,054 | Scalable Adaptive Stochastic Optimization Using
Random Projections
Gabriel Krummenacher? ?
gabriel.krummenacher@inf.ethz.ch
Yannic Kilcher?
yannic.kilcher@inf.ethz.ch
Brian McWilliams? ?
brian@disneyresearch.com
Joachim M. Buhmann?
jbuhmann@inf.ethz.ch
Nicolai Meinshausen?
meinshausen@stat.math.ethz.ch
?
Institute ... | 6054 |@word briefly:2 version:1 inversion:1 nd:2 mehta:1 tried:1 covariance:2 decomposition:6 sgd:8 incurs:1 tr:3 reduction:14 tuned:1 past:1 outperforms:1 current:1 com:1 nicolai:1 luo:1 yet:2 gpu:1 hofmann:2 remove:1 update:12 v:2 kilcher:2 selected:4 beginning:1 short:1 lr:21 provides:2 math:1 pascanu:1 org:1 zhang:... |
5,586 | 6,055 | The Forget-me-not Process
Kieran Milan? , Joel Veness? , James Kirkpatrick, Demis Hassabis
Google DeepMind
{kmilan,aixi,kirkpatrick,demishassabis}@google.com
Anna Koop, Michael Bowling
University of Alberta
{anna,bowling}@cs.ualberta.ca
Abstract
We introduce the Forget-me-not Process, an efficient, non-parametric meta... | 6055 |@word version:1 compression:3 seems:1 nd:1 c0:1 open:1 trofimov:1 lobe:1 solid:2 recursively:2 document:1 prefix:1 existing:2 current:1 com:2 wd:1 surprising:1 anne:3 yet:1 john:1 partition:30 informative:1 remove:1 designed:1 v:1 stationary:16 greedy:2 leaf:1 intelligence:2 amir:1 inspection:1 xk:2 beginning:1 o... |
5,587 | 6,056 | The Robustness of Estimator Composition
Jeff M. Phillips
School of Computing
University of Utah
Salt Lake City, UT 84112
jeffp@cs.utah.edu
Pingfan Tang
School of Computing
University of Utah
Salt Lake City, UT 84112
tang1984@cs.utah.edu
Abstract
We formalize notions of robustness for composite estimators via the not... | 6056 |@word mild:1 version:1 proportion:1 norm:2 widom:1 simulation:6 p0:5 ronchetti:3 initial:1 series:1 africa:1 aberrant:1 must:3 john:1 pe1:2 fn:2 update:1 half:2 fewer:1 website:3 xk:2 ith:6 short:1 stahel:1 cormode:1 provides:2 multiset:1 location:4 five:2 unbounded:1 height:3 burst:1 along:1 mathematical:1 becom... |
5,588 | 6,057 | Using Fast Weights to Attend to the Recent Past
Jimmy Ba
University of Toronto
Geoffrey Hinton
University of Toronto and Google Brain
jimmy@psi.toronto.edu
geoffhinton@google.com
Volodymyr Mnih
Google DeepMind
Joel Z. Leibo
Google DeepMind
Catalin Ionescu
Google DeepMind
vmnih@google.com
jzl@google.com
cdi@go... | 6057 |@word h:9 trial:1 version:2 proportion:2 norm:1 simulation:2 shot:1 recursively:3 contains:2 efficacy:1 past:9 outperforms:3 current:11 com:4 comparing:3 blank:1 activation:2 must:3 attracted:1 john:1 realize:1 uria:1 realistic:1 takeo:1 informative:1 plasticity:6 predetermined:1 enables:1 discernible:1 designed:... |
5,589 | 6,058 | Tight Complexity Bounds for Optimizing Composite
Objectives
Blake Woodworth
Toyota Technological Institute at Chicago
Chicago, IL, 60637
blake@ttic.edu
Nathan Srebro
Toyota Technological Institute at Chicago
Chicago, IL, 60637
nati@ttic.edu
Abstract
We provide tight upper and lower bounds on the complexity of minimiz... | 6058 |@word briefly:2 eliminating:1 polynomial:1 norm:1 stronger:1 tried:1 dramatic:1 sgd:2 reduction:4 series:1 precluding:1 past:1 yet:1 chu:1 must:21 realistic:2 chicago:4 zaid:1 progressively:1 v:2 alone:1 half:2 discovering:1 implying:1 provides:2 iterates:5 zhang:6 mathematical:2 c2:1 direct:1 prove:2 psetting:1 ... |
5,590 | 6,059 | Long-term causal effects via behavioral game theory
Panagiotis (Panos) Toulis
Econometrics & Statistics, Booth School
University of Chicago
Chicago, IL, 60637
panos.toulis@chicagobooth.edu
David C. Parkes
Department of Computer Science
Harvard University
Cambridge, MA, 02138
parkes@eecs.harvard.edu
Abstract
Planned ... | 6059 |@word version:1 longterm:1 middle:1 proportion:2 logit:2 open:2 adrian:1 seek:2 methodologically:1 pick:1 thereby:1 profit:2 solid:1 initial:4 series:5 score:1 comparing:2 lang:1 john:3 ronald:1 chicago:2 additive:1 subsequent:1 j1:9 enables:2 plot:3 designed:1 fund:1 stationary:1 intelligence:1 indicative:1 shor... |
5,591 | 606 | Using Aperiodic Reinforcement for Directed
Self-Organization During Development
PR Montague P Dayan SJ Nowlan A Pouget TJ Sejnowski
CNL, The Salk Institute
10010 North Torrey Pines Rd.
La Jolla, CA 92037, USA
read~helmholtz.sdsc.edu
Abstract
We present a local learning rule in which Hebbian learning is
conditional on... | 606 |@word neurophysiology:1 selforganization:1 middle:3 briefly:1 instrumental:1 r:4 pick:1 initial:2 foveal:3 reaction:1 current:4 nowlan:5 must:1 happen:1 informative:1 plasticity:1 motor:2 selected:3 shut:1 provides:2 become:4 persistent:1 incorrect:2 pathway:5 behavioral:1 manner:2 mask:1 expected:2 roughly:1 brai... |
5,592 | 6,060 | A Probabilistic Programming Approach To
Probabilistic Data Analysis
Feras Saad
MIT Probabilistic Computing Project
fsaad@mit.edu
Vikash Mansinghka
MIT Probabilistic Computing Project
vkm@mit.edu
Abstract
Probabilistic techniques are central to data analysis, but different approaches can
be challenging to apply, comb... | 6060 |@word version:4 stronger:1 heterogeneously:1 heuristically:1 km:3 simulation:2 pg:21 tr:1 carry:3 reduction:2 cyclic:2 contains:1 efficacy:1 series:3 longitudinal:1 scatter:1 written:3 exposing:1 numerical:9 j1:1 plot:1 drop:1 update:1 resampling:1 generative:16 selected:1 yr:19 guess:2 intelligence:1 xk:13 recor... |
5,593 | 6,061 | Solving Random Systems of Quadratic Equations via
Truncated Generalized Gradient Flow
?
Gang Wang?,? and Georgios B. Giannakis?
ECE Dept. and Digital Tech. Center, Univ. of Minnesota, Mpls, MN 55455, USA
?
School of Automation, Beijing Institute of Technology, Beijing 100081, China
{gangwang, georgios}@umn.edu
Abstr... | 6061 |@word trial:2 erate:1 briefly:1 version:1 inversion:1 instrumental:1 norm:2 suitably:1 c0:3 phasecut:1 open:1 confirms:2 seek:1 cos2:9 accounting:1 decomposition:1 covariance:2 incurs:1 tr:3 solid:1 moment:1 initial:8 reduction:1 series:2 liu:1 selecting:1 denoting:1 interestingly:2 ati:27 outperforms:3 existing:... |
5,594 | 6,062 | Balancing Suspense and Surprise: Timely Decision
Making with Endogenous Information Acquisition
Ahmed M. Alaa
Electrical Engineering Department
University of California, Los Angeles
Mihaela van der Schaar
Electrical Engineering Department
University of California, Los Angeles
Abstract
We develop a Bayesian model for... | 6062 |@word instrumental:1 termination:1 calculus:1 integrative:1 decomposition:1 p0:1 pressure:5 it1:1 series:19 contains:1 score:1 horvitz:1 current:7 optim:1 mihaela:1 must:1 happen:1 partition:14 informative:1 shape:1 moreno:2 plot:2 stationary:2 obsolete:1 filtered:1 harvesting:1 characterization:1 preference:2 zh... |
5,595 | 6,063 | Structure-Blind Signal Recovery
Dmitry Ostrovsky? Zaid Harchaoui? Anatoli Juditsky? Arkadi Nemirovski?
firstname.lastname@imag.fr
Abstract
We consider the problem of recovering a signal observed in Gaussian noise. If
the set of signals is convex and compact, and can be specified beforehand, one
can use classical line... | 6063 |@word trial:3 inversion:1 polynomial:13 norm:16 decomposition:1 p0:1 ipm:2 harder:2 initial:1 contains:1 outperforms:2 recovered:1 discretization:1 current:1 yet:1 must:2 fn:7 numerical:5 zaid:1 juditsky:6 n0:1 half:2 xk:1 haykin:1 num:1 completeness:1 constructed:1 prove:1 inside:1 hermitian:1 introduce:1 sayed:... |
5,596 | 6,064 | End-to-End Goal-Driven Web Navigation
Rodrigo Nogueira
Tandon School of Engineering
New York University
rodrigonogueira@nyu.edu
Kyunghyun Cho
Courant Institute of Mathematical Sciences
New York University
kyunghyun.cho@nyu.edu
Abstract
We propose a goal-driven web navigation as a benchmark task for evaluating an
age... | 6064 |@word trial:5 version:1 open:2 confirms:1 sgd:1 configuration:1 contains:2 score:3 att:1 genetic:1 document:6 ours:1 outperforms:4 existing:1 current:9 com:3 comparing:1 manuel:1 mari:1 si:22 crawling:5 jeopardy:20 written:1 must:2 ronald:1 visible:1 partition:1 v:2 implying:2 alone:2 selected:8 website:13 nq:10 ... |
5,597 | 6,065 | Stochastic Online AUC Maximization
Yiming Ying? , Longyin Wen? , Siwei Lyu?
?
Department of Mathematics and Statistics
SUNY at Albany, Albany, NY, 12222, USA
?
Department of Computer Science
SUNY at Albany, Albany, NY, 12222, USA
Abstract
Area under ROC (AUC) is a metric which is widely used for measuring the
classifi... | 6065 |@word pw:1 twelfth:1 d2:1 covariance:5 initial:1 existing:5 z2:1 comparing:1 numerical:1 plot:1 update:8 juditsky:1 v:3 intelligence:2 completeness:1 provides:1 math:1 node:1 firstly:1 five:3 along:1 kvk2:1 prove:2 x0:23 pairwise:5 news20:3 p1:4 nor:1 multi:1 moulines:1 td:1 actual:1 cpu:1 becomes:1 spain:1 xx:1 ... |
5,598 | 6,066 | f -GAN: Training Generative Neural Samplers using
Variational Divergence Minimization
Sebastian Nowozin, Botond Cseke, Ryota Tomioka
Machine Intelligence and Perception Group
Microsoft Research
{Sebastian.Nowozin, Botond.Cseke, ryoto}@microsoft.com
Abstract
Generative neural samplers are probabilistic models that impl... | 6066 |@word mild:1 norm:1 hyv:1 semicontinuous:4 contrastive:2 thereby:1 moment:1 past:1 existing:2 current:1 com:2 rnade:2 activation:10 dx:11 readily:1 uria:1 realistic:1 numerical:2 plot:3 update:4 stationary:2 generative:38 intelligence:1 isotropic:1 completeness:1 provides:1 pascanu:1 semester:1 simpler:2 zhang:1 ... |
5,599 | 6,067 | Tagger: Deep Unsupervised Perceptual Grouping
Klaus Greff* , Antti Rasmus, Mathias Berglund, Tele Hotloo Hao,
J?rgen Schmidhuber* , Harri Valpola
The Curious AI Company {antti,mathias,hotloo,harri}@cai.fi
*
IDSIA {klaus,juergen}@idsia.ch
Abstract
We present a framework for efficient perceptual inference that explicit... | 6067 |@word version:4 compression:2 seems:1 hyv:2 confirms:1 q1:3 carry:1 etric:3 contains:1 score:7 series:1 selecting:1 ours:2 outperforms:2 current:1 z2:3 comparing:1 com:1 activation:3 gpu:1 mesh:1 visible:1 subsequent:1 shape:20 enables:1 remove:1 hypothesize:1 generative:6 fewer:1 cue:1 une:1 timo:1 short:1 infra... |
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