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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3,200 | Boosting the Area Under the ROC Curve Philip M. Long plong@google.com Rocco A. Servedio rocco@cs.columbia.edu Abstract We show that any weak ranker that can achieve an area under the ROC curve slightly better than 1/2 (which can be achieved by random guessing) can be efficiently boosted to achieve an area ... | 2007 | 168 |
3,201 | Sparse Feature Learning for Deep Belief Networks Marc’Aurelio Ranzato1 Y-Lan Boureau2,1 Yann LeCun1 1 Courant Institute of Mathematical Sciences, New York University 2 INRIA Rocquencourt {ranzato,ylan,yann@courant.nyu.edu} Abstract Unsupervised learning algorithms aim to discover the structure hidden in... | 2007 | 169 |
3,202 | Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods Alessandro Lazaric Marcello Restelli Andrea Bonarini Department of Electronics and Information Politecnico di Milano piazza Leonardo da Vinci 32, I-20133 Milan, Italy {bonarini,lazaric,restelli}@elet.polimi.it Ab... | 2007 | 17 |
3,203 | Receding Horizon Differential Dynamic Programming Yuval Tassa ∗ Tom Erez & Bill Smart † Abstract The control of high-dimensional, continuous, non-linear dynamical systems is a key problem in reinforcement learning and control. Local, trajectory-based methods, using techniques such as Differential Dynamic Pr... | 2007 | 170 |
3,204 | A Risk Minimization Principle for a Class of Parzen Estimators Kristiaan Pelckmans, Johan A.K. Suykens, Bart De Moor Department of Electrical Engineering (ESAT) - SCD/SISTA K.U.Leuven University Kasteelpark Arenberg 10, Leuven, Belgium Kristiaan.Pelckmans@esat.kuleuven.be Abstract This paper1 explores t... | 2007 | 171 |
3,205 | Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning Gerald Tesauro, Rajarshi Das, Hoi Chan, Jeffrey O. Kephart, Charles Lefurgy∗, David W. Levine and Freeman Rawson∗ IBM Watson and Austin∗Research Laboratories {gtesauro,rajarshi,hychan,kephart,lefurgy,dwl,frawson}@us.i... | 2007 | 172 |
3,206 | A Game-Theoretic Approach to Apprenticeship Learning Umar Syed Computer Science Department Princeton University 35 Olden St Princeton, NJ 08540-5233 usyed@cs.princeton.edu Robert E. Schapire Computer Science Department Princeton University 35 Olden St Princeton, NJ 08540-5233 schapire@cs.princ... | 2007 | 173 |
3,207 | Scene Segmentation with Conditional Random Fields Learned from Partially Labeled Images Jakob Verbeek and Bill Triggs INRIA and Laboratoire Jean Kuntzmann, 655 avenue de l’Europe, 38330 Montbonnot, France Abstract Conditional Random Fields (CRFs) are an effective tool for a variety of different data segment... | 2007 | 174 |
3,208 | Measuring Neural Synchrony by Message Passing Justin Dauwels Amari Research Unit RIKEN Brain Science Institute Wako-shi, Saitama, Japan justin@dauwels.com Franc¸ois Vialatte, Tomasz Rutkowski, and Andrzej Cichocki Advanced Brain Signal Processing Laboratory RIKEN Brain Science Institute Wako-shi, Sait... | 2007 | 175 |
3,209 | Discriminative Batch Mode Active Learning Yuhong Guo and Dale Schuurmans Department of Computing Science University of Alberta {yuhong, dale}@cs.ualberta.ca Abstract Active learning sequentially selects unlabeled instances to label with the goal of reducing the effort needed to learn a good classifier. Mos... | 2007 | 176 |
3,210 | Comparing Bayesian models for multisensory cue combination without mandatory integration Ulrik R. Beierholm Computation and Neural Systems California Institute of Technology Pasadena, CA 91025 beierh@caltech.edu Konrad P. K¨ording Rehabilitation Institute of Chicago Northwestern University, Dept. PM&R... | 2007 | 177 |
3,211 | What Makes Some POMDP Problems Easy to Approximate? David Hsu∗ Wee Sun Lee∗ Nan Rong† ∗Department of Computer Science National University of Singapore Singapore, 117590, Singapore †Department of Computer Science Cornell University Ithaca, NY 14853, USA Abstract Point-based algorithms have been sur... | 2007 | 178 |
3,212 | Boosting Algorithms for Maximizing the Soft Margin Manfred K. Warmuth∗ Dept. of Engineering University of California Santa Cruz, CA, U.S.A. Karen Glocer Dept. of Engineering University of California Santa Cruz, CA, U.S.A. Gunnar R¨atsch Friedrich Miescher Laboratory Max Planck Society T¨ubingen,... | 2007 | 179 |
3,213 | Ensemble Clustering using Semidefinite Programming Vikas Singh Biostatistics and Medical Informatics University of Wisconsin – Madison vsingh @ biostat.wisc.edu Lopamudra Mukherjee Computer Science and Engineering State University of New York at Buffalo lm37 @ cse.buffalo.edu Jiming Peng Industrial... | 2007 | 18 |
3,214 | Gaussian Process Models for Link Analysis and Transfer Learning Kai Yu NEC Laboratories America Cupertino, CA 95014 Wei Chu Columbia University, CCLS New York, NY 10115 Abstract This paper aims to model relational data on edges of networks. We describe appropriate Gaussian Processes (GPs) for directed... | 2007 | 180 |
3,215 | Near-Maximum Entropy Models for Binary Neural Representations of Natural Images Matthias Bethge and Philipp Berens Max Planck Institute for Biological Cybernetics Spemannstrasse 41, 72076, T¨ubingen, Germany mbethge,berens@tuebingen.mpg.de Abstract Maximum entropy analysis of binary variables provides an ... | 2007 | 181 |
3,216 | Privacy-Preserving Belief Propagation and Sampling Michael Kearns, Jinsong Tan, and Jennifer Wortman Department of Computer and Information Science University of Pennsylvania, Philadelphia, PA 19104 Abstract We provide provably privacy-preserving versions of belief propagation, Gibbs sampling, and other loc... | 2007 | 182 |
3,217 | Bayesian Co-Training Shipeng Yu, Balaji Krishnapuram, Romer Rosales, Harald Steck, R. Bharat Rao CAD & Knowledge Solutions, Siemens Medical Solutions USA, Inc. firstname.lastname@siemens.com Abstract We propose a Bayesian undirected graphical model for co-training, or more generally for semi-supervised multi-... | 2007 | 183 |
3,218 | Supervised topic models David M. Blei Department of Computer Science Princeton University Princeton, NJ blei@cs.princeton.edu Jon D. McAuliffe Department of Statistics University of Pennsylvania, Wharton School Philadelphia, PA mcjon@wharton.upenn.edu Abstract We introduce supervised latent Di... | 2007 | 184 |
3,219 | A Kernel Statistical Test of Independence Arthur Gretton MPI for Biological Cybernetics T¨ubingen, Germany arthur@tuebingen.mpg.de Kenji Fukumizu Inst. of Statistical Mathematics Tokyo Japan fukumizu@ism.ac.jp Choon Hui Teo NICTA, ANU Canberra, Australia choonhui.teo@gmail.com Le Song NICTA,... | 2007 | 185 |
3,220 | Discriminative Keyword Selection Using Support Vector Machines W. M. Campbell, F. S. Richardson MIT Lincoln Laboratory Lexington, MA 02420 wcampbell,frichard@ll.mit.edu Abstract Many tasks in speech processing involve classification of long term characteristics of a speech segment such as language, speak... | 2007 | 186 |
3,221 | Probabilistic Matrix Factorization Ruslan Salakhutdinov and Andriy Mnih Department of Computer Science, University of Toronto 6 King’s College Rd, M5S 3G4, Canada {rsalakhu,amnih}@cs.toronto.edu Abstract Many existing approaches to collaborative filtering can neither handle very large datasets nor easily d... | 2007 | 187 |
3,222 | Density Estimation under Independent Similarly Distributed Sampling Assumptions Tony Jebara, Yingbo Song and Kapil Thadani Department of Computer Science Columbia University New York, NY 10027 { jebara,yingbo,kapil }@cs.columbia.edu Abstract A method is proposed for semiparametric estimation where param... | 2007 | 188 |
3,223 | Efficient Inference for Distributions on Permutations Jonathan Huang Carnegie Mellon University jch1@cs.cmu.edu Carlos Guestrin Carnegie Mellon University guestrin@cs.cmu.edu Leonidas Guibas Stanford University guibas@cs.stanford.edu Abstract Permutations are ubiquitous in many real world problems,... | 2007 | 189 |
3,224 | Theoretical Analysis of Heuristic Search Methods for Online POMDPs St´ephane Ross McGill University Montr´eal, Qc, Canada sross12@cs.mcgill.ca Joelle Pineau McGill University Montr´eal, Qc, Canada jpineau@cs.mcgill.ca Brahim Chaib-draa Laval University Qu´ebec, Qc, Canada chaib@ift.ulaval.ca ... | 2007 | 19 |
3,225 | Fitted Q-iteration in continuous action-space MDPs Andr´as Antos Computer and Automation Research Inst. of the Hungarian Academy of Sciences Kende u. 13-17, Budapest 1111, Hungary antos@sztaki.hu R´emi Munos SequeL project-team, INRIA Lille 59650 Villeneuve d’Ascq, France remi.munos@inria.fr Csaba S... | 2007 | 190 |
3,226 | Blind channel identification for speech dereverberation using l1-norm sparse learning Yuanqing Lin†, Jingdong Chen‡, Youngmoo Kim♯, Daniel D. Lee† †GRASP Laboratory, Department of Electrical and Systems Engineering, University of Pennsylvania ‡Bell Laboratories, Alcatel-Lucent ♯Department of Electrical and Com... | 2007 | 191 |
3,227 | Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression S. Ghebreab ISLA/HCS lab, Informatics Institute University of Amsterdam, The Netherlands ghebreab@science.uva.nl A.W.M. Smeulders ISLA lab, Informatics Institute University of Amsterdam, The Netherlands sme... | 2007 | 192 |
3,228 | Agreement-Based Learning Percy Liang Computer Science Division University of California Berkeley, CA 94720 pliang@cs.berkeley.edu Dan Klein Computer Science Division University of California Berkeley, CA 94720 klein@cs.berkeley.edu Michael I. Jordan Computer Science Division University of Cali... | 2007 | 193 |
3,229 | Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination Stanley Yiu Man Lam and Bertram E. Shi Department of Electronic and Computer Engineering Hong Kong Univeristy of Science and Technology Clear Water Bay, Kowloon, Hong Kong {eelym,eebert}@ee.ust.hk Abstract W... | 2007 | 194 |
3,230 | A Bayesian Framework for Cross-Situational Word-Learning Michael C. Frank, Noah D. Goodman, and Joshua B. Tenenbaum Department of Brain and Cognitive Science Massachusetts Institute of Technology {mcfrank, ndg, jbt}@mit.edu Abstract For infants, early word learning is a chicken-and-egg problem. One way to... | 2007 | 195 |
3,231 | PSVM: Parallelizing Support Vector Machines on Distributed Computers Edward Y. Chang∗, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, & Hang Cui Google Research, Beijing, China Abstract Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and comp... | 2007 | 196 |
3,232 | Object Recognition by Scene Alignment Bryan C. Russell Antonio Torralba Ce Liu Rob Fergus William T. Freeman Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambrige, MA 02139 USA {brussell,torralba,celiu,fergus,billf}@csail.mit.edu Abstract Current obj... | 2007 | 197 |
3,233 | A neural network implementing optimal state estimation based on dynamic spike train decoding Omer Bobrowski1, Ron Meir1, Shy Shoham2 and Yonina C. Eldar1 Department of Electrical Engineering1 and Biomedical Engineering2 Technion, Haifa 32000, Israel {bober@tx},{rmeir@ee},{sshoham@bm},{yonina@ee}.technion.ac.i... | 2007 | 198 |
3,234 | Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria Elad Hazan IBM Almaden Research Center 650 Harry Road San Jose, CA 95120 hazan@us.ibm.com Satyen Kale Computer Science Department, Princeton University 35 Olden St. Princeton, NJ 08540 satyen@cs.pri... | 2007 | 199 |
3,235 | Random Features for Large-Scale Kernel Machines Ali Rahimi Intel Research Seattle Seattle, WA 98105 ali.rahimi@intel.com Benjamin Recht Caltech IST Pasadena, CA 91125 brecht@ist.caltech.edu Abstract To accelerate the training of kernel machines, we propose to map the input data to a randomized low... | 2007 | 2 |
3,236 | A Constraint Generation Approach to Learning Stable Linear Dynamical Systems Sajid M. Siddiqi Robotics Institute Carnegie-Mellon University Pittsburgh, PA 15213 siddiqi@cs.cmu.edu Byron Boots Computer Science Department Carnegie-Mellon University Pittsburgh, PA 15213 beb@cs.cmu.edu Geoffrey J. G... | 2007 | 20 |
3,237 | Catching Change-points with Lasso Zaid Harchaoui, C´eline L´evy-Leduc LTCI, TELECOM ParisTech and CNRS 37/39 Rue Dareau, 75014 Paris, France {zharchao,levyledu}@enst.fr Abstract We propose a new approach for dealing with the estimation of the location of change-points in one-dimensional piecewise constant... | 2007 | 200 |
3,238 | Feature Selection Methods for Improving Protein Structure Prediction with Rosetta Ben Blum, Michael I. Jordan Department of Electrical Engineering and Computer Science University of California at Berkeley Berkeley, CA 94305 {bblum,jordan}@cs.berkeley.edu David E. Kim, Rhiju Das, Philip Bradley, David Bake... | 2007 | 201 |
3,239 | Selecting Observations against Adversarial Objectives Andreas Krause SCS, CMU H. Brendan McMahan Google, Inc. Carlos Guestrin SCS, CMU Anupam Gupta SCS, CMU Abstract In many applications, one has to actively select among a set of expensive observations before making an informed decision. Often, we w... | 2007 | 202 |
3,240 | Spatial Latent Dirichlet Allocation Xiaogang Wang and Eric Grimson Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology, Cambridge, MA, 02139, USA xgwang@csail.mit.edu, welg@csail.mit.edu Abstract In recent years, the language model Latent Dirichlet Allocation (LDA), which ... | 2007 | 203 |
3,241 | Learning Visual Attributes Vittorio Ferrari ∗ University of Oxford (UK) Andrew Zisserman University of Oxford (UK) Abstract We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ‘striped’, ... | 2007 | 204 |
3,242 | Collapsed Variational Inference for HDP Yee Whye Teh Gatsby Unit University College London ywteh@gatsby.ucl.ac.uk Kenichi Kurihara Dept. of Computer Science Tokyo Institute of Technology kurihara@mi.cs.titech.ac.jp Max Welling ICS UC Irvine welling@ics.uci.edu Abstract A wide variety of Diri... | 2007 | 205 |
3,243 | Progressive mixture rules are deviation suboptimal Jean-Yves Audibert Willow Project - Certis Lab ParisTech, Ecole des Ponts 77455 Marne-la-Vall´ee, France audibert@certis.enpc.fr Abstract We consider the learning task consisting in predicting as well as the best function in a finite reference set G up t... | 2007 | 206 |
3,244 | Experience-Guided Search: A Theory of Attentional Control Michael C. Mozer Department of Computer Science and Institute of Cognitive Science University of Colorado mozer@colorado.edu David Baldwin Department of Computer Science Indiana University Bloomington, IN 47405 baldwind@indiana.edu Abstra... | 2007 | 207 |
3,245 | Hierarchical Penalization Marie Szafranski 1, Yves Grandvalet 1, 2 and Pierre Morizet-Mahoudeaux 1 Heudiasyc 1, UMR CNRS 6599 Universit´e de Technologie de Compi`egne BP 20529, 60205 Compi`egne Cedex, France IDIAP Research Institute 2 Av. des Pr´es-Beudin 20 P.O. Box 592, 1920 Martigny, Switzerland mari... | 2007 | 208 |
3,246 | Linear Programming Analysis of Loopy Belief Propagation for Weighted Matching Sujay Sanghavi, Dmitry M. Malioutov and Alan S. Willsky Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge, MA 02139 {sanghavi,dmm,willsky}@mit.edu Abstract Loopy belief propagation... | 2007 | 209 |
3,247 | An online Hebbian learning rule that performs Independent Component Analysis Claudia Clopath School of Computer Science and Brain Mind Institute Ecole polytechnique federale de Lausanne 1015 Lausanne EPFL claudia.clopath@epfl.ch Andre Longtin Center for Neural Dynamics University of Ottawa 150 Louis... | 2007 | 21 |
3,248 | Learning Horizontal Connections in a Sparse Coding Model of Natural Images Pierre J. Garrigues Department of EECS Redwood Center for Theoretical Neuroscience Univ. of California, Berkeley Berkeley, CA 94720 garrigue@eecs.berkeley.edu Bruno A. Olshausen Helen Wills Neuroscience Inst. School of Optome... | 2007 | 210 |
3,249 | COFIRANK Maximum Margin Matrix Factorization for Collaborative Ranking Markus Weimer∗ Alexandros Karatzoglou† Quoc Viet Le‡ Alex Smola§ Abstract In this paper, we consider collaborative filtering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes rank... | 2007 | 211 |
3,250 | Infinite State Bayesian Networks Max Welling∗, Ian Porteous, Evgeniy Bart† Donald Bren School of Information and Computer Sciences University of California Irvine Irvine, CA 92697-3425 USA {welling,iporteou}@ics.uci.edu, bart@caltech.edu Abstract A general modeling framework is proposed that unifies nonpara... | 2007 | 212 |
3,251 | Regularized Boost for Semi-Supervised Learning Ke Chen and Shihai Wang School of Computer Science The University of Manchester Manchester M13 9PL, United Kingdom {chen,swang}@cs.manchester.ac.uk Abstract Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing ... | 2007 | 213 |
3,252 | Consistent Minimization of Clustering Objective Functions Ulrike von Luxburg Max Planck Institute for Biological Cybernetics ulrike.luxburg@tuebingen.mpg.de S´ebastien Bubeck INRIA Futurs Lille, France sebastien.bubeck@inria.fr Stefanie Jegelka Max Planck Institute for Biological Cybernetics stefani... | 2007 | 214 |
3,253 | The pigeon as particle filter Nathaniel D. Daw Center for Neural Science and Department of Psychology New York University daw@cns.nyu.edu Aaron C. Courville Département d’Informatique et de recherche opérationnelle Université de Montréal aaron.courville@gmail.com Abstract Although theorists have ... | 2007 | 215 |
3,254 | Non-Parametric Modeling of Partially Ranked Data Guy Lebanon Department of Statistics, and School of Elec. and Computer Engineering Purdue University - West Lafayette, IN lebanon@stat.purdue.edu Yi Mao School of Elec. and Computer Engineering Purdue University - West Lafayette, IN ymao@ecn.purdue.edu ... | 2007 | 216 |
3,255 | Multiple-Instance Pruning For Learning Efficient Cascade Detectors Cha Zhang and Paul Viola Microsoft Research One Microsoft Way, Redmond, WA 98052 {chazhang,viola}@microsoft.com Abstract Cascade detectors have been shown to operate extremely rapidly, with high accuracy, and have important applications suc... | 2007 | 217 |
3,256 | Modeling Natural Sounds with Modulation Cascade Processes Richard E. Turner and Maneesh Sahani Gatsby Computational Neuroscience Unit 17 Alexandra House, Queen Square, London, WC1N 3AR, London Abstract Natural sounds are structured on many time-scales. A typical segment of speech, for example, contains fe... | 2007 | 22 |
3,257 | Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition Maryam Mahdaviani Computer Science Department University of British Columbia Vancouver, BC, Canada Tanzeem Choudhury Intel Research 1100 NE 45th Street Seattle, WA 98105,USA Abstract We present a new and ef... | 2007 | 23 |
3,258 | How SVMs can estimate quantiles and the median Ingo Steinwart Information Sciences Group CCS-3 Los Alamos National Laboratory Los Alamos, NM 87545, USA ingo@lanl.gov Andreas Christmann Department of Mathematics Vrije Universiteit Brussel B-1050 Brussels, Belgium andreas.christmann@vub.ac.be Abstra... | 2007 | 24 |
3,259 | Random Projections for Manifold Learning Chinmay Hegde ECE Department Rice University ch3@rice.edu Michael B. Wakin EECS Department University of Michigan wakin@eecs.umich.edu Richard G. Baraniuk ECE Department Rice University richb@rice.edu Abstract We propose a novel method for linear dime... | 2007 | 25 |
3,260 | Hippocampal Contributions to Control: The Third Way M´at´e Lengyel Collegium Budapest Institute for Advanced Study 2 Szenth´aroms´ag u, Budapest, H-1014, Hungary and Computational & Biological Learning Lab Cambridge University Engineering Department Trumpington Street, Cambridge CB2 1PZ, UK lmate@gats... | 2007 | 26 |
3,261 | Rapid Inference on a Novel AND/OR graph for Object Detection, Segmentation and Parsing Yuanhao Chen Department of Automation University of Science and Technology of China yhchen4@ustc.edu.cn Long (Leo) Zhu Department of Statistics University of California, Los Angeles lzhu@stat.ucla.edu Chenxi Lin ... | 2007 | 27 |
3,262 | Convex Learning with Invariances Choon Hui Teo Australian National University choonhui.teo@anu.edu.au Amir Globerson CSAIL, MIT gamir@csail.mit.edu Sam Roweis Department of Computer Science University of Toronto roweis@cs.toronto.edu Alexander J. Smola NICTA Canberra, Australia alex.smola@gm... | 2007 | 28 |
3,263 | The Noisy-Logical Distribution and its Application to Causal Inference Alan Yuille Department of Statistics University of California at Los Angeles Los Angeles, CA 90095 yuille@stat.ucla.edu Hongjing Lu Department of Psychology University of California at Los Angeles Los Angeles, CA 90095 hongjing... | 2007 | 29 |
3,264 | Compressed Regression Shuheng Zhou∗ John Lafferty∗† Larry Wasserman‡† ∗Computer Science Department ‡Department of Statistics †Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 Abstract Recent research has studied the role of sparsity in high dimensional regression and sig... | 2007 | 3 |
3,265 | DIFFRAC : a discriminative and flexible framework for clustering Francis R. Bach INRIA - Willow Project ´Ecole Normale Sup´erieure 45, rue d’Ulm, 75230 Paris, France francis.bach@mines.org Za¨ıd Harchaoui LTCI, TELECOM ParisTech and CNRS 46, rue Barrault 75634 Paris cedex 13, France zaid.harchaoui... | 2007 | 30 |
3,266 | Bundle Methods for Machine Learning Alexander J. Smola, S.V. N. Vishwanathan, Quoc V. Le NICTA and Australian National University, Canberra, Australia Alex.Smola@gmail.com, {SVN.Vishwanathan, Quoc.Le}@nicta.com.au Abstract We present a globally convergent method for regularized risk minimization problems. Our... | 2007 | 31 |
3,267 | Catching Up Faster in Bayesian Model Selection and Model Averaging Tim van Erven Peter Gr¨unwald Steven de Rooij Centrum voor Wiskunde en Informatica (CWI) Kruislaan 413, P.O. Box 94079 1090 GB Amsterdam, The Netherlands {Tim.van.Erven,Peter.Grunwald,Steven.de.Rooij}@cwi.nl Abstract Bayesian model a... | 2007 | 32 |
3,268 | Nearest-Neighbor-Based Active Learning for Rare Category Detection Jingrui He School of Computer Science Carnegie Mellon University jingruih@cs.cmu.edu Jaime Carbonell School of Computer Science Carnegie Mellon University jgc@cs.cmu.edu Abstract Rare category detection is an open challenge for act... | 2007 | 33 |
3,269 | Receptive Fields without Spike-Triggering Jakob H Macke j akob@ tuebi ngen. mpg. de Max Planck Institute for Biological Cybernetics Spemannstrasse 41 72076 T¨ubingen, Germany G ¨unther Zeck z eck@ neuro. mpg. de Max Planck Institute of Neurobiology Am Klopferspitze 1 8 821 52 Martinsried, Germany ... | 2007 | 34 |
3,270 | Robust Regression with Twinned Gaussian Processes Andrew Naish-Guzman & Sean Holden Computer Laboratory University of Cambridge Cambridge, CB3 0FD. United Kingdom {agpn2,sbh11}@cl.cam.ac.uk Abstract We propose a Gaussian process (GP) framework for robust inference in which a GP prior on the mixing weigh... | 2007 | 35 |
3,271 | New Outer Bounds on the Marginal Polytope David Sontag Tommi Jaakkola Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 dsontag,tommi@csail.mit.edu Abstract We give a new class of outer bounds on the marginal polytope, and propose a cuttin... | 2007 | 36 |
3,272 | Neural characterization in partially observed populations of spiking neurons Jonathan W. Pillow Peter Latham Gatsby Computational Neuroscience Unit, UCL 17 Queen Square, London WC1N 3AR, UK pillow@gatsby.ucl.ac.uk pel@gatsby.ucl.ac.uk Abstract Point process encoding models provide powerful statistical... | 2007 | 37 |
3,273 | Bayesian Agglomerative Clustering with Coalescents Yee Whye Teh Gatsby Unit University College London ywteh@gatsby.ucl.ac.uk Hal Daum´e III School of Computing University of Utah me@hal3.name Daniel Roy CSAIL MIT droy@mit.edu Abstract We introduce a new Bayesian model for hierarchical cluste... | 2007 | 38 |
3,274 | Distributed Inference for Latent Dirichlet Allocation David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling Department of Computer Science University of California, Irvine newman,asuncion,smyth,welling @ics.uci.edu Abstract We investigate the problem of learning a widely-used latent-variable mode... | 2007 | 39 |
3,275 | Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains Andrea Lecchini-Visintini Department of Engineering University of Leicester, UK alv1@leicester.ac.uk John Lygeros Automatic Control Laboratory ETH Zurich, Switzerland. lygeros@control.ee.ethz.ch Jan Maciejowsk... | 2007 | 4 |
3,276 | Comparison of objective functions for estimating linear-nonlinear models Tatyana O. Sharpee Computational Neurobiology Laboratory, the Salk Institute for Biological Studies, La Jolla, CA 92037 sharpee@salk.edu Abstract This paper compares a family of methods for characterizing neural feature selectivity w... | 2007 | 40 |
3,277 | Structured Learning with Approximate Inference Alex Kulesza and Fernando Pereira∗ Department of Computer and Information Science University of Pennsylvania {kulesza, pereira}@cis.upenn.edu Abstract In many structured prediction problems, the highest-scoring labeling is hard to compute exactly, leading to ... | 2007 | 41 |
3,278 | On Ranking in Survival Analysis: Bounds on the Concordance Index Vikas C. Raykar, Harald Steck, Balaji Krishnapuram CAD and Knowledge Solutions (IKM CKS), Siemens Medical Solutions Inc., Malvern, USA {vikas.raykar,harald.steck,balaji.krishnapuram}@siemens.com Cary Dehing-Oberije, Philippe Lambin Maastro Cli... | 2007 | 42 |
3,279 | Competition adds complexity Judy Goldsmith Department of Computer Science University of Kentucky Lexington, KY goldsmit@cs.uky.edu Martin Mundhenk Friedrich-Schiller-Universit¨at Jena Jena, Germany mundhenk@cs.uni-jena.de Abstract It is known that determinining whether a DEC-POMDP, namely, a coope... | 2007 | 43 |
3,280 | Classification via Minimum Incremental Coding Length (MICL) John Wright∗, Yi Ma Coordinated Science Laboratory University of Illinois at Urbana-Champaign {jnwright,yima}@uiuc.edu Yangyu Tao, Zhouchen Lin, Heung-Yeung Shum Visual Computing Group Microsoft Research Asia {v-yatao,zhoulin,hshum}@microsoft.... | 2007 | 44 |
3,281 | Kernel Measures of Conditional Dependence Kenji Fukumizu Institute of Statistical Mathematics 4-6-7 Minami-Azabu, Minato-ku Tokyo 106-8569 Japan fukumizu@ism.ac.jp Arthur Gretton Max-Planck Institute for Biological Cybernetics Spemannstraße 38, 72076 T¨ubingen, Germany arthur.gretton@tuebingen.mpg.de ... | 2007 | 45 |
3,282 | Trans-dimensional MCMC for Bayesian Policy Learning Matt Hoffman Dept. of Computer Science University of British Columbia hoffmanm@cs.ubc.ca Arnaud Doucet Depts. of Statistics and Computer Science University of British Columbia arnaud@cs.ubc.ca Nando de Freitas Dept. of Computer Science Universi... | 2007 | 46 |
3,283 | Temporal Difference Updating without a Learning Rate Marcus Hutter RSISE@ANU and SML@NICTA Canberra, ACT, 0200, Australia marcus@hutter1.net www.hutter1.net Shane Legg IDSIA, Galleria 2, Manno-Lugano CH-6928, Switzerland shane@vetta.org www.vetta.org/shane Abstract We derive an equation for temp... | 2007 | 47 |
3,284 | Bayes-Adaptive POMDPs St´ephane Ross McGill University Montr´eal, Qc, Canada sross12@cs.mcgill.ca Brahim Chaib-draa Laval University Qu´ebec, Qc, Canada chaib@ift.ulaval.ca Joelle Pineau McGill University Montr´eal, Qc, Canada jpineau@cs.mcgill.ca Abstract Bayesian Reinforcement Learning has... | 2007 | 48 |
3,285 | Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierarchical Approach Jos´e Miguel Hern´andez-Lobato Escuela Polit´ecnica Superior Universidad Aut´onoma de Madrid, Madrid, Spain Josemiguel.hernandez@uam.es Tjeerd Dijkstra Leiden Malaria Research Group LUMC, Leiden, The Nethe... | 2007 | 49 |
3,286 | Predictive Matrix-Variate t Models Shenghuo Zhu Kai Yu Yihong Gong NEC Labs America, Inc. 10080 N. Wolfe Rd. SW3-350 Cupertino, CA 95014 {zsh,kyu,ygong}@sv.nec-labs.com Abstract It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. W... | 2007 | 5 |
3,287 | Convex Clustering with Exemplar-Based Models Danial Lashkari Polina Golland Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 {danial, polina}@csail.mit.edu Abstract Clustering is often formulated as the maximum likelihood estimation of a mi... | 2007 | 50 |
3,288 | Learning Bounds for Domain Adaptation John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Department of Computer and Information Science University of Pennsylvania, Philadelphia, PA 19146 {blitzer,crammer,kulesza,pereira,wortmanj}@cis.upenn.edu Abstract Empirical risk minimizati... | 2007 | 51 |
3,289 | SpAM: Sparse Additive Models Pradeep Ravikumar† Han Liu†‡ John Lafferty∗† Larry Wasserman‡† †Machine Learning Department ‡Department of Statistics ∗Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 Abstract We present a new class of models for high-dimensional nonparametr... | 2007 | 52 |
3,290 | Bayesian Inference for Spiking Neuron Models with a Sparsity Prior Sebastian Gerwinn Jakob H Macke Matthias Seeger Matthias Bethge Max Planck Institute for Biological Cybernetics Spemannstrasse 41 72076 Tuebingen, Germany {firstname.surname}@tuebingen.mpg.de Abstract Generalized linear models are ... | 2007 | 53 |
3,291 | Unconstrained Online Handwriting Recognition with Recurrent Neural Networks Alex Graves TUM, Germany alex@idsia.ch Santiago Fern´andez IDSIA, Switzerland santiago@idsia.ch Marcus Liwicki University of Bern, Switzerland liwicki@iam.unibe.ch Horst Bunke University of Bern, Switzerland bunke@iam.... | 2007 | 54 |
3,292 | The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits John Langford Yahoo! Research jl@yahoo-inc.com Tong Zhang Department of Statistics Rutgers University tongz@rci.rutgers.edu Abstract We present Epoch-Greedy, an algorithm for contextual multi-armed bandits (also known as bandits with si... | 2007 | 55 |
3,293 | Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes Ruslan Salakhutdinov and Geoffrey Hinton Department of Computer Science, University of Toronto 6 King’s College Rd, M5S 3G4, Canada rsalakhu,hinton@cs.toronto.edu Abstract We show how to use unlabeled data and a deep belief net (DBN... | 2007 | 56 |
3,294 | Kernels on Attributed Pointsets with Applications Mehul Parsana1 mehul.parsana@gmail.com Sourangshu Bhattacharya1 sourangshu@gmail.com Chiranjib Bhattacharyya1 chiru@csa.iisc.ernet.in K. R. Ramakrishnan2 krr@ee.iisc.ernet.in Abstract This paper introduces kernels on attributed pointsets, which are s... | 2007 | 57 |
3,295 | Testing for Homogeneity with Kernel Fisher Discriminant Analysis Za¨ıd Harchaoui LTCI, TELECOM ParisTech and CNRS 46, rue Barrault, 75634 Paris cedex 13, France zaid.harchaoui@enst.fr Francis Bach Willow Project, INRIA-ENS 45, rue d’Ulm, 75230 Paris, France francis.bach@mines.org ´Eric Moulines LT... | 2007 | 58 |
3,296 | Sparse deep belief net model for visual area V2 Honglak Lee Chaitanya Ekanadham Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 {hllee,chaitu,ang}@cs.stanford.edu Abstract Motivated in part by the hierarchical organization of the cortex, a number of algorithms have rece... | 2007 | 59 |
3,297 | Loop Series and Bethe Variational Bounds in Attractive Graphical Models Erik B. Sudderth and Martin J. Wainwright Electrical Engineering & Computer Science, University of California, Berkeley sudderth@eecs.berkeley.edu, wainwrig@eecs.berkeley.edu Alan S. Willsky Electrical Engineering & Computer Science, Ma... | 2007 | 6 |
3,298 | Second Order Bilinear Discriminant Analysis for single-trial EEG analysis Christoforos Christoforou Department of Computer Science The Graduate Center of the City University of New York 365 Fifth Avenue New York, NY 10016-4309 cchristoforou@gc.cuny.edu Paul Sajda Department of Biomedical Engineering ... | 2007 | 60 |
3,299 | Convex Relaxations of Latent Variable Training Yuhong Guo and Dale Schuurmans Department of Computing Science University of Alberta {yuhong, dale}@cs.ualberta.ca Abstract We investigate a new, convex relaxation of an expectation-maximization (EM) variant that approximates a standard objective while elimin... | 2007 | 61 |
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