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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4,200 | Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity Nir Ailon∗ Technion, Haifa, Israel nailon@cs.technion.ac.il Abstract Given a set V of n elements we wish to linearly order them using pairwise preference labels which may be non-transitive (due to irrationality or arbitra... | 2011 | 149 |
4,201 | Hashing Algorithms for Large-Scale Learning Ping Li Cornell University pingli@cornell.edu Anshumali Shrivastava Cornell University anshu@cs.cornell.edu Joshua Moore Cornell University jlmo@cs.cornell.edu Arnd Christian K¨onig Microsoft Research chrisko@microsoft.com Abstract Minwise hashing ... | 2011 | 15 |
4,202 | Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation Onur Dikmen CNRS LTCI; T´el´ecom ParisTech 75014, Paris, France dikmen@telecom-paristech.fr C´edric F´evotte CNRS LTCI; T´el´ecom ParisTech 75014, Paris, France fevotte@telecom-paristech.fr Abstra... | 2011 | 150 |
4,203 | From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models Skander Mensi School of Computer and Communication Sciences and Brain-Mind Institute Ecole Polytechnique Federale de Lausanne 1015 Lausanne EPFL, SWITZERLAND skander.mensi@epfl.ch Richard Naud School of Computer and Communication ... | 2011 | 151 |
4,204 | Generalised Coupled Tensor Factorisation Y. Kenan Yılmaz A. Taylan Cemgil Umut S¸ims¸ekli Department of Computer Engineering Bo˘gazic¸i University, Istanbul, Turkey kenan@sibnet.com.tr, {taylan.cemgil, umut.simsekli}@boun.edu.tr Abstract We derive algorithms for generalised tensor factorisation (GTF) ... | 2011 | 152 |
4,205 | Prismatic Algorithm for Discrete D.C. Programming Problem Yoshinobu Kawahara∗and Takashi Washio The Institute of Scientific and Industrial Research (ISIR) Osaka University 8-1 Mihogaoka, Ibaraki-shi, Osaka 567-0047 JAPAN {kawahara,washio}@ar.sanken.osaka-u.ac.jp Abstract In this paper, we propose the first ... | 2011 | 153 |
4,206 | On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference Guy Van den Broeck Department of Computer Science, Katholieke Universiteit Leuven Celestijnenlaan 200A, B-3001 Heverlee, Belgium guy.vandenbroeck@cs.kuleuven.be Abstract Probabilistic logics are receiving a lot of ... | 2011 | 154 |
4,207 | Analysis and Improvement of Policy Gradient Estimation Tingting Zhao, Hirotaka Hachiya, Gang Niu, and Masashi Sugiyama Tokyo Institute of Technology {tingting@sg., hachiya@sg., gang@sg., sugiyama@}cs.titech.ac.jp Abstract Policy gradient is a useful model-free reinforcement learning approach, but it tends... | 2011 | 155 |
4,208 | On Tracking The Partition Function Guillaume Desjardins, Aaron Courville, Yoshua Bengio {desjagui,courvila,bengioy}@iro.umontreal.ca D´epartement d’informatique et de recherche op´erationnelle Universit´e de Montr´eal Abstract Markov Random Fields (MRFs) have proven very powerful both as density estimators ... | 2011 | 156 |
4,209 | Portmanteau Vocabularies for Multi-Cue Image Representation Fahad Shahbaz Khan1, Joost van de Weijer1, Andrew D. Bagdanov1,2, Maria Vanrell1 1Centre de Visio per Computador, Computer Science Department 1Universitat Autonoma de Barcelona, Edifci O, Campus UAB (Bellaterra), Barcelona, Spain 2 Media Integration ... | 2011 | 157 |
4,210 | Algorithms for Hyper-Parameter Optimization James Bergstra The Rowland Institute Harvard University bergstra@rowland.harvard.edu R´emi Bardenet Laboratoire de Recherche en Informatique Universit´e Paris-Sud bardenet@lri.fr Yoshua Bengio D´ept. d’Informatique et Recherche Op´erationelle Universit´e... | 2011 | 158 |
4,211 | Finite-Time Analysis of Stratified Sampling for Monte Carlo Alexandra Carpentier INRIA Lille - Nord Europe alexandra.carpentier@inria.fr R´emi Munos INRIA Lille - Nord Europe remi.munos@inria.fr Abstract We consider the problem of stratified sampling for Monte-Carlo integration. We model this problem ... | 2011 | 159 |
4,212 | Active learning of neural response functions with Gaussian processes Mijung Park Electrical and Computer Engineering The University of Texas at Austin mjpark@mail.utexas.edu Greg Horwitz Departments of Physiology and Biophysics The University of Washington ghorwitz@uw.edu Jonathan W. Pillow Depart... | 2011 | 16 |
4,213 | Online Submodular Set Cover, Ranking, and Repeated Active Learning Andrew Guillory Department of Computer Science University of Washington guillory@cs.washington.edu Jeff Bilmes Department of Electrical Engineering University of Washington bilmes@ee.washington.edu Abstract We propose an online pre... | 2011 | 160 |
4,214 | The Fast Convergence of Boosting Matus Telgarsky Department of Computer Science and Engineering University of California, San Diego 9500 Gilman Drive, La Jolla, CA 92093-0404 mtelgars@cs.ucsd.edu Abstract This manuscript considers the convergence rate of boosting under a large class of losses, including... | 2011 | 161 |
4,215 | See the Tree Through the Lines: The Shazoo Algorithm∗ Fabio Vitale DSI, University of Milan, Italy fabio.vitale@unimi.it Nicol`o Cesa-Bianchi DSI, University of Milan, Italy nicolo.cesa-bianchi@unimi.it Claudio Gentile DICOM, University of Insubria, Italy claudio.gentile@uninsubria.it Giovanni Zap... | 2011 | 162 |
4,216 | t-divergence Based Approximate Inference Nan Ding2, S.V. N. Vishwanathan1,2, Yuan Qi2,1 Departments of 1Statistics and 2Computer Science Purdue University ding10@purdue.edu, vishy@stat.purdue.edu, alanqi@cs.purdue.edu Abstract Approximate inference is an important technique for dealing with large, intractab... | 2011 | 163 |
4,217 | Boosting with Maximum Adaptive Sampling Charles Dubout Idiap Research Institute charles.dubout@idiap.ch Franc¸ois Fleuret Idiap Research Institute francois.fleuret@idiap.ch Abstract Classical Boosting algorithms, such as AdaBoost, build a strong classifier without concern about the computational cost. ... | 2011 | 164 |
4,218 | Inverting Grice’s Maxims to Learn Rules from Natural Language Extractions Mohammad Shahed Sorower, Thomas G. Dietterich, Janardhan Rao Doppa Walker Orr, Prasad Tadepalli, and Xiaoli Fern School of Electrical Engineering and Computer Science Oregon State University Corvallis, OR 97331 {sorower,tgd,doppa,or... | 2011 | 165 |
4,219 | Efficient anomaly detection using bipartite k-NN graphs Kumar Sricharan Department of EECS University of Michigan Ann Arbor, MI 48104 kksreddy@umich.edu Alfred O. Hero III Department of EECS University of Michigan Ann Arbor, MI 48104 hero@umich.edu Abstract Learning minimum volume sets of an un... | 2011 | 166 |
4,220 | Shallow vs. Deep Sum-Product Networks Olivier Delalleau Department of Computer Science and Operation Research Universit´e de Montr´eal delallea@iro.umontreal.ca Yoshua Bengio Department of Computer Science and Operation Research Universit´e de Montr´eal yoshua.bengio@umontreal.ca Abstract We investi... | 2011 | 167 |
4,221 | Expressive Power and Approximation Errors of Restricted Boltzmann Machines Guido F. Mont´ufar1, Johannes Rauh1, and Nihat Ay1,2 1Max Planck Institute for Mathematics in the Sciences, Inselstraße 22 04103 Leipzig, Germany 2Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA {montufar,jrauh... | 2011 | 168 |
4,222 | Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators Dominique C. Perrault-Joncas Department of Statistics University of Washington Seattle, WA 98195 dcpj@stat.washington.edu Marina Meil˘a Department of Statistics University of Washington Seattle, WA 98195 ... | 2011 | 169 |
4,223 | Nonstandard Interpretations of Probabilistic Programs for Efficient Inference David Wingate BCS / LIDS, MIT wingated@mit.edu Noah D. Goodman Psychology, Stanford ngoodman@stanford.edu Andreas Stuhlm¨uller BCS, MIT ast@mit.edu Jeffrey M. Siskind ECE, Purdue qobi@purdue.edu Abstract Probabili... | 2011 | 17 |
4,224 | Information Rates and Optimal Decoding in Large Neural Populations Kamiar Rahnama Rad Liam Paninski Department of Statistics, Columbia University {kamiar,liam}@stat.columbia.edu http://www.stat.columbia.edu/˜liam/research/pubs/kamiar-ss-info.pdf Abstract Many fundamental questions in theoretical neurosc... | 2011 | 170 |
4,225 | Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization Mark Schmidt mark.schmidt@inria.fr Nicolas Le Roux nicolas@le-roux.name Francis Bach francis.bach@ens.fr ´Ecole Normale Sup´erieure, Paris INRIA - SIERRA Project Team Abstract We consider the problem of optimizing the s... | 2011 | 171 |
4,226 | Co-Training for Domain Adaptation Minmin Chen, Kilian Q. Weinberger Department of Computer Science and Engineering Washington University in St. Louis St. Louis, MO 63130 mc15,kilian@wustl.edu John C. Blitzer Google Research 1600 Amphitheatre Parkway Mountain View, CA 94043 blitzer@google.com Abstr... | 2011 | 172 |
4,227 | Learning to Agglomerate Superpixel Hierarchies Viren Jain Janelia Farm Research Campus Howard Hughes Medical Institute Srinivas C. Turaga Brain & Cognitive Sciences Massachusetts Institute of Technology Kevin L. Briggman, Moritz N. Helmstaedter, Winfried Denk Department of Biomedical Optics Max Planck... | 2011 | 173 |
4,228 | Structured Learning for Cell Tracking Xinghua Lou, Fred A. Hamprecht Heidelberg Collaboratory for Image Processing (HCI) Interdisciplinary Center for Scientific Computing (IWR) University of Heidelberg, Heidelberg 69115, Germany {xinghua.lou,fred.hamprecht}@iwr.uni-heidelberg.de Abstract We study the probl... | 2011 | 174 |
4,229 | Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices XianXing Zhang Duke University xianxing.zhang@duke.edu David B. Dunson Duke University dunson@stat.duke.edu Lawrence Carin Duke University lcarin@ee.duke.edu Abstract The nested Chinese restaurant process is extended to d... | 2011 | 175 |
4,230 | Selecting the State-Representation in Reinforcement Learning Odalric-Ambrym Maillard INRIA Lille - Nord Europe odalricambrym.maillard@gmail.com R´emi Munos INRIA Lille - Nord Europe remi.munos@inria.fr Daniil Ryabko INRIA Lille - Nord Europe daniil@ryabko.net Abstract The problem of selecting th... | 2011 | 176 |
4,231 | A Reinforcement Learning Theory for Homeostatic Regulation Mehdi Keramati Group for Neural Theory, LNC, ENS Paris, France mohammadmahdi.keramati@ens.fr Boris Gutkin Group for Neural Theory, LNC, ENS Paris, France boris.gutkin@ens.fr Abstract Reinforcement learning models address animal’s behaviora... | 2011 | 177 |
4,232 | Semantic Labeling of 3D Point Clouds for Indoor Scenes Hema Swetha Koppula∗, Abhishek Anand∗, Thorsten Joachims, and Ashutosh Saxena Department of Computer Science, Cornell University. {hema,aa755,tj,asaxena}@cs.cornell.edu Abstract Inexpensive RGB-D cameras that give an RGB image together with depth data ... | 2011 | 178 |
4,233 | Higher-Order Correlation Clustering for Image Segmentation Sungwoong Kim Department of EE, KAIST Daejeon, South Korea sungwoong.kim01@gmail.com Sebastian Nowozin Microsoft Research Cambridge Cambridge, UK Sebastian.Nowozin@microsoft.com Pushmeet Kohli Microsoft Research Cambridge Cambridge, UK ... | 2011 | 179 |
4,234 | The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella Department of Biophysical and Electronic Engineering University of Genova Via Opera Pia 11A, I-16145 Genova, Italy {Davide.Anguita,Alessandro.Ghio} @unige.it {... | 2011 | 18 |
4,235 | Learning large-margin halfspaces with more malicious noise Philip M. Long Google plong@google.com Rocco A. Servedio Columbia University rocco@cs.columbia.edu Abstract We describe a simple algorithm that runs in time poly(n, 1/γ, 1/ε) and learns an unknown n-dimensional γ-margin halfspace to accuracy... | 2011 | 180 |
4,236 | The Local Rademacher Complexity of ℓp-Norm Multiple Kernel Learning Marius Kloft∗ Machine Learning Laboratory TU Berlin, Germany kloft@tu-berlin.de Gilles Blanchard Department of Mathematics University of Potsdam, Germany gilles.blanchard@math.uni-potsdam.de Abstract We derive an upper bound on th... | 2011 | 181 |
4,237 | A Global Structural EM Algorithm for a Model of Cancer Progression Ali Tofigh School of Computer Science McGill Centre for Bioinformatics McGill University, Canada ali.tofigh@mcgill.ca Erik Sj¨olund Stockholm Bioinformatics Center Stockholm University, Sweden erik.sj¨olund@sbc.su.se Mattias H¨oglun... | 2011 | 182 |
4,238 | Infinite Latent SVM for Classification and Multi-task Learning Jun Zhu†, Ning Chen†, and Eric P. Xing‡ †Dept. of Computer Science & Tech., TNList Lab, Tsinghua University, Beijing 100084, China ‡Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA dcszj@tsinghua.edu.cn;chenn07@mail... | 2011 | 183 |
4,239 | On U-processes and clustering performance St´ephan Cl´emenc¸on∗ LTCI UMR Telecom ParisTech/CNRS No. 5141 Institut Telecom, Paris, 75634 Cedex 13, France stephan.clemencon@telecom-paristech.fr Abstract Many clustering techniques aim at optimizing empirical criteria that are of the form of a U-statistic of ... | 2011 | 184 |
4,240 | Robust Lasso with missing and grossly corrupted observations Nam H. Nguyen Johns Hopkins University nam@jhu.edu Nasser M. Nasrabadi U.S. Army Research Lab nasser.m.nasrabadi.civ@mail.mil Trac D. Tran Johns Hopkins University trac@jhu.edu Abstract This paper studies the problem of accurately reco... | 2011 | 185 |
4,241 | Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction Siwei Lyu Computer Science Department University at Albany, State University of New York lsw@cs.albany.edu Abstract When used to learn high dimensional parametric probabilistic models, the classical maximum likelihood (ML) lea... | 2011 | 186 |
4,242 | Select and Sample — A Model of Efficient Neural Inference and Learning Jacquelyn A. Shelton, J¨org Bornschein, Abdul-Saboor Sheikh Frankfurt Institute for Advanced Studies Goethe-University Frankfurt, Germany {shelton,bornschein,sheikh}@fias.uni-frankfurt.de Pietro Berkes Volen Center for Complex Syste... | 2011 | 187 |
4,243 | Clustered Multi-Task Learning Via Alternating Structure Optimization Jiayu Zhou, Jianhui Chen, Jieping Ye Computer Science and Engineering Arizona State University Tempe, AZ 85287 {jiayu.zhou, jianhui.chen, jieping.ye}@asu.edu Abstract Multi-task learning (MTL) learns multiple related tasks simultaneous... | 2011 | 188 |
4,244 | On Learning Discrete Graphical Models Using Greedy Methods Ali Jalali University of Texas at Austin alij@mail.utexas.edu Christopher C. Johnson University of Texas at Asutin cjohnson@cs.utexas.edu Pradeep Ravikumar University of Texas at Asutin pradeepr@cs.utexas.edu Abstract In this paper, we a... | 2011 | 189 |
4,245 | Regularized Laplacian Estimation and Fast Eigenvector Approximation Patrick O. Perry Information, Operations, and Management Sciences NYU Stern School of Business New York, NY 10012 pperry@stern.nyu.edu Michael W. Mahoney Department of Mathematics Stanford University Stanford, CA 94305 mmahoney@cs... | 2011 | 19 |
4,246 | Learning person-object interactions for action recognition in still images Vincent Delaitre∗ ´Ecole Normale Sup´erieure Josef Sivic* INRIA Paris - Rocquencourt Ivan Laptev* INRIA Paris - Rocquencourt Abstract We investigate a discriminatively trained model of person-object interactions for recognizi... | 2011 | 190 |
4,247 | Efficient inference in matrix-variate Gaussian models with iid observation noise Oliver Stegle1 Max Planck Institutes T¨ubingen, Germany stegle@tuebingen.mpg.de Christoph Lippert1 Max Planck Institutes T¨ubingen, Germany clippert@tuebingen.mpg.de Joris Mooij Institute for Computing and Information ... | 2011 | 191 |
4,248 | Confidence Sets for Network Structure David S. Choi School of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 dchoi@seas.harvard.edu Patrick Wolfe School of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 patrick@seas.harvard.edu Edoardo M. Airoldi ... | 2011 | 192 |
4,249 | Structural equations and divisive normalization for energy-dependent component analysis Jun-ichiro Hirayama Dept. of Systems Science Graduate School of of Informatics Kyoto University 611-0011 Uji, Kyoto, Japan Aapo Hyv¨arinen Dept. of Mathematics and Statistics Dept. of Computer Science and HIIT Un... | 2011 | 193 |
4,250 | Gaussian Process Training with Input Noise Andrew McHutchon Department of Engineering Cambridge University Cambridge, CB2 1PZ ajm257@cam.ac.uk Carl Edward Rasmussen Department of Engineering Cambridge University Cambridge, CB2 1PZ cer54@cam.ac.uk Abstract In standard Gaussian Process regression ... | 2011 | 194 |
4,251 | On the Universality of Online Mirror Descent Nathan Srebro TTIC nati@ttic.edu Karthik Sridharan TTIC karthik@ttic.edu Ambuj Tewari University of Texas at Austin ambuj@cs.utexas.edu Abstract We show that for a general class of convex online learning problems, Mirror Descent can always achieve a (... | 2011 | 195 |
4,252 | Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning Taiji Suzuki Department of Mathematical Informatics The University of Tokyo Tokyo 113-8656, Japan s-taiji@stat.t.u-tokyo.ac.jp Abstract In this paper, we give a new generalization error bound of Multiple Kernel Learning (MK... | 2011 | 196 |
4,253 | Speedy Q-Learning Mohammad Gheshlaghi Azar Radboud University Nijmegen Geert Grooteplein 21N, 6525 EZ Nijmegen, Netherlands m.azar@science.ru.nl Remi Munos INRIA Lille, SequeL Project 40 avenue Halley 59650 Villeneuve d’Ascq, France r.munos@inria.fr Mohammad Ghavamzadeh INRIA Lille, SequeL Proje... | 2011 | 197 |
4,254 | High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity Po-Ling Loh Department of Statistics University of California, Berkeley Berkeley, CA 94720 ploh@berkeley.edu Martin J. Wainwright Departments of Statistics and EECS University of California, Berkeley Berk... | 2011 | 198 |
4,255 | Greedy Model Averaging Dong Dai Department of Statistics Rutgers University, New Jersey, 08816 dongdai916@gmail.com Tong Zhang Department of Statistics, Rutgers University, New Jersey, 08816 tzhang@stat.rutgers.edu Abstract This paper considers the problem of combining multiple models to achieve a pre... | 2011 | 199 |
4,256 | Action-Gap Phenomenon in Reinforcement Learning Amir-massoud Farahmand∗ School of Computer Science, McGill University Montreal, Quebec, Canada Abstract Many practitioners of reinforcement learning problems have observed that oftentimes the performance of the agent reaches very close to the optimal performance... | 2011 | 2 |
4,257 | The Doubly Correlated Nonparametric Topic Model Dae Il Kim and Erik B. Sudderth Department of Computer Science Brown University, Providence, RI 02906 daeil@cs.brown.edu, sudderth@cs.brown.edu Abstract Topic models are learned via a statistical model of variation within document collections, but designed to ... | 2011 | 20 |
4,258 | Hierarchical Multitask Structured Output Learning for Large-Scale Sequence Segmentation Nico G¨ornitz1 Technical University Berlin, Franklinstr. 28/29, 10587 Berlin, Germany Nico.Goernitz@tu-berlin.de Christian Widmer1 FML of the Max Planck Society Spemannstr. 39, 72070 T¨ubingen, Germany Christian.Wi... | 2011 | 200 |
4,259 | Generalized Beta Mixtures of Gaussians Artin Armagan Dept. of Statistical Science Duke University Durham, NC 27708 artin@stat.duke.edu David B. Dunson Dept. of Statistical Science Duke University Durham, NC 27708 dunson@stat.duke.edu Merlise Clyde Dept. of Statistical Science Duke University ... | 2011 | 201 |
4,260 | Variational Gaussian Process Dynamical Systems Andreas C. Damianou∗ Department of Computer Science University of Sheffield, UK andreas.damianou@sheffield.ac.uk Michalis K. Titsias School of Computer Science University of Manchester, UK mtitsias@gmail.com Neil D. Lawrence∗ Department of Computer Scien... | 2011 | 202 |
4,261 | Statistical Tests for Optimization Efficiency Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling Department of Computer Science University of California, Irvine Irvine, CA 92697-3425 {lboyles},{akoratti},{dramanan},{welling}@ics.uci.edu Abstract Learning problems, such as logistic regression, are ty... | 2011 | 203 |
4,262 | Statistical Performance of Convex Tensor Decomposition Ryota Tomioka† Taiji Suzuki† †Department of Mathematical Informatics, The University of Tokyo Tokyo 113-8656, Japan tomioka@mist.i.u-tokyo.ac.jp s-taiji@stat.t.u-tokyo.ac.jp Kohei Hayashi‡ ‡Graduate School of Information Science, Nara Institut... | 2011 | 204 |
4,263 | A Machine Learning Approach to Predict Chemical Reactions Matthew A. Kayala Pierre Baldi∗ Institute of Genomics and Bioinformatics School of Information and Computer Sciences University of California, Irvine Irvine, CA 92697 {mkayala,pfbaldi}@ics.uci.edu Abstract Being able to predict the course of ... | 2011 | 205 |
4,264 | Sparse Features for PCA-Like Linear Regression Christos Boutsidis Mathematical Sciences Department IBM T. J. Watson Research Center Yorktown Heights, New York cboutsi@us.ibm.com Petros Drineas Computer Science Department Rensselaer Polytechnic Institute Troy, NY 12180 drinep@cs.rpi.edu Malik Magdo... | 2011 | 206 |
4,265 | Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning Michalis K. Titsias University of Manchester mtitsias@gmail.com Miguel L´azaro-Gredilla Univ. de Cantabria & Univ. Carlos III de Madrid miguel@tsc.uc3m.es Abstract We introduce a variational Bayesian inference algorithm w... | 2011 | 207 |
4,266 | Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability David P. Reichert, Peggy Seriès, and Amos J. Storkey School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh, EH8 9AB {d.p.reichert@sms., pseries@inf., a.storkey@} ed.ac.uk Abstract It has been ar... | 2011 | 208 |
4,267 | Reinforcement Learning using Kernel-Based Stochastic Factorization Andr´e M. S. Barreto School of Computer Science McGill University Montreal, Canada amsb@cs.mcgill.ca Doina Precup School of Computer Science McGill University Montreal, Canada dprecup@cs.mcgill.ca Joelle Pineau School of Comput... | 2011 | 209 |
4,268 | Generalized Lasso based Approximation of Sparse Coding for Visual Recognition Nobuyuki Morioka The University of New South Wales & NICTA Sydney, Australia nmorioka@cse.unsw.edu.au Shin’ichi Satoh National Institute of Informatics Tokyo, Japan satoh@nii.ac.jp Abstract Sparse coding, a method of exp... | 2011 | 21 |
4,269 | Better Mini-Batch Algorithms via Accelerated Gradient Methods Andrew Cotter Toyota Technological Institute at Chicago cotter@ttic.edu Ohad Shamir Microsoft Research, NE ohadsh@microsoft.com Nathan Srebro Toyota Technological Institute at Chicago nati@ttic.edu Karthik Sridharan Toyota Technologic... | 2011 | 210 |
4,270 | Generalizing from Several Related Classification Tasks to a New Unlabeled Sample Gilles Blanchard Universit¨at Potsdam blanchard@math.uni-potsdam.de Gyemin Lee, Clayton Scott University of Michigan {gyemin,clayscot}@umich.edu Abstract We consider the problem of assigning class labels to an unlabeled te... | 2011 | 211 |
4,271 | Energetically Optimal Action Potentials Martin Stemmler BCCN and LMU Munich Grosshadernerstr. 2, Planegg, 82125 Germany Biswa Sengupta, Simon Laughlin, Jeremy Niven Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK Abstract Most action potentials in the nervous sy... | 2011 | 212 |
4,272 | Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent Shinichi Nakajima Nikon Corporation Tokyo, 140-8601, Japan nakajima.s@nikon.co.jp Masashi Sugiyama Tokyo Institute of Technology Tokyo 152-8552, Japan sugi@cs.titech.ac.jp Derin Babacan Univers... | 2011 | 213 |
4,273 | Algorithms and hardness results for parallel large margin learning Philip M. Long Google plong@google.com Rocco A. Servedio Columbia University rocco@cs.columbia.edu Abstract We study the fundamental problem of learning an unknown large-margin halfspace in the context of parallel computation. Our ma... | 2011 | 214 |
4,274 | Semi-supervised Regression via Parallel Field Regularization Binbin Lin Chiyuan Zhang Xiaofei He State Key Lab of CAD&CG, College of Computer Science, Zhejiang University Hangzhou 310058, China {binbinlinzju, chiyuan.zhang.zju, xiaofeihe}@gmail.com Abstract This paper studies the problem of semi-super... | 2011 | 215 |
4,275 | Thinning Measurement Models and Questionnaire Design Ricardo Silva Department of Statistical Science University College London Gower Street, London WC1E 6BT ricardo@stats.ucl.ac.uk Abstract Inferring key unobservable features of individuals is an important task in the applied sciences. In particular, an... | 2011 | 216 |
4,276 | Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials Philipp Kr¨ahenb¨uhl Computer Science Department Stanford University philkr@cs.stanford.edu Vladlen Koltun Computer Science Department Stanford University vladlen@cs.stanford.edu Abstract Most state-of-the-art techniques for ... | 2011 | 217 |
4,277 | Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC Trung Thanh Pham, Tat-Jun Chin, Jin Yu and David Suter School of Computer Science, The University of Adelaide, South Australia {trung,tjchin,jin.yu,dsuter}@cs.adelaide.edu.au Abstract Multi-structure model fitting has tradit... | 2011 | 218 |
4,278 | Active dendrites: adaptation to spike-based communication Bal´azs B Ujfalussy1,2 ubi@rmki.kfki.hu M´at´e Lengyel1 m.lengyel@eng.cam.ac.uk 1 Computational & Biological Learning Lab, Dept. of Engineering, University of Cambridge, UK 2 Computational Neuroscience Group, Dept. of Biophysics, MTA KFKI RMKI, Bud... | 2011 | 219 |
4,279 | SpaRCS: Recovering Low-Rank and Sparse Matrices from Compressive Measurements Andrew E. Waters, Aswin C. Sankaranarayanan, Richard G. Baraniuk Rice University {andrew.e.waters, saswin, richb}@rice.edu Abstract We consider the problem of recovering a matrix M that is the sum of a low-rank matrix L and a sp... | 2011 | 22 |
4,280 | Shaping Level Sets with Submodular Functions Francis Bach INRIA - Sierra Project-team Laboratoire d’Informatique de l’Ecole Normale Sup´erieure, Paris, France francis.bach@ens.fr Abstract We consider a class of sparsity-inducing regularization terms based on submodular functions. While previous work has foc... | 2011 | 220 |
4,281 | Probabilistic amplitude and frequency demodulation Richard E. Turner∗ Computational and Biological Learning Lab, Department of Engineering University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK ret26@cam.ac.uk Maneesh Sahani Gatsby Computational Neuroscience Unit, University College London Al... | 2011 | 221 |
4,282 | Multi-Bandit Best Arm Identification Victor Gabillon Mohammad Ghavamzadeh Alessandro Lazaric INRIA Lille - Nord Europe, Team SequeL {victor.gabillon,mohammad.ghavamzadeh,alessandro.lazaric}@inria.fr S´ebastien Bubeck Department of Operations Research and Financial Engineering, Princeton University sbubec... | 2011 | 222 |
4,283 | Nonlinear Inverse Reinforcement Learning with Gaussian Processes Sergey Levine Stanford University svlevine@cs.stanford.edu Zoran Popovi´c University of Washington zoran@cs.washington.edu Vladlen Koltun Stanford University vladlen@cs.stanford.edu Abstract We present a probabilistic algorithm for... | 2011 | 223 |
4,284 | EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning Yuan Qi Computer Science and Statistics Depts. Purdue University West Lafayette, IN 47907, USA Feng Yan Computer Science Dept. Purdue University West Lafayette, IN 47907, USA Abstract For many real-world applicati... | 2011 | 224 |
4,285 | Iterative Learning for Reliable Crowdsourcing Systems David R. Karger Sewoong Oh Devavrat Shah Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Abstract Crowdsourcing systems, in which tasks are electronically distributed to numerous “information piece-wo... | 2011 | 225 |
4,286 | Minimax Localization of Structural Information in Large Noisy Matrices Mladen Kolar†⇤ mladenk@cs.cmu.edu Sivaraman Balakrishnan†⇤ sbalakri@cs.cmu.edu Alessandro Rinaldo†† arinaldo@stat.cmu.edu Aarti Singh† aarti@cs.cmu.edu † School of Computer Science and †† Department of Statistics, Carnegie Mellon... | 2011 | 226 |
4,287 | Crowdclustering Ryan Gomes∗ Caltech Peter Welinder Caltech Andreas Krause ETH Zurich & Caltech Pietro Perona Caltech Abstract Is it possible to crowdsource categorization? Amongst the challenges: (a) each worker has only a partial view of the data, (b) different workers may have different clusteri... | 2011 | 227 |
4,288 | Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs Armen Allahverdyan∗ Yerevan Physics Institute Yerevan, Armenia aarmen@yerphi.am Aram Galstyan USC Information Sciences Institute Marina del Rey, CA, USA galstyan@isi.edu Abstract We present an asymptotic analysis o... | 2011 | 228 |
4,289 | Environmental statistics and the trade-off between model-based and TD learning in humans Dylan A. Simon Department of Psychology New York University New York, NY 10003 dylex@nyu.edu Nathaniel D. Daw Center for Neural Science and Department of Psychology New York University New York, NY 10003 natha... | 2011 | 229 |
4,290 | Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin Department of Computing Science University of Alberta Edmonton, AB T6G 2E8 {chunnam,rgreiner,hsiuchin}@ualberta.ca Vickie Baracos Department of Oncology University o... | 2011 | 23 |
4,291 | A Model for Temporal Dependencies in Event Streams Asela Gunawardana Microsoft Research One Microsoft Way Redmond, WA 98052 aselag@microsoft.com Christopher Meek Microsoft Research One Microsoft Way Redmond, WA 98052 meek@microsoft.com Puyang Xu ECE Dept. & CLSP Johns Hopkins University Ba... | 2011 | 230 |
4,292 | Inferring Interaction Networks using the IBP applied to microRNA Target Prediction Hai-Son Le Machine Learning Department Carnegie Mellon University Pittsburgh, PA, USA hple@cs.cmu.edu Ziv Bar-Joseph Machine Learning Department Carnegie Mellon University Pittsburgh, PA, USA zivbj@cs.cmu.edu Abst... | 2011 | 231 |
4,293 | Learning unbelievable probabilities Xaq Pitkow Department of Brain and Cognitive Science University of Rochester Rochester, NY 14607 xaq@neurotheory.columbia.edu Yashar Ahmadian Center for Theoretical Neuroscience Columbia University New York, NY 10032 ya2005@columbia.edu Ken D. Miller Center fo... | 2011 | 232 |
4,294 | Relative Density-Ratio Estimation for Robust Distribution Comparison Makoto Yamada Tokyo Institute of Technology yamada@sg.cs.titech.ac.jp Taiji Suzuki The University of Tokyo s-taiji@stat.t.u-tokyo.ac.jp Takafumi Kanamori Nagoya University kanamori@is.nagoya-u.ac.jp Hirotaka Hachiya Masashi Sug... | 2011 | 233 |
4,295 | The Manifold Tangent Classifier Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller Department of Computer Science and Operations Research University of Montreal Montreal, H3C 3J7 {rifaisal, dauphiya, vincentp, bengioy, mullerx}@iro.umontreal.ca Abstract We combine three important id... | 2011 | 234 |
4,296 | Manifold Pr´ecis: An Annealing Technique for Diverse Sampling of Manifolds Nitesh Shroff †, Pavan Turaga ‡, Rama Chellappa † †Department of Electrical and Computer Engineering, University of Maryland, College Park ‡School of Arts, Media, Engineering and ECEE, Arizona State University {nshroff,rama}@umiacs.umd... | 2011 | 235 |
4,297 | Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines Matthew D. Zeiler1, Graham W. Taylor1, Leonid Sigal2, Iain Matthews2, and Rob Fergus1 1Department of Computer Science, New York University, New York, NY 10012 2Disney Research, Pittsburgh, PA 15213 Abstract We present a type... | 2011 | 236 |
4,298 | Committing Bandits Loc Bui∗ MS&E Department Stanford University Ramesh Johari† MS&E Department Stanford University Shie Mannor‡ EE Department Technion Abstract We consider a multi-armed bandit problem where there are two phases. The first phase is an experimentation phase where the decision maker... | 2011 | 237 |
4,299 | Large-Scale Category Structure Aware Image Categorization Bin Zhao School of Computer Science Carnegie Mellon University binzhao@cs.cmu.edu Li Fei-Fei Computer Science Department Stanford University feifeili@cs.stanford.edu Eric P. Xing School of Computer Science Carnegie Mellon University epx... | 2011 | 238 |
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