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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4,600 | Learning from Distributions via Support Measure Machines Krikamol Muandet MPI for Intelligent Systems, T¨ubingen krikamol@tuebingen.mpg.de Kenji Fukumizu The Institute of Statistical Mathematics, Tokyo fukumizu@ism.ac.jp Francesco Dinuzzo MPI for Intelligent Systems, T¨ubingen fdinuzzo@tuebingen.mpg... | 2012 | 233 |
4,601 | Learning Multiple Tasks using Shared Hypotheses Koby Crammer Department of Electrical Enginering The Technion - Israel Institute of Technology Haifa, 32000 Israel koby@ee.technion.ac.il Yishay Mansour School of Computer Science Tel Aviv University Tel - Aviv 69978 mansour@tau.ac.il Abstract In t... | 2012 | 234 |
4,602 | Minimizing Uncertainty in Pipelines∗ Nilesh Dalvi Facebook, Inc. nileshd@fb.com Aditya Parameswaran Stanford University adityagp@cs.stanford.edu Vibhor Rastogi Google, Inc. vibhor.rastogi@gmail.com Abstract In this paper, we consider the problem of debugging large pipelines by human labeling. We... | 2012 | 235 |
4,603 | Algorithms for Learning Markov Field Policies Abdeslam Boularias Max Planck Institute for Intelligent Systems boularias@tuebingen.mpg.de Oliver Kr¨omer, Jan Peters Technische Universit¨at Darmstadt {oli,jan}@robot-learning.de Abstract We use a graphical model for representing policies in Markov Decision... | 2012 | 237 |
4,604 | Bayesian Probabilistic Co-Subspace Addition Lei Shi Baidu.com, Inc shilei06@baidu.com Abstract For modeling data matrices, this paper introduces Probabilistic Co-Subspace Addition (PCSA) model by simultaneously capturing the dependent structures among both rows and columns. Briefly, PCSA assumes that each en... | 2012 | 238 |
4,605 | Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress Manuel Lopes INRIA Bordeaux, France Tobias Lang FU Berlin Germany Marc Toussaint FU Berlin Germany Pierre-Yves Oudeyer INRIA Bordeaux, France Abstract Formal exploration approaches in model-based ... | 2012 | 239 |
4,606 | Learning visual motion in recurrent neural networks Marius Pachitariu, Maneesh Sahani Gatsby Computational Neuroscience Unit University College London, UK {marius, maneesh}@gatsby.ucl.ac.uk Abstract We present a dynamic nonlinear generative model for visual motion based on a latent representation of binar... | 2012 | 24 |
4,607 | From Deformations to Parts: Motion-based Segmentation of 3D Objects Soumya Ghosh1, Erik B. Sudderth1, Matthew Loper2, and Michael J. Black2 1Department of Computer Science, Brown University, {sghosh,sudderth}@cs.brown.edu 2Perceiving Systems Department, Max Planck Institute for Intelligent Systems, {mloper,bl... | 2012 | 240 |
4,608 | Bayesian models for Large-scale Hierarchical Classification Siddharth Gopal Yiming Yang sgopal1@andrew.cmu.edu yiming@cs.cmu.edu Carnegie Mellon University Bing Bai Alexandru Niculescu-Mizil {bing,alex}@nec-labs.com NEC Laboratories America, Princeton Abstract A challenging problem in hierarchical ... | 2012 | 241 |
4,609 | Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model Sander M. Bohte CWI, Life Sciences Amsterdam, The Netherlands S.M.Bohte@cwi.nl Abstract Neural adaptation underlies the ability of neurons to maximize encoded information over a wide dynamic range of input stimuli. Recent spiki... | 2012 | 242 |
4,610 | Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach Dijun Luo, Chris Ding, Heng Huang, Feiping Nie Department of Computer Science and Engineering The University of Texas at Arlington dijun.luo@gmail.com, chqding@uta.edu heng@uta.edu, feipingnie@gmail.com Abstract In many grap... | 2012 | 243 |
4,611 | Random Utility Theory for Social Choice Hossein Azari Soufiani SEAS, Harvard University azari@fas.harvard.edu David C. Parkes SEAS, Harvard University parkes@eecs.harvard.edu Lirong Xia SEAS, Harvard University lxia@seas.harvard.edu Abstract Random utility theory models an agent’s preferences on al... | 2012 | 244 |
4,612 | Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs Shay B. Cohen and Michael Collins Department of Computer Science Columbia University New York, NY 10027 scohen,mcollins@cs.columbia.edu Abstract We describe an approach to speed-up inference with latent-variable PCFGs, which have been sh... | 2012 | 245 |
4,613 | Action-Model Based Multi-agent Plan Recognition Hankz Hankui Zhuo Department of Computer Science Sun Yat-sen University, Guangzhou, China 510006 zhuohank@mail.sysu.edu.cn Qiang Yang Huawei Noah’s Ark Research Lab Core Building 2, Hong Kong Science Park, Shatin, Hong Kong qyang@cse.ust.hk Subbarao Kamb... | 2012 | 246 |
4,614 | Semiparametric Principal Component Analysis Fang Han Department of Biostatistics Johns Hopkins University Baltimore, MD 21210 fhan@jhsph.edu Han Liu Department of Operations Research and Financial Engineering Princeton University, NJ 08544 hanliu@princeton.edu Abstract We propose two new princip... | 2012 | 247 |
4,615 | Multi-task Vector Field Learning 1Binbin Lin 2Sen Yang 1Chiyuan Zhang 2Jieping Ye 1Xiaofei He 1State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, China {binbinlinzju, chiyuan.zhang.zju, xiaofeihe}@gmail.com 2The Biodesign Institute, Arizona State University, Tempe, AZ, 85287 {senyang, jiep... | 2012 | 248 |
4,616 | Local Supervised Learning through Space Partitioning Joseph Wang Dept. of Electrical and Computer Engineering Boston University Boston, MA 02116 joewang@bu.edu Venkatesh Saligrama Dept. of Electrical and Computer Engineering Boston University Boston, MA 02116 srv@bu.edu Abstract We develop a n... | 2012 | 249 |
4,617 | Graphical Models via Generalized Linear Models Eunho Yang Department of Computer Science University of Texas at Austin eunho@cs.utexas.edu Pradeep Ravikumar Department of Computer Science University of Texas at Austin pradeepr@cs.utexas.edu Genevera I. Allen Department of Statistics Rice Universit... | 2012 | 25 |
4,618 | Dip-means: an incremental clustering method for estimating the number of clusters Argyris Kalogeratos Department of Computer Science University of Ioannina Ioannina, Greece 45110 akaloger@cs.uoi.gr Aristidis Likas Department of Computer Science University of Ioannina Ioannina, Greece 45110 arly@cs... | 2012 | 250 |
4,619 | Unsupervised Template Learning for Fine-Grained Object Recognition Shulin Yang University of Washington, Seattle, WA 98195 yang@cs.washington.edu Liefeng Bo ISTC-PC Intel labs, Seattle, WA 98195 liefeng.bo@intel.com Jue Wang Adobe ATL Labs, Seattle, WA 98103 juewang@adobe.com Linda Shapiro Unive... | 2012 | 251 |
4,620 | A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function Pedro A. Ortega Max Planck Institute for Intelligent Systems Max Planck Institute for Biolog. Cybernetics pedro.ortega@tuebingen.mpg.de Jordi Grau-Moya Max Planck Institute for Intelligent Systems Max Planck Ins... | 2012 | 252 |
4,621 | Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems Morteza Ibrahimi Stanford University Stanford, CA 94305 ibrahimi@stanford.edu Adel Javanmard Stanford University Stanford, CA 94305 adelj@stanford.edu Benjamin Van Roy Stanford University Stanford, CA 94305 bvr@stanf... | 2012 | 253 |
4,622 | A Conditional Multinomial Mixture Model for Superset Label Learning Li-Ping Liu EECS, Oregon State University Corvallis, OR 97331 liuli@eecs.oregonstate.edu Thomas G. Dietterich EECS, Oregon State University Corvallis, OR 97331 tgd@cs.orst.edu Abstract In the superset label learning problem (SLL),... | 2012 | 254 |
4,623 | Value Pursuit Iteration Amir-massoud Farahmand∗ Doina Precup † School of Computer Science, McGill University, Montreal, Canada Abstract Value Pursuit Iteration (VPI) is an approximate value iteration algorithm that finds a close to optimal policy for reinforcement learning problems with large state spaces.... | 2012 | 255 |
4,624 | Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning Matthew Der and Lawrence K. Saul Department of Computer Science and Engineering University of California, San Diego La Jolla, CA 92093 {mfder,saul}@cs.ucsd.edu Abstract We describe a latent variable model for supervised di... | 2012 | 256 |
4,625 | Identification of Recurrent Patterns in the Activation of Brain Networks Firdaus Janoos∗ Weichang Li Niranjan Subrahmanya ExxonMobil Corporate Strategic Research Annandale, NJ 08801 Istv´an ´A. M´orocz William M. Wells (III) Harvard Medical School Boston, MA 02115 Abstract Identifying patterns fr... | 2012 | 257 |
4,626 | To appear in: Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada. December 3-6, 2012. Hierarchical spike coding of sound Yan Karklin∗ Howard Hughes Medical Institute, Center for Neural Science New York University yan.karklin@nyu.edu Chaitanya Ekanadham∗ Courant Institute of Mathematical ... | 2012 | 258 |
4,627 | A Polynomial-time Form of Robust Regression Yaoliang Yu, ¨Ozlem Aslan and Dale Schuurmans Department of Computing Science, University of Alberta, Edmonton AB T6G 2E8, Canada {yaoliang,ozlem,dale}@cs.ualberta.ca Abstract Despite the variety of robust regression methods that have been developed, current regress... | 2012 | 259 |
4,628 | Searching for objects driven by context Bogdan Alexe BIWI ETH Zurich Nicolas Heess Gatsby Unit UCL Yee Whye Teh Department of Statistics University of Oxford Vittorio Ferrari School of Informatics University of Edinburgh Abstract The dominant visual search paradigm for object class detection... | 2012 | 26 |
4,629 | Multimodal Learning with Deep Boltzmann Machines Nitish Srivastava Department of Computer Science University of Toronto nitish@cs.toronto.edu Ruslan Salakhutdinov Department of Statistics and Computer Science University of Toronto rsalakhu@cs.toronto.edu Abstract A Deep Boltzmann Machine is describe... | 2012 | 260 |
4,630 | A nonparametric variable clustering model Konstantina Palla∗ University of Cambridge kp376@cam.ac.uk David A. Knowles∗ Stanford University davidknowles@cs.stanford.edu Zoubin Ghahramani University of Cambridge zoubin@eng.cam.ac.uk Abstract Factor analysis models effectively summarise the covarianc... | 2012 | 261 |
4,631 | Transelliptical Graphical Models Han Liu Department of Operations Research and Financial Engineering Princeton University, NJ 08544 hanliu@princeton.edu Fang Han Department of Biostatistics Johns Hopkins University Baltimore, MD 21210 fhan@jhsph.edu Cun-hui Zhang Department of Statistics Rutge... | 2012 | 262 |
4,632 | Collaborative Gaussian Processes for Preference Learning Neil Houlsby ∗ Department of Engineering University of Cambridge Jose Miguel Hern´andez-Lobato ∗ Department of Engineering University of Cambridge Ferenc Husz´ar Department of Engineering University of Cambridge Zoubin Ghahramani Departmen... | 2012 | 263 |
4,633 | Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation Tuo Zhao Department of Computer Science Johns Hopkins University Kathryn Roeder Department of Statistics Carnegie Mellon University Han Liu Department of Operations Research and Financial Engineering Princeton ... | 2012 | 264 |
4,634 | Learning Manifolds with K-Means and K-Flats Guillermo D. Canas⋆,† Tomaso Poggio⋆,† Lorenzo A. Rosasco⋆,† ⋆Laboratory for Computational and Statistical Learning - MIT-IIT † CBCL, McGovern Institute - Massachusetts Institute of Technology guilledc@mit.edu tp@ai.mit.edu lrosasco@mit.edu Abstract We stu... | 2012 | 265 |
4,635 | Newton-Like Methods for Sparse Inverse Covariance Estimation Peder A. Olsen IBM, T. J. Watson Research Center pederao@us.ibm.com Figen Oztoprak Sabanci University figen@sabanciuniv.edu Jorge Nocedal Northwestern University nocedal@eecs.northwestern.edu Steven J. Rennie IBM, T. J. Watson Research... | 2012 | 266 |
4,636 | A Neural Autoregressive Topic Model Hugo Larochelle D´epartement d’informatique Universit´e de Sherbrooke hugo.larochelle@usherbrooke.ca Stanislas Lauly D´epartement d’informatique Universit´e de Sherbrooke stanislas.lauly@usherbrooke.ca Abstract We describe a new model for learning meaningful repre... | 2012 | 267 |
4,637 | Active Learning of Multi-Index Function Models Hemant Tyagi and Volkan Cevher LIONS – EPFL Abstract We consider the problem of actively learning multi-index functions of the form f(x) = g(Ax) = Pk i=1 gi(aT i x) from point evaluations of f. We assume that the function f is defined on an ℓ2-ball in Rd, g ... | 2012 | 268 |
4,638 | Probabilistic Low-Rank Subspace Clustering S. Derin Babacan University of Illinois at Urbana-Champaign Urbana, IL 61801, USA dbabacan@gmail.com Shinichi Nakajima Nikon Corporation Tokyo, 140-8601, Japan nakajima.s@nikon.co.jp Minh N. Do University of Illinois at Urbana-Champaign Urbana, IL 61801, ... | 2012 | 269 |
4,639 | Learning Mixtures of Tree Graphical Models Animashree Anandkumar UC Irvine a.anandkumar@uci.edu Daniel Hsu Microsoft Research New England dahsu@microsoft.com Furong Huang UC Irvine furongh@uci.edu Sham M. Kakade Microsoft Research New England skakade@microsoft.com Abstract We consider unsupe... | 2012 | 27 |
4,640 | Fully Bayesian inference for neural models with negative-binomial spiking Jonathan W. Pillow Center for Perceptual Systems Department of Psychology The University of Texas at Austin pillow@mail.utexas.edu James G. Scott Division of Statistics and Scientific Computation McCombs School of Business The ... | 2012 | 270 |
4,641 | Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting Amadou Ba IBM Research - Ireland Mulhuddart, Dublin 15 amadouba@ie.ibm.com Mathieu Sinn IBM Research - Ireland Mulhuddart, Dublin 15 mathsinn@ie.ibm.com Yannig Goude EDF R&D Clamart, France yannig.goude@edf.f... | 2012 | 271 |
4,642 | Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models Jeff Beck Department of Brain and Cognitive Sciences University of Rochester jbeck@bcs.rochester.edu Katherine Heller Department of Statistical Science Duke University kheller@stat.duke.edu Alexandre Pouget D... | 2012 | 272 |
4,643 | Clustering by Nonnegative Matrix Factorization Using Graph Random Walk Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen and Erkki Oja Department of Information and Computer Science Aalto University, 00076, Finland {zhirong.yang,tele.hao,onur.dikmen,xi.chen,erkki.oja}@aalto.fi Abstract Nonnegative Matrix Facto... | 2012 | 273 |
4,644 | Graphical Gaussian Vector for Image Categorization Tatsuya Harada The University of Tokyo/JST PRESTO 7-3-1 Hongo Bunkyo-ku, Tokyo Japan harada@isi.imi.i.u-tokyo.ac.jp Yasuo Kuniyoshi The University of Tokyo 7-3-1 Hongo Bunkyo-ku, Tokyo Japan kuniyosh@isi.imi.i.u-tokyo.ac.jp Abstract This paper propo... | 2012 | 274 |
4,645 | Convergence Rate Analysis of MAP Coordinate Minimization Algorithms Ofer Meshi ∗ meshi@cs.huji.ac.il Tommi Jaakkola † tommi@csail.mit.edu Amir Globerson ∗ gamir@cs.huji.ac.il Abstract Finding maximum a posteriori (MAP) assignments in graphical models is an important task in many applications. Since th... | 2012 | 275 |
4,646 | Distributed Probabilistic Learning for Camera Networks with Missing Data Sejong Yoon Department of Computer Science Rutgers University sjyoon@cs.rutgers.edu Vladimir Pavlovic Department of Computer Science Rutgers University vladimir@cs.rutgers.edu Abstract Probabilistic approaches to computer vis... | 2012 | 276 |
4,647 | Confusion-Based Online Learning and a Passive-Aggressive Scheme Liva Ralaivola QARMA, Laboratoire d’Informatique Fondamentale de Marseille Aix-Marseille University, France liva.ralaivola@lif.univ-mrs.fr Abstract This paper provides the first —to the best of our knowledge— analysis of online learning algo... | 2012 | 277 |
4,648 | Context-Sensitive Decision Forests for Object Detection Peter Kontschieder1 Samuel Rota Bul`o2 Antonio Criminisi3 Pushmeet Kohli3 Marcello Pelillo2 Horst Bischof1 1ICG, Graz University of Technology, Austria 2DAIS, Universit`a Ca’ Foscari Venezia, Italy 3Microsoft Research Cambridge, UK Abstract ... | 2012 | 278 |
4,649 | Approximating Concavely Parameterized Optimization Problems Joachim Giesen Friedrich-Schiller-Universit¨at Jena Germany joachim.giesen@uni-jena.de S¨oren Laue Friedrich-Schiller-Universit¨at Jena Germany soeren.laue@uni-jena.de Jens K. Mueller Friedrich-Schiller-Universit¨at Jena Germany jkm@i... | 2012 | 279 |
4,650 | Convex Multi-view Subspace Learning Martha White, Yaoliang Yu, Xinhua Zhang∗and Dale Schuurmans Department of Computing Science, University of Alberta, Edmonton AB T6G 2E8, Canada {whitem,yaoliang,xinhua2,dale}@cs.ualberta.ca Abstract Subspace learning seeks a low dimensional representation of data that enabl... | 2012 | 28 |
4,651 | Kernel Latent SVM for Visual Recognition Weilong Yang School of Computing Science Simon Fraser University wya16@sfu.ca Yang Wang Department of Computer Science University of Manitoba ywang@cs.umanitoba.ca Arash Vahdat School of Computing Science Simon Fraser University avahdat@sfu.ca Greg Mori... | 2012 | 280 |
4,652 | A Linear Time Active Learning Algorithm for Link Classification∗ Nicol`o Cesa-Bianchi Dipartimento di Informatica Universit`a degli Studi di Milano, Italy Claudio Gentile Dipartimento di Scienze Teoriche ed Applicate Universit`a dell’Insubria, Italy Fabio Vitale Dipartimento di Informatica Universit`... | 2012 | 281 |
4,653 | A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling Pieter-Jan Kindermans, Hannes Verschore, David Verstraeten and Benjamin Schrauwen Ghent University, Electronics and Information Systems Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium PieterJan.Kindermans@UGent.be Abstract ... | 2012 | 282 |
4,654 | Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction Christoph H. Lampert IST Austria (Institute of Science and Technology Austria) Am Campus 1, 3400 Klosterneuburg, Austria http://www.ist.ac.at/∼chl chl@ist.ac.at Abstract We study the problem of maximum marginal prediction (MMP) in probabil... | 2012 | 283 |
4,655 | Locally Uniform Comparison Image Descriptor Andrew Ziegler∗Eric Christiansen David Kriegman Serge Belongie Department of Computer Science and Engineering, University of California, San Diego amz@gatech.edu, {echristiansen, kriegman, sjb}@cs.ucsd.edu Abstract Keypoint matching between pairs of images using... | 2012 | 284 |
4,656 | Priors for Diversity in Generative Latent Variable Models James Y. Zou School of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 jzou@fas.harvard.edu Ryan P. Adams School of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 rpa@seas.harvard.edu Abstr... | 2012 | 285 |
4,657 | Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions Mathieu Sinn IBM Research - Ireland Mulhuddart, Dublin 15 mathsinn@ie.ibm.com Bei Chen McMaster University Hamilton, Ontario, Canada bei.chen@math.mcmaster.ca Abstract Conditional Markov Chains (also known as Linear-Ch... | 2012 | 286 |
4,658 | ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto kriz@cs.utoronto.ca Ilya Sutskever University of Toronto ilya@cs.utoronto.ca Geoffrey E. Hinton University of Toronto hinton@cs.utoronto.ca Abstract We trained a large, deep convolutional neural ... | 2012 | 287 |
4,659 | Stochastic Gradient Descent with Only One Projection Mehrdad Mahdavi†, Tianbao Yang‡, Rong Jin†, Shenghuo Zhu⋆, and Jinfeng Yi† †Dept. of Computer Science and Engineering, Michigan State University, MI, USA ‡Machine Learning Lab, GE Global Research, CA, USA ⋆NEC Laboratories America, CA, USA †{mahdavim,rong... | 2012 | 288 |
4,660 | Learning Probability Measures with Respect to Optimal Transport Metrics Guillermo D. Canas⋆,† Lorenzo A. Rosasco⋆,† ⋆Laboratory for Computational and Statistical Learning - MIT-IIT † CBCL, McGovern Institute - Massachusetts Institute of Technology {guilledc,lrosasco}@mit.edu Abstract We study the proble... | 2012 | 289 |
4,661 | Learning Invariant Representations of Molecules for Atomization Energy Prediction Grégoire Montavon1∗, Katja Hansen2, Siamac Fazli1, Matthias Rupp3, Franziska Biegler1, Andreas Ziehe1, Alexandre Tkatchenko2, O. Anatole von Lilienfeld4, Klaus-Robert Müller1,5† 1. Machine Learning Group, TU Berlin 2. Fritz-Habe... | 2012 | 29 |
4,662 | Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data Michael C. Hughes1, Emily B. Fox2, and Erik B. Sudderth1 1Department of Computer Science, Brown University, {mhughes,sudderth}@cs.brown.edu 2Department of Statistics, University of Washington, ebfox@stat.washington.edu Abstr... | 2012 | 290 |
4,663 | Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization Stephen H. Bach University of Maryland, College Park College Park, MD 20742 bach@cs.umd.edu Matthias Broecheler Aurelius LLC matthias@thinkaurelius.com Lise Getoor University of Maryland, College Park ... | 2012 | 291 |
4,664 | A systematic approach to extracting semantic information from functional MRI data Francisco Pereira Siemens Corporation, Corporate Technology Princeton, NJ 08540 francisco.pereira@gmail.com Matthew Botvinick Princeton Neuroscience Institute and Department of Psychology Princeton University Princeton N... | 2012 | 292 |
4,665 | Sketch-Based Linear Value Function Approximation Marc G. Bellemare University of Alberta mg17@cs.ualberta.ca Joel Veness University of Alberta veness@cs.ualberta.ca Michael Bowling University of Alberta bowling@cs.ualberta.ca Abstract Hashing is a common method to reduce large, potentially infinite... | 2012 | 293 |
4,666 | Unsupervised Structure Discovery for Semantic Analysis of Audio Sourish Chaudhuri Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213 sourishc@cs.cmu.edu Bhiksha Raj Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213 bhiksha@cs.cmu.edu Abs... | 2012 | 294 |
4,667 | The Time-Marginalized Coalescent Prior for Hierarchical Clustering Levi Boyles Department of Computer Science University of California, Irvine Irvine, CA 92617 lboyles@uci.edu Max Welling Department of Computer Science University of California, Irvine Irvine, CA 92617 welling@uci.edu Abstract ... | 2012 | 295 |
4,668 | Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction Minjie Xu, Jun Zhu and Bo Zhang State Key Laboratory of Intelligent Technology and Systems (LITS) Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computer Science and Technology, Tsinghua Univ... | 2012 | 296 |
4,669 | Exponential Concentration for Mutual Information Estimation with Application to Forests Han Liu Department of Operations Research and Financial Engineering Princeton University, NJ 08544 hanliu@princeton.edu John Lafferty Department of Computer Science Department of Statistics University of Chicago,... | 2012 | 297 |
4,670 | Slice Normalized Dynamic Markov Logic Networks Tivadar Papai Henry Kautz Daniel Stefankovic Department of Computer Science University of Rochester Rochester, NY 14627 {papai,kautz,stefanko}@cs.rochester.edu Abstract Markov logic is a widely used tool in statistical relational learning, which uses a ... | 2012 | 298 |
4,671 | Continuous Relaxations for Discrete Hamiltonian Monte Carlo Yichuan Zhang, Charles Sutton, Amos Storkey School of Informatics University of Edinburgh United Kingdom Y.Zhang-60@sms.ed.ac.uk, csutton@inf.ed.ac.uk, a.storkey@ed.ac.uk Zoubin Ghahramani Department of Engineering University of Cambridge... | 2012 | 299 |
4,672 | FastEx: Hash Clustering with Exponential Families Amr Ahmed∗ Research at Google, Mountain View, CA amra@google.com Sujith Ravi Research at Google, Mountain View, CA sravi@google.com Shravan M. Narayanamurthy Microsoft Research, Bangalore, India shravanmn@gmail.com Alexander J. Smola Research at Go... | 2012 | 3 |
4,673 | On Multilabel Classification and Ranking with Partial Feedback Claudio Gentile DiSTA, Universit`a dell’Insubria, Italy claudio.gentile@uninsubria.it Francesco Orabona TTI Chicago, USA francesco@orabona.com Abstract We present a novel multilabel/ranking algorithm working in partial information setting... | 2012 | 30 |
4,674 | Learning with Target Prior Zuoguan Wang Dept. of ECSE, Rensselaer Polytechnic Inst. Troy, NY 12180 wangz6@rpi.edu Siwei Lyu Computer Science, Univ. at Albany, SUNY Albany, NY 12222 lsw@cs.albany.edu Gerwin Schalk Wadsworth Center, NYS Dept. of Health Albany, NY, 12201 schalk@wadsworth.org Qian... | 2012 | 300 |
4,675 | Generalization Bounds for Domain Adaptation Chao Zhang1, Lei Zhang2, Jieping Ye1,3 1Center for Evolutionary Medicine and Informatics, The Biodesign Institute, and 3Computer Science and Engineering, Arizona State University, Tempe, USA {czhan117,jieping.ye}@asu.edu 2School of Computer Science and Technolog... | 2012 | 301 |
4,676 | Multiplicative Forests for Continuous-Time Processes Jeremy C. Weiss University of Wisconsin Madison, WI 53706, USA jcweiss@cs.wisc.edu Sriraam Natarajan Wake Forest University Winston Salem, NC 27157, USA snataraj@wakehealth.edu David Page University of Wisconsin Madison, WI 53706, USA page@bio... | 2012 | 302 |
4,677 | Variational Inference for Crowdsourcing Qiang Liu ICS, UC Irvine qliu1@ics.uci.edu Jian Peng TTI-C & CSAIL, MIT jpeng@csail.mit.edu Alexander Ihler ICS, UC Irvine ihler@ics.uci.edu Abstract Crowdsourcing has become a popular paradigm for labeling large datasets. However, it has given rise to the c... | 2012 | 303 |
4,678 | Why MCA? Nonlinear sparse coding with spike-andslab prior for neurally plausible image encoding Jacquelyn A. Shelton, Philip Sterne, J¨org Bornschein, Abdul-Saboor Sheikh, Frankfurt Institute for Advanced Studies Goethe-University Frankfurt, Germany {shelton, sterne, bornschein, sheikh}@fias.uni-frankfurt... | 2012 | 304 |
4,679 | Tractable Objectives for Robust Policy Optimization Katherine Chen University of Alberta kchen4@ualberta.ca Michael Bowling University of Alberta bowling@cs.ualberta.ca Abstract Robust policy optimization acknowledges that risk-aversion plays a vital role in real-world decision-making. When faced with... | 2012 | 305 |
4,680 | Multiple Choice Learning: Learning to Produce Multiple Structured Outputs Abner Guzman-Rivera University of Illinois aguzman5@illinois.edu Dhruv Batra Virginia Tech dbatra@vt.edu Pushmeet Kohli Microsoft Research Cambridge pkohli@microsoft.com Abstract We address the problem of generating multip... | 2012 | 306 |
4,681 | Robustness and risk-sensitivity in Markov decision processes Takayuki Osogami IBM Research - Tokyo 5-6-52 Toyosu, Koto-ku, Tokyo, Japan osogami@jp.ibm.com Abstract We uncover relations between robust MDPs and risk-sensitive MDPs. The objective of a robust MDP is to minimize a function, such as the expecta... | 2012 | 307 |
4,682 | Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization Konstantinos I. Tsianos, Sean Lawlor, and Michael G. Rabbat Department of Electrical and Computer Engineering McGill University, Montr´eal, Canada {konstantinos.tsianos, sean.lawlor}@mail.mcgill.ca michael.rabbat@mcgill.ca Abs... | 2012 | 308 |
4,683 | A provably efficient simplex algorithm for classification Elad Hazan ∗ Technion - Israel Inst. of Tech. Haifa, 32000 ehazan@ie.technion.ac.il Zohar Karnin Yahoo! Research Haifa zkarnin@ymail.com Abstract We present a simplex algorithm for linear programming in a linear classification formulation. T... | 2012 | 309 |
4,684 | Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling Antonino Freno Mikaela Keller∗ Marc Tommasi∗ INRIA Lille – Nord Europe 40 avenue Halley – Bˆat A – Park Plaza 59650 Villeneuve d’Ascq (France) {antonino.freno, mikaela.keller, marc.tommasi}@inria.fr Abstract Stati... | 2012 | 310 |
4,685 | Majorization for CRFs and Latent Likelihoods Tony Jebara Department of Computer Science Columbia University jebara@cs.columbia.edu Anna Choromanska Department of Electrical Engineering Columbia University aec2163@columbia.edu Abstract The partition function plays a key role in probabilistic modeling... | 2012 | 311 |
4,686 | Feature-aware Label Space Dimension Reduction for Multi-label Classification Yao-Nan Chen Department of Computer Science & Information Engineering, National Taiwan University r99922008@csie.ntu.edu.tw Hsuan-Tien Lin Department of Computer Science & Information Engineering, National Taiwan University ... | 2012 | 312 |
4,687 | Semantic Kernel Forests from Multiple Taxonomies Sung Ju Hwang University of Texas Austin, TX 78701 sjhwang@cs.utexas.edu Kristen Grauman University of Texas Austin, TX 78701 grauman@cs.utexas.edu Fei Sha University of Southern California Los Angeles, CA 90089 feisha@usc.edu Abstract When le... | 2012 | 313 |
4,688 | Spectral learning of linear dynamics from generalised-linear observations with application to neural population data Lars Buesing∗, Jakob H. Macke∗,† , Maneesh Sahani Gatsby Computational Neuroscience Unit University College London, London, UK {lars, jakob, maneesh}@gatsby.ucl.ac.uk Abstract Latent line... | 2012 | 314 |
4,689 | Assessing Blinding in Clinical Trials Ognjen Arandjelovi´c Deakin University, Australia Abstract The interaction between the patient’s expected outcome of an intervention and the inherent effects of that intervention can have extraordinary effects. Thus in clinical trials an effort is made to conceal the na... | 2012 | 315 |
4,690 | Scalable Inference of Overlapping Communities Prem Gopalan David Mimno Sean M. Gerrish Michael J. Freedman David M. Blei {pgopalan,mimno,sgerrish,mfreed,blei}@cs.princeton.edu Department of Computer Science Princeton University Princeton, NJ 08540 Abstract We develop a scalable algorithm for poste... | 2012 | 316 |
4,691 | Learning Networks of Heterogeneous Influence Nan Du∗ Le Song∗ Alex Smola† Ming Yuan∗ Georgia Institute of Technology∗, Google Research† dunan@gatech.edu lsong@cc.gatech.edu alex@smola.org myuan@isye.gatech.edu Abstract Information, disease, and influence diffuse over networks of entities in both n... | 2012 | 317 |
4,692 | Learning to Align from Scratch Gary B. Huang1 Marwan A. Mattar1 Honglak Lee2 Erik Learned-Miller1 1University of Massachusetts, Amherst, MA {gbhuang,mmattar,elm}@cs.umass.edu 2University of Michigan, Ann Arbor, MI honglak@eecs.umich.edu Abstract Unsupervised joint alignment of images has been demons... | 2012 | 318 |
4,693 | Bayesian Warped Gaussian Processes Miguel L´azaro-Gredilla Dept. Signal Processing & Communications Universidad Carlos III de Madrid - Spain miguel@tsc.uc3m.es Abstract Warped Gaussian processes (WGP) [1] model output observations in regression tasks as a parametric nonlinear transformation of a Gaussian ... | 2012 | 319 |
4,694 | Distributed Non-Stochastic Experts Varun Kanade∗ UC Berkeley vkanade@eecs.berkeley.edu Zhenming Liu† Princeton University zhenming@cs.princeton.edu Boˇzidar Radunovi´c Microsoft Research bozidar@microsoft.com Abstract We consider the online distributed non-stochastic experts problem, where the dis... | 2012 | 32 |
4,695 | Affine Independent Variational Inference Edward Challis David Barber Department of Computer Science University College London, UK {edward.challis,david.barber}@cs.ucl.ac.uk Abstract We consider inference in a broad class of non-conjugate probabilistic models based on minimising the Kullback-Leibler diver... | 2012 | 320 |
4,696 | Submodular-Bregman and the Lov´asz-Bregman Divergences with Applications Rishabh Iyer Department of Electrical Engineering University of Washington rkiyer@u.washington.edu Jeff Bilmes Department of Electrical Engineering University of Washington bilmes@uw.edu Abstract We introduce a class of discr... | 2012 | 321 |
4,697 | Optimal kernel choice for large-scale two-sample tests Arthur Gretton,1,3 Bharath Sriperumbudur,1 Dino Sejdinovic,1 Heiko Strathmann2 1Gatsby Unit and 2CSD, CSML, UCL, UK; 3MPI for Intelligent Systems, Germany {arthur.gretton,bharat.sv,dino.sejdinovic,heiko.strathmann}@gmail Sivaraman Balakrishnan LTI, CMU, U... | 2012 | 322 |
4,698 | Entangled Monte Carlo Seong-Hwan Jun Liangliang Wang Alexandre Bouchard-Cˆot´e Department of Statistics University of British Columbia {seong.jun, l.wang, bouchard}@stat.ubc.ca Abstract We propose a novel method for scalable parallelization of SMC algorithms, Entangled Monte Carlo simulation (EMC). EMC ... | 2012 | 323 |
4,699 | Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL Nishant A. Mehta†, Dongryeol Lee∗, Alexander G. Gray† niche@cc.gatech.edu, drselee@gmail.com, agray@cc.gatech.edu † College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA ∗GE Global Research, Niskayuna, NY... | 2012 | 324 |
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