index int64 0 20.3k | text stringlengths 0 1.3M | year stringdate 1987-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
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3,700 | Nash Equilibria of Static Prediction Games Michael Br¨uckner Department of Computer Science University of Potsdam, Germany mibrueck@cs.uni-potsdam.de Tobias Scheffer Department of Computer Science University of Potsdam, Germany scheffer@cs.uni-potsdam.de Abstract The standard assumption of identical... | 2009 | 198 |
3,701 | Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices by Convex Optimization John Wright∗, Yigang Peng, Yi Ma Visual Computing Group Microsoft Research Asia {jowrig,v-yipe,mayi}@microsoft.com Arvind Ganesh, Shankar Rao Coordinated Science Laboratory University of Illinois ... | 2009 | 199 |
3,702 | DUOL: A Double Updating Approach for Online Learning Peilin Zhao School of Comp. Eng. Nanyang Tech. University Singapore 639798 zhao0106@ntu.edu.sg Steven C.H. Hoi School of Comp. Eng. Nanyang Tech. University Singapore 639798 chhoi@ntu.edu.sg Rong Jin Dept. of Comp. Sci. & Eng. Michigan Sta... | 2009 | 2 |
3,703 | Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning Anne S. Hsu Thomas L. Griffiths Department of Psychology University of California, Berkeley Berkeley, CA 94720 {showen,tom griffiths}@berkeley.edu Abstract A classic debate in cognitive science revolves ar... | 2009 | 20 |
3,704 | Unsupervised Feature Selection for the k-means Clustering Problem Christos Boutsidis Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180 boutsc@cs.rpi.edu Michael W. Mahoney Department of Mathematics Stanford University Stanford, CA 94305 mmahoney@cs.stanford.edu Petro... | 2009 | 200 |
3,705 | Multi-step Linear Dyna-style Planning Hengshuai Yao Department of Computing Science University of Alberta Edmonton, AB, Canada T6G2E8 Shalabh Bhatnagar Department of Computer Science and Automation Indian Institute of Science Bangalore, India 560012 Dongcui Diao School of Economics and Management ... | 2009 | 201 |
3,706 | Variational Inference for the Nested Chinese Restaurant Process Chong Wang Computer Science Department Princeton University chongw@cs.princeton.edu David M. Blei Computer Science Department Princeton University blei@cs.princeton.edu Abstract The nested Chinese restaurant process (nCRP) is a powerf... | 2009 | 202 |
3,707 | A Biologically Plausible Model for Rapid Natural Image Identification S. Ghebreab, A. W.M. Smeulders Intelligent Sensory Information Systems Group University of Amsterdam, The Netherlands s.ghebreab@uva.nl H. S. Schoite, V.A.F. Lamme Cognitive Neuroscience Group University of Amsterdam, The Neth... | 2009 | 203 |
3,708 | Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition Tom Y. Ouyang Randall Davis Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 USA {ouyang,davis}@csail.mit.edu Abstract We propose a ... | 2009 | 204 |
3,709 | Relax then Compensate: On Max-Product Belief Propagation and More Arthur Choi Computer Science Department University of California, Los Angeles Los Angeles, CA 90095 aychoi@cs.ucla.edu Adnan Darwiche Computer Science Department University of California, Los Angeles Los Angeles, CA 90095 darwiche@c... | 2009 | 205 |
3,710 | Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations Mingyuan Zhou, Haojun Chen, John Paisley, Lu Ren, 1Guillermo Sapiro and Lawrence Carin Department of Electrical and Computer Engineering Duke University, Durham, NC 27708-0291, USA 1Department of Electrical and Computer Engineering ... | 2009 | 206 |
3,711 | Discrete MDL Predicts in Total Variation Marcus Hutter RSISE @ ANU and SML @ NICTA Canberra, ACT, 0200, Australia marcus@hutter1.net www.hutter1.net Abstract The Minimum Description Length (MDL) principle selects the model that has the shortest code for data plus model. We show that for a countable clas... | 2009 | 207 |
3,712 | Efficient and Accurate ℓp-Norm Multiple Kernel Learning Marius Kloft University of California Berkeley, USA Ulf Brefeld Yahoo! Research Barcelona, Spain S¨oren Sonnenburg Technische Universit¨at Berlin Berlin, Germany Pavel Laskov Universit¨at T¨ubingen T¨ubingen, Germany Klaus-Robert M¨uller... | 2009 | 208 |
3,713 | Occlusive Components Analysis J¨org L¨ucke Frankfurt Institute for Advanced Studies Goethe-University Frankfurt, Germany luecke@fias.uni-frankfurt.de Richard Turner Gatsby Computational Neuroscience Unit, UCL 17 Queen Square, London WC1N 3AR, UK turner@gatsby.ucl.ac.uk Maneesh Sahani Gatsby Computat... | 2009 | 209 |
3,714 | Non-stationary continuous dynamic Bayesian networks Marco Grzegorczyk Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany grzegorczyk@statistik.tu-dortmund.de Dirk Husmeier Biomathematics & Statistics Scotland (BioSS) JCMB, The King’s Buildings, Edinburgh EH93JZ, United Kingdom dir... | 2009 | 21 |
3,715 | Distribution-Calibrated Hierarchical Classification Ofer Dekel Microsoft Research One Microsoft Way, Redmond, WA 98052, USA oferd@microsoft.com Abstract While many advances have already been made in hierarchical classification learning, we take a step back and examine how a hierarchical classification problem ... | 2009 | 210 |
3,716 | A Smoothed Approximate Linear Program Vijay V. Desai IEOR, Columbia University vvd2101@columbia.edu Vivek F. Farias MIT Sloan vivekf@mit.edu Ciamac C. Moallemi GSB, Columbia University ciamac@gsb.columbia.edu Abstract We present a novel linear program for the approximation of the dynamic program... | 2009 | 211 |
3,717 | Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model Edward Vul, Michael C. Frank, and Joshua B. Tenenbaum Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02138 {evul, mcfrank, jbt}@mit.edu ... | 2009 | 212 |
3,718 | Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models Jing Gao†, Feng Liang†, Wei Fan‡, Yizhou Sun†, and Jiawei Han† †University of Illinois at Urbana-Champaign, IL USA ‡IBM TJ Watson Research Center, Hawthorn, NY USA †{jinggao3,liangf,sun22,hanj}@illinois.edu, ‡weifan@us.ibm.co... | 2009 | 213 |
3,719 | Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models Gideon Mann Google gmann@google.com Ryan McDonald Google ryanmcd@google.com Mehryar Mohri Courant Institute and Google mohri@cims.nyu.edu Nathan Silberman Google nsilberman@google.com Daniel D. Walker∗ NLP Lab,... | 2009 | 214 |
3,720 | On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation J. Saketha Nath Dept. of Computer Science & Engg., Indian Institute of Technology, Bombay. saketh@cse.iitb.ac.in G. Dinesh Dept. of Computer Science & Automation, Indian Institute of Science, Bangalore. dinesh@csa.i... | 2009 | 215 |
3,721 | Estimating image bases for visual image reconstruction from human brain activity Yusuke Fujiwara1 Yoichi Miyawaki2,1 Yukiyasu Kamitani1 1ATR Computational Neuroscience Laboratories 2National Institute of Information and Communications Technology 2-2-2 Hikaridai, Seika-cho, Kyoto, Japan yureisoul@gmail.c... | 2009 | 216 |
3,722 | A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers Sahand Negahban Department of EECS UC Berkeley sahand n@eecs.berkeley.edu Pradeep Ravikumar Department of Computer Sciences UT Austin pradeepr@cs.utexas.edu Martin J. Wainwright Department of Statistics... | 2009 | 217 |
3,723 | Group Orthogonal Matching Pursuit for Variable Selection and Prediction Aur´elie C. Lozano, Grzegorz ´Swirszcz, Naoki Abe IBM Watson Research Center, 1101 Kitchawan Road, Yorktown Heights NY 10598,USA {aclozano,swirszcz,nabe}@us.ibm.com Abstract We consider the problem of variable group selection for le... | 2009 | 218 |
3,724 | Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions Parikshit Ram, Dongryeol Lee, Hua Ouyang and Alexander G. Gray Computational Science and Engineering, Georgia Institute of Technology Atlanta, GA 30332 {p.ram@,dongryel@cc.,houyang@,agray@cc.}gatech.edu Abstract The l... | 2009 | 219 |
3,725 | Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data Shuai Huang1, Jing Li1, Liang Sun2,3, Jun Liu2,3, Teresa Wu1, Kewei Chen4, Adam Fleisher4, Eric Reiman4, Jieping Ye2,3 1Industrial Engineering, 2Computer Science and Engineering, and 3Center for Evolutionary Functional... | 2009 | 22 |
3,726 | Online Learning of Assignments Matthew Streeter Google, Inc. Pittsburgh, PA 15213 mstreeter@google.com Daniel Golovin Carnegie Mellon University Pittsburgh, PA 15213 dgolovin@cs.cmu.edu Andreas Krause California Institute of Technology Pasadena, CA 91125 krausea@caltech.edu Abstract Which ad... | 2009 | 220 |
3,727 | Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining George Konidaris Computer Science Department University of Massachusetts Amherst Amherst MA 01003 USA gdk@cs.umass.edu Andrew Barto Computer Science Department University of Massachusetts Amherst Amherst MA 01003 USA ... | 2009 | 221 |
3,728 | Strategy Grafting in Extensive Games Kevin Waugh waugh@cs.cmu.edu Department of Computer Science Carnegie Mellon University Nolan Bard, Michael Bowling {nolan,bowling}@cs.ualberta.ca Department of Computing Science University of Alberta Abstract Extensive games are often used to model the interactio... | 2009 | 222 |
3,729 | Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference Michael Wick, Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum Department of Computer Science University of Massachusetts Amherst Amherst, MA 01003 {mwick,khash,sameer,mccallum}@cs.umass.edu Abstract Large, relational fac... | 2009 | 223 |
3,730 | Learning a Small Mixture of Trees∗ M. Pawan Kumar Computer Science Department Stanford University pawan@cs.stanford.edu Daphne Koller Computer Science Department Stanford University koller@cs.stanford.edu Abstract The problem of approximating a given probability distribution using a simpler distribu... | 2009 | 224 |
3,731 | An Additive Latent Feature Model for Transparent Object Recognition Mario Fritz UC Berkeley Michael Black Brown University Gary Bradski Willow Garage Sergey Karayev UC Berkeley Trevor Darrell UC Berkeley Abstract Existing methods for visual recognition based on quantized local features can per... | 2009 | 225 |
3,732 | Know Thy Neighbour: A Normative Theory of Synaptic Depression Jean-Pascal Pfister Computational & Biological Learning Lab Department of Engineering, University of Cambridge Trumpington Street, Cambridge CB2 1PZ, United Kingdom jean-pascal.pfister@eng.cam.ac.uk Peter Dayan Gatsby Computational Neuroscienc... | 2009 | 226 |
3,733 | Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs Alexandre Bouchard-Cˆot´e1 Slav Petrov2,† bouchard@cs.berkeley.edu slav@google.com 1Computer Science Division University of California at Berkeley Berkeley, CA 94720 Dan Klein1 klein@cs.berkeley.edu 2Google Research ... | 2009 | 227 |
3,734 | A Rate Distortion Approach for Semi-Supervised Conditional Random Fields Yang Wang†∗ Gholamreza Haffari†∗ Shaojun Wang‡ Greg Mori† †School of Computing Science ‡Kno.e.sis Center Simon Fraser University Wright State University Burnaby, BC V5A 1S6, Canada Dayton, OH 45435, USA {ywang12,ghaffar1,mo... | 2009 | 228 |
3,735 | Learning to Explore and Exploit in POMDPs Chenghui Cai, Xuejun Liao, and Lawrence Carin Department of Electrical and Computer Engineering Duke University Durham, NC 27708-0291, USA Abstract A fundamental objective in reinforcement learning is the maintenance of a proper balance between exploration and exp... | 2009 | 229 |
3,736 | Potential-Based Agnostic Boosting Adam Tauman Kalai Microsoft Research adum@microsoft.com Varun Kanade Harvard University vkanade@fas.harvard.edu Abstract We prove strong noise-tolerance properties of a potential-based boosting algorithm, similar to MadaBoost (Domingo and Watanabe, 2000) and SmoothBoost... | 2009 | 23 |
3,737 | Multiple Incremental Decremental Learning of Support Vector Machines Masayuki Karasuyama and Ichiro Takeuchi Department of Engineering, Nagoya Institute of Technology Gokiso-cho, Syouwa-ku, Nagoya, Aichi, 466-8555, JAPAN krsym@ics.nitech.ac.jp, takeuchi.ichiro@nitech.ac.jp Abstract We propose a multiple i... | 2009 | 230 |
3,738 | Bayesian Source Localization with the Multivariate Laplace Prior Marcel van Gerven1,2 Botond Cseke1 Robert Oostenveld2 Tom Heskes1,2 1Institute for Computing and Information Sciences 2Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen Nijmegen, The Netherlands Abstract ... | 2009 | 231 |
3,739 | Learning Non-Linear Combinations of Kernels Corinna Cortes Google Research 76 Ninth Ave New York, NY 10011 corinna@google.com Mehryar Mohri Courant Institute and Google 251 Mercer Street New York, NY 10012 mohri@cims.nyu.edu Afshin Rostamizadeh Courant Institute and Google 251 Mercer Street ... | 2009 | 232 |
3,740 | Conditional Neural Fields Jian Peng Toyota Technological Institute at Chicago 6045 S. Kenwood Ave. Chicago, IL 60637 jpengwhu@gmail.com Liefeng Bo Toyota Technological Institute at Chicago 6045 S. Kenwood Ave. Chicago, IL 60637 liefengbo@gmail.com Jinbo Xu Toyota Technological Institute at Chica... | 2009 | 233 |
3,741 | An Online Algorithm for Large Scale Image Similarity Learning Gal Chechik Google Mountain View, CA gal@google.com Varun Sharma Google Bengalooru, Karnataka, India vasharma@google.com Uri Shalit ICNC, The Hebrew University Israel uri.shalit@mail.huji.ac.il Samy Bengio Google Mountain View... | 2009 | 234 |
3,742 | Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning Robert Legenstein1∗, Steven M. Chase2,3,4, Andrew B. Schwartz2,3, Wolfgang Maass1 1 Institute for Theoretical Computer Science, Graz University of Technology, Austria 2Department of Neurobiology, University... | 2009 | 235 |
3,743 | The Infinite Partially Observable Markov Decision Process Finale Doshi-Velez Cambridge University Cambridge, CB21PZ, UK finale@alum.mit.edu Abstract The Partially Observable Markov Decision Process (POMDP) framework has proven useful in planning domains where agents must balance actions that provide know... | 2009 | 236 |
3,744 | Accelerated Gradient Methods for Stochastic Optimization and Online Learning Chonghai Hu♯†, James T. Kwok♯, Weike Pan♯ ♯Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong † Department of Mathematics, Zhejiang University Hangzho... | 2009 | 237 |
3,745 | Robust Value Function Approximation Using Bilinear Programming Marek Petrik Department of Computer Science University of Massachusetts Amherst, MA 01003 petrik@cs.umass.edu Shlomo Zilberstein Department of Computer Science University of Massachusetts Amherst, MA 01003 shlomo@cs.umass.edu Abstrac... | 2009 | 238 |
3,746 | Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units Feng Yan Department of CS Purdue University West Lafayette, IN 47907 Ningyi Xu Microsoft Research Asia No. 49 Zhichun Road Beijing, P.R. China Yuan (Alan) Qi Departments of CS and Statistics Purdue University West ... | 2009 | 239 |
3,747 | Gaussian process regression with Student-t likelihood Jarno Vanhatalo Department of Biomedical Engineering and Computational Science Helsinki University of Technology Finland jarno.vanhatalo@tkk.fi Pasi Jyl¨anki Department of Biomedical Engineering and Computational Science Helsinki University of Te... | 2009 | 24 |
3,748 | Local Rules for Global MAP: When Do They Work ? Kyomin Jung∗ KAIST Daejeon, Korea kyomin@kaist.edu Pushmeet Kohli Microsoft Research Cambridge, UK pkohli@microsoft.com Devavrat Shah MIT Cambridge, MA, USA devavrat@mit.edu Abstract We consider the question of computing Maximum A Posteriori (M... | 2009 | 240 |
3,749 | Bayesian estimation of orientation preference maps Jakob H. Macke MPI for Biological Cybernetics and University of T¨ubingen Computational Vision and Neuroscience Spemannstrasse 41, 72076 T¨ubingen jakob@tuebingen.mpg.de Sebastian Gerwinn MPI for Biological Cybernetics and University of T¨ubingen Co... | 2009 | 241 |
3,750 | A Bayesian Analysis of Dynamics in Free Recall Richard Socher Department of Computer Science Stanford University Stanford, CA 94305 richard@socher.org Samuel J. Gershman, Adler J. Perotte, Per B. Sederberg Department of Psychology Princeton University Princeton, NJ 08540 {sjgershm,aperotte,persed}@p... | 2009 | 242 |
3,751 | Structural inference affects depth perception in the context of potential occlusion Ian H. Stevenson and Konrad P. K¨ording Department of Physical Medicine and Rehabilitation Northwestern University Chicago, IL 60611 i-stevenson@northwestern.edu Abstract In many domains, humans appear to combine percept... | 2009 | 243 |
3,752 | Indian Buffet Processes with Power-law Behavior Yee Whye Teh and Dilan G¨or¨ur Gatsby Computational Neuroscience Unit, UCL 17 Queen Square, London WC1N 3AR, United Kingdom {ywteh,dilan}@gatsby.ucl.ac.uk Abstract The Indian buffet process (IBP) is an exchangeable distribution over binary matrices used in Bay... | 2009 | 244 |
3,753 | Boosting with Spatial Regularization Zhen James Xiang1 Yongxin Taylor Xi1 Uri Hasson2 Peter J. Ramadge1 1: Department of Electrical Engineering, Princeton University, Princeton NJ, USA 2: Department of Psychology, and Neuroscience Institute, Princeton University, Princeton NJ, USA {zxiang, yxi, hasson, ra... | 2009 | 245 |
3,754 | Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models Jonathan W. Pillow Departments of Psychology and Neurobiology University of Texas at Austin pillow@mail.utexas.edu Abstract Recent work on the statistical modeling of neural responses has focused on modulated re... | 2009 | 246 |
3,755 | Semi-supervised Regression using Hessian Energy with an Application to Semi-supervised Dimensionality Reduction Kwang In Kim1, Florian Steinke2,3, and Matthias Hein1 1Department of Computer Science, Saarland University Saarbr¨ucken, Germany 2Siemens AG Corporate Technology Munich, Germany 3MPI for Biologica... | 2009 | 247 |
3,756 | Localizing Bugs in Program Executions with Graphical Models Laura Dietz Max-Planck Institute for Computer Science Saarbruecken, Germany dietz@mpi-inf.mpg.de Valentin Dallmeier Saarland University Saarbruecken, Germany dallmeier@cs.uni-saarland.de Andreas Zeller Saarland University Saarbruecken, ... | 2009 | 248 |
3,757 | Human Rademacher Complexity Xiaojin Zhu1, Timothy T. Rogers2, Bryan R. Gibson1 Department of {1Computer Sciences, 2Psychology} University of Wisconsin-Madison. Madison, WI 15213 jerryzhu@cs.wisc.edu, ttrogers@wisc.edu, bgibson@cs.wisc.edu Abstract We propose to use Rademacher complexity, originally develope... | 2009 | 249 |
3,758 | Bilinear classifiers for visual recognition Hamed Pirsiavash Deva Ramanan Charless Fowlkes Department of Computer Science University of California at Irvine {hpirsiav,dramanan,fowlkes}@ics.uci.edu Abstract We describe an algorithm for learning bilinear SVMs. Bilinear classifiers are a discriminative var... | 2009 | 25 |
3,759 | Online Submodular Minimization Elad Hazan IBM Almaden Research Center 650 Harry Rd, San Jose, CA 95120 hazan@us.ibm.com Satyen Kale Yahoo! Research 4301 Great America Parkway, Santa Clara, CA 95054 skale@yahoo-inc.com Abstract We consider an online decision problem over a discrete space in which the... | 2009 | 250 |
3,760 | Learning from Multiple Partially Observed Views – an Application to Multilingual Text Categorization Massih R. Amini Interactive Language Technologies Group National Research Council Canada Massih-Reza.Amini@cnrc-nrc.gc.ca Nicolas Usunier Laboratoire d’Informatique de Paris 6 Universit´e Pierre et Marie... | 2009 | 251 |
3,761 | Measuring model complexity with the prior predictive Wolf Vanpaemel ∗ Department of Psychology University of Leuven Belgium. wolf.vanpaemel@psy.kuleuven.be Abstract In the last few decades, model complexity has received a lot of press. While many methods have been proposed that jointly measure a model’s... | 2009 | 252 |
3,762 | Nonparametric Bayesian Texture Learning and Synthesis Long (Leo) Zhu1 Yuanhao Chen2 William Freeman1 Antonio Torralba1 1CSAIL, MIT {leozhu, billf, antonio}@csail.mit.edu 2Department of Statistics, UCLA yhchen@stat.ucla.edu Abstract We present a nonparametric Bayesian method for texture learning and synt... | 2009 | 253 |
3,763 | A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation Lan Du, Lu Ren, 1David B. Dunson and Lawrence Carin Department of Electrical and Computer Engineering 1Statistics Department Duke University Durham, NC 27708-0291, USA {ld53, lr, lcarin}@ee.duke.edu, dunson@stats.duke.e... | 2009 | 254 |
3,764 | Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise Jacob Whitehill, Paul Ruvolo, Tingfan Wu, Jacob Bergsma, and Javier Movellan Machine Perception Laboratory University of California, San Diego La Jolla, CA, USA { jake, paul, ting, jbergsma, movellan }@mplab.ucs... | 2009 | 255 |
3,765 | Reading Tea Leaves: How Humans Interpret Topic Models Jonathan Chang ∗ Facebook 1601 S California Ave. Palo Alto, CA 94304 jonchang@facebook.com Jordan Boyd-Graber ∗ Institute for Advanced Computer Studies University of Maryland jbg@umiacs.umd.edu Sean Gerrish, Chong Wang, David M. Blei Department... | 2009 | 256 |
3,766 | Segmenting Scenes by Matching Image Composites Bryan C. Russell1 Alexei A. Efros2,1 Josef Sivic1 William T. Freeman3 Andrew Zisserman4,1 1INRIA∗ 2Carnegie Mellon University 3CSAIL MIT 4University of Oxford Abstract In this paper, we investigate how, given an image, similar images sharing the same global... | 2009 | 257 |
3,767 | Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes Kian Ming A. Chai School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK k.m.a.chai@ed.ac.uk Abstract We provide some insights into how task correlations in multi-task Gaussian pr... | 2009 | 258 |
3,768 | Breaking Boundaries: Active Information Acquisition Across Learning and Diagnosis Ashish Kapoor and Eric Horvitz Microsoft Research 1 Microsoft Way Redmond, WA 98052 Abstract To date, the processes employed for active information acquisition during periods of learning and diagnosis have been considered ... | 2009 | 259 |
3,769 | Zero-Shot Learning with Semantic Output Codes Mark Palatucci Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 mpalatuc@cs.cmu.edu Dean Pomerleau Intel Labs Pittsburgh, PA 15213 dean.a.pomerleau@intel.com Geoffrey Hinton Computer Science Department University of Toronto Toront... | 2009 | 26 |
3,770 | An Integer Projected Fixed Point Method for Graph Matching and MAP Inference Marius Leordeanu Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 leordeanu@gmail.com Martial Hebert Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 hebert@ri.cmu.edu Rahul Sukthanka... | 2009 | 260 |
3,771 | Efficient Bregman Range Search Lawrence Cayton Max Planck Institute for Biological Cybernetics lcayton@tuebingen.mpg.de Abstract We develop an algorithm for efficient range search when the notion of dissimilarity is given by a Bregman divergence. The range search task is to return all points in a potentiall... | 2009 | 261 |
3,772 | Complexity of Decentralized Control: Special Cases Martin Allen Department of Computer Science Connecticut College New London, CT 06320 martin.allen@conncoll.edu Shlomo Zilberstein Department of Computer Science University of Massachusetts Amherst, MA 01003 shlomo@cs.umass.edu Abstract The worst... | 2009 | 262 |
3,773 | Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition Natasha Singh-Miller Massachusetts Institute of Technology Cambridge, MA natashas@mit.edu Michael Collins Massachusetts Institute of Technology Cambridge, MA mcollins@csail.mit.edu Ab... | 2009 | 27 |
3,774 | Semi-supervised Learning in Gigantic Image Collections Rob Fergus Courant Institute, NYU, 715 Broadway, New York, NY 10003 fergus@cs.nyu.edu Yair Weiss School of Computer Science, Hebrew University, 91904, Jerusalem, Israel yweiss@huji.ac.il Antonio Torralba CSAIL, EECS, MIT, 32 Vassar St., ... | 2009 | 28 |
3,775 | Sensitivity analysis in HMMs with application to likelihood maximization Pierre-Arnaud Coquelin, Vekia, Lille, France pacoquelin@vekia.fr Romain Deguest∗ Columbia University, New York City, NY 10027 rd2304@columbia.edu Rémi Munos INRIA Lille - Nord Europe, Sequel Project, France remi.munos@inria.fr ... | 2009 | 29 |
3,776 | Compressed Least-Squares Regression Odalric-Ambrym Maillard and R´emi Munos SequeL Project, INRIA Lille - Nord Europe, France {odalric.maillard, remi.munos}@inria.fr Abstract We consider the problem of learning, from K data, a regression function in a linear space of high dimension N using projections onto a ... | 2009 | 3 |
3,777 | Who’s Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation Luo Jie Idiap and EPF Lausanne jluo@idiap.ch Barbara Caputo Idiap Research Institute bcaputo@idiap.ch Vittorio Ferrari ETH Zurich ferrari@vision.ee.ethz.ch Abstract Given a corpus of news items consisti... | 2009 | 30 |
3,778 | Streaming Pointwise Mutual Information Benjamin Van Durme University of Rochester Rochester, NY 14627, USA Ashwin Lall Georgia Institute of Technology Atlanta, GA 30332, USA Abstract Recent work has led to the ability to perform space efficient, approximate counting over large vocabularies in a streami... | 2009 | 31 |
3,779 | Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution Cosmin Adrian Bejan1, Matthew Titsworth2, Andrew Hickl2, & Sanda Harabagiu1 1 Human Language Technology Research Institute, University of Texas at Dallas 2 Language Computer Corporation, Richardson, Texas ady@hlt.utdallas.edu Abstra... | 2009 | 32 |
3,780 | A Stochastic approximation method for inference in probabilistic graphical models Peter Carbonetto Dept. of Human Genetics University of Chicago Chicago, IL, U.S.A. pcarbone@bsd.uchicago.edu Matthew King Dept. of Botany University of British Columbia Vancouver, B.C., Canada kingdom@interchange.ubc... | 2009 | 33 |
3,781 | Factor Modeling for Advertisement Targeting Ye Chen∗ eBay Inc. yechen1@ebay.com Michael Kapralov Stanford University kapralov@stanford.edu Dmitry Pavlov† Yandex Labs dmitry-pavlov@yandex-team.ru John F. Canny University of California, Berkeley jfc@cs.berkeley.edu Abstract We adapt a probabil... | 2009 | 34 |
3,782 | Sparse Estimation Using General Likelihoods and Non-Factorial Priors David Wipf and Srikantan Nagarajan, ∗ Biomagnetic Imaging Lab, UC San Francisco {david.wipf, sri}@mrsc.ucsf.edu Abstract Finding maximally sparse representations from overcomplete feature dictionaries frequently involves minimizing a cos... | 2009 | 35 |
3,783 | Modeling the spacing effect in sequential category learning Hongjing Lu Department of Psychology & Statistics Hongjing@ucla.edu Matthew Weiden Department of Psychology mweiden@ucla.edu Alan Yuille Department of Statistics, Computer Science & Psychology University of California, Los Angeles Los Ang... | 2009 | 36 |
3,784 | An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism Aaron C. Courville, Douglas Eck and Yoshua Bengio Department of Computer Science and Operations Research University of Montr´eal Montr´eal, Qu´ebec, Canada {courvila,eckdoug,bengioy}@iro.umontreal.ca Abstract The Indian Buffet Process is a Bayes... | 2009 | 37 |
3,785 | Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process Finale Doshi-Velez∗ University of Cambridge Cambridge, CB21PZ, UK finale@alum.mit.edu David Knowles∗ University of Cambridge Cambridge, CB21PZ, UK dak33@cam.ac.uk Shakir Mohamed∗ University of Cambridge... | 2009 | 38 |
3,786 | Matrix Completion from Power-Law Distributed Samples Raghu Meka, Prateek Jain, and Inderjit S. Dhillon Department of Computer Sciences University of Texas at Austin Austin, TX 78712 {raghu,pjain,inderjit}@cs.utexas.edu Abstract The low-rank matrix completion problem is a fundamental problem with many ... | 2009 | 39 |
3,787 | White Functionals for Anomaly Detection in Dynamical Systems Marco Cuturi ORFE - Princeton University mcuturi@princeton.edu Jean-Philippe Vert Mines ParisTech, Institut Curie, INSERM U900 Jean-Philippe.Vert@mines.org Alexandre d’Aspremont ORFE - Princeton University aspremon@princeton.edu Abstract... | 2009 | 4 |
3,788 | Filtering Abstract Senses From Image Search Results Kate Saenko1,2 and Trevor Darrell2 1 MIT CSAIL, Cambridge, MA 2 UC Berkeley EECS and ICSI, Berkeley, CA saenko@csail.mit.edu, trevor@eecs.berkeley.edu Abstract We propose an unsupervised method that, given a word, automatically selects non-abstract sense... | 2009 | 40 |
3,789 | Heavy-Tailed Symmetric Stochastic Neighbor Embedding Zhirong Yang The Chinese University of Hong Kong Helsinki University of Technology zhirong.yang@tkk.fi Irwin King The Chinese University of Hong Kong king@cse.cuhk.edu.hk Zenglin Xu The Chinese University of Hong Kong Saarland University & MPI f... | 2009 | 41 |
3,790 | Which graphical models are difficult to learn? Jos´e Bento Department of Electrical Engineering Stanford University jbento@stanford.edu Andrea Montanari Department of Electrical Engineering and Department of Statistics Stanford University montanari@stanford.edu Abstract We consider the problem of l... | 2009 | 42 |
3,791 | Information-theoretic lower bounds on the oracle complexity of convex optimization Alekh Agarwal Computer Science Division UC Berkeley alekh@cs.berkeley.edu Peter Bartlett Computer Science Division Department of Statistics UC Berkeley bartlett@cs.berkeley.edu Pradeep Ravikumar Department of Comp... | 2009 | 43 |
3,792 | Linear-time Algorithms for Pairwise Statistical Problems Parikshit Ram, Dongryeol Lee, William B. March and Alexander G. Gray Computational Science and Engineering, Georgia Institute of Technology Atlanta, GA 30332 {p.ram@,dongryel@cc.,march@,agray@cc.}gatech.edu Abstract Several key computational bottlen... | 2009 | 44 |
3,793 | From PAC-Bayes Bounds to KL Regularization Pascal Germain, Alexandre Lacasse, Franc¸ois Laviolette, Mario Marchand, Sara Shanian Department of Computer Science and Software Engineering Laval University, Qu´ebec (QC), Canada firstname.secondname@ift.ulaval.ca Abstract We show that convex KL-regularized objec... | 2009 | 45 |
3,794 | On Stochastic and Worst-case Models for Investing Elad Hazan IBM Almaden Research Center 650 Harry Rd, San Jose, CA 95120 ehazan@cs.princeton.edu Satyen Kale Yahoo! Research 4301 Great America Parkway, Santa Clara, CA 95054 skale@yahoo-inc.com Abstract In practice, most investing is done assuming a ... | 2009 | 46 |
3,795 | Structured output regression for detection with partial truncation Andrea Vedaldi Andrew Zisserman Department of Engineering University of Oxford Oxford, UK {vedaldi,az}@robots.ox.ac.uk Abstract We develop a structured output model for object category detection that explicitly accounts for alignment... | 2009 | 47 |
3,796 | On Invariance in Hierarchical Models Jake Bouvrie, Lorenzo Rosasco, and Tomaso Poggio Center for Biological and Computational Learning Massachusetts Institute of Technology Cambridge, MA USA {jvb,lrosasco}@mit.edu, tp@ai.mit.edu Abstract A goal of central importance in the study of hierarchical models for... | 2009 | 48 |
3,797 | Submanifold density estimation Arkadas Ozakin Georgia Tech Research Institute Georgia Insitute of Technology arkadas.ozakin@gtri.gatech.edu Alexander Gray College of Computing Georgia Institute of Technology agray@cc.gatech.edu Abstract Kernel density estimation is the most widely-used practical met... | 2009 | 49 |
3,798 | Learning models of object structure Joseph Schlecht Department of Computer Science University of Arizona schlecht@cs.arizona.edu Kobus Barnard Department of Computer Science University of Arizona kobus@cs.arizona.edu Abstract We present an approach for learning stochastic geometric models of object ... | 2009 | 5 |
3,799 | Nonlinear Learning using Local Coordinate Coding Kai Yu NEC Laboratories America kyu@sv.nec-labs.com Tong Zhang Rutgers University tzhang@stat.rutgers.edu Yihong Gong NEC Laboratories America ygong@sv.nec-labs.com Abstract This paper introduces a new method for semi-supervised learning on high dim... | 2009 | 50 |
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