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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3,900 | Movement extraction by detecting dynamics switches and repetitions Silvia Chiappa Statistical Laboratory Wilberforce Road, Cambridge, UK silvia@statslab.cam.ac.uk Jan Peters Max Planck Institute for Biological Cybernetics Spemannstrasse 38, Tuebingen, Germany jan.peters@tuebingen.mpg.de Abstract M... | 2010 | 141 |
3,901 | Exact inference and learning for cumulative distribution functions on loopy graphs Jim C. Huang, Nebojsa Jojic and Christopher Meek Microsoft Research One Microsoft Way, Redmond, WA 98052 Abstract Many problem domains including climatology and epidemiology require models that can capture both heavy-tailed... | 2010 | 142 |
3,902 | Spectral Regularization for Support Estimation Ernesto De Vito DSA, Univ. di Genova, and INFN, Sezione di Genova, Italy devito@dima.ungie.it Lorenzo Rosasco CBCL - MIT, - USA, and IIT, Italy lrosasco@mit.edu Alessandro Toigo Politec. di Milano, Dept. of Math., and INFN, Sezione di Milano, Italy ... | 2010 | 143 |
3,903 | Online Learning for Latent Dirichlet Allocation Matthew D. Hoffman Department of Computer Science Princeton University Princeton, NJ mdhoffma@cs.princeton.edu David M. Blei Department of Computer Science Princeton University Princeton, NJ blei@cs.princeton.edu Francis Bach INRIA—Ecole Normale Su... | 2010 | 144 |
3,904 | Random Projections for k-means Clustering Christos Boutsidis Department of Computer Science RPI Anastasios Zouzias Department of Computer Science University of Toronto Petros Drineas Department of Computer Science RPI Abstract This paper discusses the topic of dimensionality reduction for k-means ... | 2010 | 145 |
3,905 | Inference and communication in the game of Password Yang Xu∗and Charles Kemp† Machine Learning Department∗ School of Computer Science∗ Department of Psychology† Carnegie Mellon University {yx1@cs.cmu.edu, ckemp@cmu.edu} Abstract Communication between a speaker and hearer will be most efficient when bot... | 2010 | 146 |
3,906 | Smoothness, Low-Noise and Fast Rates Nathan Srebro Karthik Sridharan nati@ttic.edu karthik@ttic.edu Toyota Technological Institute at Chicago Ambuj Tewari ambuj@cs.utexas.edu Computer Science Dept., University of Texas at Austin Abstract We establish an excess risk bound of ˜O HR2 n + √ ... | 2010 | 147 |
3,907 | Energy Disaggregation via Discriminative Sparse Coding J. Zico Kolter Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 kolter@csail.mit.edu Siddarth Batra, Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 9430... | 2010 | 148 |
3,908 | Random Conic Pursuit for Semidefinite Programming Ariel Kleiner Computer Science Division Univerisity of California Berkeley, CA 94720 akleiner@cs.berkeley.edu Ali Rahimi Intel Research Berkeley Berkeley, CA 94720 ali.rahimi@intel.com Michael I. Jordan Computer Science Division University of Cali... | 2010 | 149 |
3,909 | A Computational Decision Theory for Interactive Assistants Alan Fern School of EECS Oregon State University Corvallis, OR 97331 afern@eecs.oregonstate.edu Prasad Tadepalli School of EECS Oregon State University Corvallis, OR 97331 tadepall@eecs.oregonstate.edu Abstract We study several classes... | 2010 | 15 |
3,910 | Deterministic Single-Pass Algorithm for LDA Issei Sato University of Tokyo, Japan sato@r.dl.itc.u-tokyo.ac.jp Kenichi Kurihara Google kenichi.kurihara@gmail.com Hiroshi Nakagawa University of Tokyo, Japan n3@dl.itc.u-tokyo.ac.jp Abstract We develop a deterministic single-pass algorithm for latent ... | 2010 | 150 |
3,911 | A Bayesian Framework for Figure-Ground Interpretation Vicky Froyen ∗Center for Cognitive Science Rutgers University, Piscataway, NJ 08854 Laboratory of Experimental Psychology University of Leuven (K.U. Leuven), Belgium vicky.froyen@eden.rutgers.edu Jacob Feldman Center for Cognitive Science Rutgers... | 2010 | 151 |
3,912 | Online Markov Decision Processes under Bandit Feedback Gergely Neu∗† ∗Department of Computer Science and Information Theory, Budapest University of Technology and Economics, Hungary neu.gergely@gmail.com Andr´as Gy¨orgy †Machine Learning Research Group MTA SZTAKI Institute for Computer Science and C... | 2010 | 152 |
3,913 | A Log-Domain Implementation of the Diffusion Network in Very Large Scale Integration Yi-Da Wu, Shi-Jie Lin, and Hsin Chen Department of Electrical Engineering National Tsing Hua University Hsinchu, Taiwan 30013 {ydwu;hchen}@ee.nthu.edu.tw Abstract The Diffusion Network(DN) is a stochastic recurrent netw... | 2010 | 153 |
3,914 | SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system Sylvain Chevallier TAO, INRIA-Saclay Univ. Paris-Sud F-91405 Orsay, France sylchev@lri.fr H´el`ene Paugam-Moisy LIRIS, CNRS Univ. Lyon 2 F-69676 Bron, France hpaugam@liris.cnrs.fr Mich`ele Sebag TAO... | 2010 | 154 |
3,915 | Permutation Complexity Bound on Out-Sample Error Malik Magdon-Ismail Computer Science Department Rensselaer Ploytechnic Institute 110 8th Street, Troy, NY 12180, USA magdon@cs.rpi.edu Abstract We define a data dependent permutation complexity for a hypothesis set H, which is similar to a Rademacher compl... | 2010 | 155 |
3,916 | Fast global convergence of gradient methods for high-dimensional statistical recovery Alekh Agarwal1 Sahand N. Negahban1 Martin J. Wainwright1,2 Department of Electrical Engineering and Computer Science1 and Department of Statistics2 University of California, Berkeley Berkeley, CA 94720-1776 {alekh,saha... | 2010 | 156 |
3,917 | Attractor Dynamics with Synaptic Depression C. C. Alan Fung, K. Y. Michael Wong Hong Kong University of Science and Technology, Hong Kong, China alanfung@ust.hk, phkywong@ust.hk He Wang Tsinghua University, Beijing, China wanghe07@mails.tsinghua.edu.cn Si Wu Institute of Neuroscience, Chinese Acad... | 2010 | 157 |
3,918 | Layer-wise analysis of deep networks with Gaussian kernels Gr´egoire Montavon Machine Learning Group TU Berlin gmontavon@cs.tu-berlin.de Mikio L. Braun Machine Learning Group TU Berlin mikio@cs.tu-berlin.de Klaus-Robert M¨uller Machine Learning Group TU Berlin krm@cs.tu-berlin.de Abstract ... | 2010 | 158 |
3,919 | Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake Stefan Harmeling, Michael Hirsch, and Bernhard Sch¨olkopf Max Planck Institute for Biological Cybernetics, T¨ubingen, Germany firstname.lastname@tuebingen.mpg.de Abstract Modelling camera shake as a space-invariant convolution simplifie... | 2010 | 159 |
3,920 | Large Margin Multi-Task Metric Learning Shibin Parameswaran Department of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 92093 sparames@ucsd.edu Kilian Q. Weinberger Department of Computer Science and Engineering Washington University in St. Louis St. Louis, MO 63... | 2010 | 16 |
3,921 | Sodium entry efficiency during action potentials: A novel single-parameter family of Hodgkin-Huxley models Anand Singh Institute of Pharmacology and Toxicology University of Z¨urich, Z¨urich, Switzerland anands@pharma.uzh.ch Renaud Jolivet∗ Institute of Pharmacology and Toxicology University of Z¨urich... | 2010 | 160 |
3,922 | Global Analytic Solution for Variational Bayesian Matrix Factorization 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 Ryota Tomioka The University of Tokyo Tokyo 113-8685,... | 2010 | 161 |
3,923 | Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices Yi Sun IDSIA Galleria 2, Manno CH-6928, Switzerland yi@idsia.ch Faustino Gomez IDSIA Galleria 2, Manno CH-6928, Switzerland tino@idsia.ch J¨urgen Schmidhuber IDSIA Galleria 2, Manno CH-6928, Switzerland juer... | 2010 | 162 |
3,924 | Linear Complementarity for Regularized Policy Evaluation and Improvement Jeff Johns Christopher Painter-Wakefield Ronald Parr Department of Computer Science Duke University Durham, NC 27708 {johns, paint007, parr}@cs.duke.edu Abstract Recent work in reinforcement learning has emphasized the power of ... | 2010 | 163 |
3,925 | Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks Dirk Husmeier & Frank Dondelinger Biomathematics & Statistics Scotland (BioSS) JCMB, The King’s Buildings, Edinburgh EH93JZ, United Kingdom dirk@bioss.ac.uk, frank@bioss.ac.uk Sophie L`ebre Universit´e de Strasbourg, LSII... | 2010 | 164 |
3,926 | Graph-Valued Regression Han Liu Xi Chen John Lafferty Larry Wasserman Carnegie Mellon University Pittsburgh, PA 15213 Abstract Undirected graphical models encode in a graph G the dependency structure of a random vector Y . In many applications, it is of interest to model Y given another random vector X as i... | 2010 | 165 |
3,927 | Probabilistic Multi-Task Feature Selection Yu Zhang1, Dit-Yan Yeung1, Qian Xu2 1Department of Computer Science and Engineering, 2Bioengineering Program Hong Kong University of Science and Technology {zhangyu,dyyeung}@cse.ust.hk, fleurxq@ust.hk Abstract Recently, some variants of the 푙1 norm, particularly ma... | 2010 | 166 |
3,928 | Penalized Principal Component Regression on Graphs for Analysis of Subnetworks Ali Shojaie Department of Statistics University of Michigan Ann Arbor, MI 48109 shojaie@umich.edu George Michailidis Department of Statistics and EECS University of Michigan Ann Arbor, MI 48109 gmichail@umich.edu Abst... | 2010 | 167 |
3,929 | Bayesian Action-Graph Games Albert Xin Jiang Department of Computer Science University of British Columbia jiang@cs.ubc.ca Kevin Leyton-Brown Department of Computer Science University of British Columbia kevinlb@cs.ubc.ca Abstract Games of incomplete information, or Bayesian games, are an important ... | 2010 | 168 |
3,930 | A Family of Penalty Functions for Structured Sparsity Charles A. Micchelli∗ Department of Mathematics City University of Hong Kong 83 Tat Chee Avenue, Kowloon Tong Hong Kong charles micchelli@hotmail.com Jean M. Morales Department of Computer Science University College London Gower Street, London ... | 2010 | 169 |
3,931 | Evaluating neuronal codes for inference using Fisher information Ralf M. Haefner∗and Matthias Bethge Centre for Integrative Neuroscience, University of T¨ubingen, Bernstein Center for Computational Neuroscience, T¨ubingen, Max Planck Institute for Biological Cybernetics Spemannstr. 41, 72076 T¨ubingen, Germ... | 2010 | 17 |
3,932 | Spike timing-dependent plasticity as dynamic filter Joscha T. Schmiedt∗, Christian Albers and Klaus Pawelzik Institute for Theoretical Physics University of Bremen Bremen, Germany schmiedt@uni-bremen.de, {calbers, pawelzik}@neuro.uni-bremen.de Abstract When stimulated with complex action potential sequence... | 2010 | 170 |
3,933 | The Neural Costs of Optimal Control Samuel J. Gershman and Robert C. Wilson Psychology Department and Neuroscience Institute Princeton University Princeton, NJ 08540 {sjgershm,rcw2}@princeton.edu Abstract Optimal control entails combining probabilities and utilities. However, for most practical problems... | 2010 | 171 |
3,934 | Sample complexity of testing the manifold hypothesis Hariharan Narayanan∗ Laboratory for Information and Decision Systems EECS, MIT Cambridge, MA 02139 har@mit.edu Sanjoy Mitter Laboratory for Information and Decision Systems EECS, MIT Cambridge, MA 02139 mitter@mit.edu Abstract The hypothesis t... | 2010 | 172 |
3,935 | A biologically plausible network for the computation of orientation dominance Kritika Muralidharan Statistical Visual Computing Laboratory University of California San Diego La Jolla, CA 92039 krmurali@ucsd.edu Nuno Vasconcelos Statistical Visual Computing Laboratory University of California San Diego... | 2010 | 173 |
3,936 | A primal-dual algorithm for group sparse regularization with overlapping groups Sofia Mosci DISI- Universit`a di Genova mosci@disi.unige.it Silvia Villa DISI- Universit`a di Genova villa@dima.unige.it Alessandro Verri DISI- Universit`a di Genova verri@disi.unige.it Lorenzo Rosasco IIT - MIT lro... | 2010 | 174 |
3,937 | Heavy-Tailed Process Priors for Selective Shrinkage Fabian L. Wauthier University of California, Berkeley flw@cs.berkeley.edu Michael I. Jordan University of California, Berkeley jordan@cs.berkeley.edu Abstract Heavy-tailed distributions are often used to enhance the robustness of regression and class... | 2010 | 175 |
3,938 | A New Probabilistic Model for Rank Aggregation Tao Qin Microsoft Research Asia taoqin@microsoft.com Xiubo Geng Chinese Academy of Sciences xiubogeng@gmail.com Tie-Yan Liu Microsoft Research Asia tyliu@microsoft.com Abstract This paper is concerned with rank aggregation, which aims to combine multi... | 2010 | 176 |
3,939 | Learning Networks of Stochastic Differential Equations Jos´e Bento Department of Electrical Engineering Stanford University Stanford, CA 94305 jbento@stanford.edu Morteza Ibrahimi Department of Electrical Engineering Stanford University Stanford, CA 94305 ibrahimi@stanford.edu Andrea Montanari ... | 2010 | 177 |
3,940 | Predictive Subspace Learning for Multi-view Data: a Large Margin Approach Ning Chen†‡ Jun Zhu‡ Eric P. Xing‡ †chenn07@mails.tsinghua.edu.cn, ‡{ningchen,junzhu,epxing}@cs.cmu.edu †Dept. of CS & T, TNList Lab, State Key Lab of ITS, Tsinghua University, Beijing 100084 China ‡School of Computer Science, Carne... | 2010 | 178 |
3,941 | A Bayesian Approach to Concept Drift Stephen H. Bach Marcus A. Maloof Department of Computer Science Georgetown University Washington, DC 20007, USA {bach, maloof}@cs.georgetown.edu Abstract To cope with concept drift, we placed a probability distribution over the location of the most-recent drift poi... | 2010 | 179 |
3,942 | Guaranteed Rank Minimization via Singular Value Projection Prateek Jain Microsoft Research Bangalore Bangalore, India prajain@microsoft.com Raghu Meka UT Austin Dept. of Computer Sciences Austin, TX, USA raghu@cs.utexas.edu Inderjit Dhillon UT Austin Dept. of Computer Sciences Austin, TX, USA ... | 2010 | 18 |
3,943 | Multivariate Dyadic Regression Trees for Sparse Learning Problems Han Liu and Xi Chen School of Computer Science, Carnegie Mellon University Pittsburgh, PA 15213 Abstract We propose a new nonparametric learning method based on multivariate dyadic regression trees (MDRTs). Unlike traditional dyadic decisio... | 2010 | 180 |
3,944 | A novel family of non-parametric cumulative based divergences for point processes Sohan Seth University of Florida Il “Memming” Park University of Texas at Austin Austin J. Brockmeier University of Florida Mulugeta Semework SUNY Downstate Medical Center John Choi, Joseph T. Francis SUNY Downstate ... | 2010 | 181 |
3,945 | Sidestepping Intractable Inference with Structured Ensemble Cascades David Weiss∗ Benjamin Sapp∗ Ben Taskar Computer and Information Science University of Pennsylvania Philadelphia, PA 19104, USA {djweiss,bensapp,taskar}@cis.upenn.edu Abstract For many structured prediction problems, complex models ... | 2010 | 182 |
3,946 | Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings 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.c... | 2010 | 183 |
3,947 | t-Logistic Regression Nan Ding2, S.V. N. Vishwanathan1,2 Departments of 1Statistics and 2Computer Science Purdue University ding10@purdue.edu, vishy@stat.purdue.edu Abstract We extend logistic regression by using t-exponential families which were introduced recently in statistical physics. This gives rise t... | 2010 | 184 |
3,948 | Occlusion Detection and Motion Estimation with Convex Optimization Alper Ayvaci, Michalis Raptis, Stefano Soatto University of California, Los Angeles {ayvaci, mraptis, soatto}@cs.ucla.edu Abstract We tackle the problem of simultaneously detecting occlusions and estimating optical flow. We show that, und... | 2010 | 185 |
3,949 | Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach Alessandro Bergamo Lorenzo Torresani Computer Science Department Dartmouth College Hanover, NH 03755, U.S.A. {aleb, lorenzo}@cs.dartmouth.edu Abstract Most current image categorization methods require la... | 2010 | 186 |
3,950 | Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression Ling Huang Intel Labs Berkeley ling.huang@intel.com Jinzhu Jia UC Berkeley jzjia@stat.berkeley.edu Bin Yu UC Berkeley binyu@stat.berkeley.edu Byung-Gon Chun Intel Labs Berkeley byung-gon.chun@intel.com Petros M... | 2010 | 187 |
3,951 | Humans Learn Using Manifolds, Reluctantly Bryan R. Gibson, Xiaojin Zhu, Timothy T. Rogers∗, Charles W. Kalish†, Joseph Harrison∗ Department of Computer Sciences, ∗Psychology, and †Educational Psychology University of Wisconsin-Madison, Madison, WI 53706 USA {bgibson, jerryzhu}@cs.wisc.edu {ttrogers, cwkalish,... | 2010 | 188 |
3,952 | Sphere Embedding: An Application to Part-of-Speech Induction Yariv Maron Michael Lamar Gonda Brain Research Center Department of Mathematics and Computer Science Bar-Ilan University Saint Louis University Ramat-Gan 52900, Israel St. Louis, MO 63103, USA syariv... | 2010 | 189 |
3,953 | Double Q-learning Hado van Hasselt Multi-agent and Adaptive Computation Group Centrum Wiskunde & Informatica Abstract In some stochastic environments the well-known reinforcement learning algorithm Q-learning performs very poorly. This poor performance is caused by large overestimations of action values. Th... | 2010 | 19 |
3,954 | Tight Sample Complexity of Large-Margin Learning Sivan Sabato1 Nathan Srebro2 Naftali Tishby1 1 School of Computer Science & Engineering, The Hebrew University, Jerusalem 91904, Israel 2 Toyota Technological Institute at Chicago, Chicago, IL 60637, USA {sivan sabato,tishby}@cs.huji.ac.il, nati@ttic.edu Ab... | 2010 | 190 |
3,955 | Minimum Average Cost Clustering Kiyohito Nagano Institute of Industrial Science University of Tokyo, Japan nagano@sat.t.u-tokyo.ac.jp Yoshinobu Kawahara The Institute of Scientific and Industrial Research Osaka University, Japan kawahara@ar.sanken.osaka-u.ac.jp Satoru Iwata Research Institute for Mat... | 2010 | 191 |
3,956 | Online Classification with Specificity Constraints Andrey Bernstein Department of Electrical Engineering Technion - Israel Institute of Technology Haifa, 32000, Israel andreyb@tx.technion.ac.il Shie Mannor Department of Electrical Engineering Technion - Israel Institute of Technology Haifa, 32000, Israe... | 2010 | 192 |
3,957 | Construction of Dependent Dirichlet Processes based on Poisson Processes Dahua Lin CSAIL, MIT dhlin@mit.edu Eric Grimson CSAIL, MIT welg@csail.mit.edu John Fisher CSAIL, MIT fisher@csail.mit.edu Abstract We present a novel method for constructing dependent Dirichlet processes. The approach exp... | 2010 | 193 |
3,958 | Practical Large-Scale Optimization for Max-Norm Regularization Jason Lee Institute of Computational and Mathematical Engineering Stanford University email: jl115@yahoo.com Benjamin Recht Department of Computer Sciences University of Wisconsin-Madison email: brecht@cs.wisc.edu Ruslan Salakhutdinov ... | 2010 | 194 |
3,959 | Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication Guy Isely Redwood Center for Theoretical Neuroscience University of California, Berkeley guyi@berkeley.edu Christopher J. Hillar Mathematical Sciences Research Institute chillar@msri.org Friedrich T. Somme... | 2010 | 195 |
3,960 | Learning Convolutional Feature Hierarchies for Visual Recognition Koray Kavukcuoglu1, Pierre Sermanet1, Y-Lan Boureau2,1, Karol Gregor1, Micha¨el Mathieu1, Yann LeCun1 1 Courant Institute of Mathematical Sciences, New York University 2 INRIA - Willow project-team∗ {koray,sermanet,ylan,kgregor,yann}@... | 2010 | 196 |
3,961 | Multiple Kernel Learning and the SMO Algorithm S. V. N. Vishwanathan, Zhaonan Sun, Nawanol Theera-Ampornpunt Purdue University vishy@stat.purdue.edu, sunz@stat.purdue.edu, ntheeraa@cs.purdue.edu Manik Varma Microsoft Research India manik@microsoft.com Abstract Our objective is to train p-norm Multiple K... | 2010 | 197 |
3,962 | Segmentation as Maximum-Weight Independent Set William Brendel and Sinisa Todorovic School of Electrical Engineering and Computer Science Oregon State University Corvallis, OR 97331 brendelw@onid.orst.edu, sinisa@eecs.oregonstate.edu Abstract Given an ensemble of distinct, low-level segmentations of an im... | 2010 | 198 |
3,963 | Static Analysis of Binary Executables Using Structural SVMs Nikos Karampatziakis∗ Department of Computer Science Cornell University Ithaca, NY 14853 nk@cs.cornell.edu Abstract We cast the problem of identifying basic blocks of code in a binary executable as learning a mapping from a byte sequence to a... | 2010 | 199 |
3,964 | A Dirty Model for Multi-task Learning Ali Jalali University of Texas at Austin alij@mail.utexas.edu Pradeep Ravikumar University of Texas at Asutin pradeepr@cs.utexas.edu Sujay Sanghavi University of Texas at Austin sanghavi@mail.utexas.edu Chao Ruan University of Texas at Austin ruan@cs.utexas.... | 2010 | 2 |
3,965 | Generalized roof duality and bisubmodular functions Vladimir Kolmogorov Department of Computer Science University College London, UK v.kolmogorov@cs.ucl.ac.uk Abstract Consider a convex relaxation ˆf of a pseudo-boolean function f. We say that the relaxation is totally half-integral if ˆf(x) is a polyhedr... | 2010 | 20 |
3,966 | Factorized Latent Spaces with Structured Sparsity Yangqing Jia1, Mathieu Salzmann1,2, and Trevor Darrell1 1UC Berkeley EECS and ICSI 2TTI-Chicago {jiayq,trevor}@eecs.berkeley.edu, salzmann@ttic.edu Abstract Recent approaches to multi-view learning have shown that factorizing the information into parts that ... | 2010 | 200 |
3,967 | A unified model of short-range and long-range motion perception Shuang Wu Department of Statistics UCLA Los Angeles , CA 90095 shuangw@stat.ucla.edu Xuming He Department of Statistics UCLA Los Angeles , CA 90095 hexm@stat.ucla.edu Hongjing Lu Department of Psychology UCLA Los Angeles , CA 9... | 2010 | 201 |
3,968 | Structural epitome: A way to summarize one’s visual experience Nebojsa Jojic Microsoft Research Alessandro Perina Microsoft Research University of Verona Vittorio Murino Italian Institute of Technology University of Verona Abstract In order to study the properties of total visual input in humans, ... | 2010 | 202 |
3,969 | Tree-Structured Stick Breaking for Hierarchical Data Ryan Prescott Adams∗ Dept. of Computer Science University of Toronto Zoubin Ghahramani Dept. of Engineering University of Cambridge Michael I. Jordan Depts. of EECS and Statistics University of California, Berkeley Abstract Many data are natural... | 2010 | 203 |
3,970 | LSTD with Random Projections Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric-Ambrym Maillard, R´emi Munos INRIA Lille - Nord Europe, Team SequeL, France Abstract We consider the problem of reinforcement learning in high-dimensional spaces when the number of features is bigger than the number of samples. In ... | 2010 | 204 |
3,971 | Feature Construction for Inverse Reinforcement Learning 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 The goal of inverse reinforcement learning is to ... | 2010 | 205 |
3,972 | An analysis on negative curvature induced by singularity in multi-layer neural-network learning Eiji Mizutani Department of Industrial Management Taiwan Univ. of Science & Technology eiji@mail.ntust.edu.tw Stuart Dreyfus Industrial Engineering & Operations Research University of California, Berkeley d... | 2010 | 206 |
3,973 | Joint Cascade Optimization Using a Product of Boosted Classifiers Leonidas Lefakis Idiap Research Institute Martigny, Switzerland leonidas.lefakis@idiap.ch Franc¸ois Fleuret Idiap Research Institute Martigny, Switzerland francois.fleuret@idiap.ch Abstract The standard strategy for efficient object d... | 2010 | 207 |
3,974 | Parallelized Stochastic Gradient Descent Martin A. Zinkevich Yahoo! Labs Sunnyvale, CA 94089 maz@yahoo-inc.com Markus Weimer Yahoo! Labs Sunnyvale, CA 94089 weimer@yahoo-inc.com Alex Smola Yahoo! Labs Sunnyvale, CA 94089 smola@yahoo-inc.com Lihong Li Yahoo! Labs Sunnyvale, CA 94089 lihon... | 2010 | 208 |
3,975 | Shadow Dirichlet for Restricted Probability Modeling Bela A. Frigyik, Maya R. Gupta, and Yihua Chen Department of Electrical Engineering University of Washington Seattle, WA 98195 frigyik@gmail.com, gupta@ee.washington.edu, yihuachn@gmail.com Abstract Although the Dirichlet distribution is widely used, th... | 2010 | 209 |
3,976 | Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization Feiping Nie Computer Science and Engineering University of Texas at Arlington feipingnie@gmail.com Heng Huang Computer Science and Engineering University of Texas at Arlington heng@uta.edu Xiao Cai Computer Science and Enginee... | 2010 | 21 |
3,977 | MAP estimation in Binary MRFs via Bipartite Multi-cuts Sashank J. Reddi∗ Sunita Sarawagi Sundar Vishwanathan IIT Bombay IIT Bombay IIT Bombay sashank@cse.iitb.ac.in sunita@cse.iitb.ac.in sundar@cse.iitb.ac.in Abstract We propose a new LP relaxation for obtaining the MAP assignment of a binary ... | 2010 | 210 |
3,978 | Approximate Inference by Compilation to Arithmetic Circuits Daniel Lowd Department of Computer and Information Science University of Oregon Eugene, OR 97403-1202 lowd@cs.uoregon.edu Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350 pedrod... | 2010 | 211 |
3,979 | Random Walk Approach to Regret Minimization Hariharan Narayanan MIT Cambridge, MA 02139 har@mit.edu Alexander Rakhlin University of Pennsylvania Philadelphia, PA 19104 rakhlin@wharton.upenn.edu Abstract We propose a computationally efficient random walk on a convex body which rapidly mixes to a tim... | 2010 | 212 |
3,980 | Unsupervised Kernel Dimension Reduction Meihong Wang Dept. of Computer Science U. of Southern California Los Angeles, CA 90089 meihongw@usc.edu Fei Sha Dept. of Computer Science U. of Southern California Los Angeles, CA 90089 feisha@usc.edu Michael I. Jordan Dept. of Statistics U. of Californi... | 2010 | 213 |
3,981 | Multitask Learning without Label Correspondences Novi Quadrianto1, Alex Smola2, Tib´erio Caetano1, S.V.N. Vishwanathan3, James Petterson1 1 SML-NICTA & RSISE-ANU, Canberra, ACT, Australia 2 Yahoo! Research, Santa Clara, CA, USA 3 Purdue University, West Lafayette, IN, USA Abstract We propose an algorithm to... | 2010 | 214 |
3,982 | CUR from a Sparse Optimization Viewpoint Jacob Bien∗ Department of Statistics Stanford University Stanford, CA 94305 jbien@stanford.edu Ya Xu∗ Department of Statistics Stanford University Stanford, CA 94305 yax.stanford@gmail.com Michael W. Mahoney Department of Mathematics Stanford University... | 2010 | 215 |
3,983 | Robust Clustering as Ensembles of Affinity Relations Hairong Liu1, Longin Jan Latecki2, Shuicheng Yan1 1Department of Electrical and Computer Engineering, National University of Singapore, Singapore 2Department of Computer and Information Sciences, Temple University, Philadelphia, USA lhrbss@gmail.com,latecki@te... | 2010 | 216 |
3,984 | Optimal learning rates for Kernel Conjugate Gradient regression Gilles Blanchard Mathematics Institute, University of Potsdam Am neuen Palais 10, 14469 Potsdam blanchard@math.uni-potsdam.de Nicole Kr¨amer Weierstrass Institute Mohrenstr. 39, 10117 Berlin, Germany nicole.kraemer@wias-berlin.de Abstra... | 2010 | 217 |
3,985 | Rates of convergence for the cluster tree Kamalika Chaudhuri UC San Diego kchaudhuri@ucsd.edu Sanjoy Dasgupta UC San Diego dasgupta@cs.ucsd.edu Abstract For a density f on Rd, a high-density cluster is any connected component of {x : f(x) ≥λ}, for some λ > 0. The set of all high-density clusters form ... | 2010 | 218 |
3,986 | Throttling Poisson Processes Uwe Dick Peter Haider Thomas Vanck Michael Br¨uckner Tobias Scheffer University of Potsdam Department of Computer Science August-Bebel-Strasse 89, 14482 Potsdam, Germany {uwedick,haider,vanck,mibrueck,scheffer}@cs.uni-potsdam.de Abstract We study a setting in which Poi... | 2010 | 219 |
3,987 | Repeated Games against Budgeted Adversaries Jacob Abernethy∗ Division of Computer Science UC Berkeley jake@cs.berkeley.edu Manfred K. Warmuth† Department of Computer Science UC Santa Cruz manfred@cse.ucsc.edu Abstract We study repeated zero-sum games against an adversary on a budget. Given that an... | 2010 | 22 |
3,988 | An Approximate Inference Approach to Temporal Optimization in Optimal Control Konrad C. Rawlik School of Informatics University of Edinburgh Edinburgh, UK Marc Toussaint TU Berlin Berlin, Germany Sethu Vijayakumar School of Informatics University of Edinburgh Edinburgh, UK Abstract Algorithm... | 2010 | 220 |
3,989 | Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine George E. Dahl, Marc’Aurelio Ranzato, Abdel-rahman Mohamed, and Geoffrey Hinton Department of Computer Science University of Toronto {gdahl, ranzato, asamir, hinton}@cs.toronto.edu Abstract Straightforward application of Deep Belief N... | 2010 | 221 |
3,990 | Sparse Coding for Learning Interpretable Spatio-Temporal Primitives Taehwan Kim TTI Chicago taehwan@ttic.edu Gregory Shakhnarovich TTI Chicago gregory@ttic.edu Raquel Urtasun TTI Chicago rurtasun@ttic.edu Abstract Sparse coding has recently become a popular approach in computer vision to learn ... | 2010 | 222 |
3,991 | Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference Aur´elie C. Lozano, Vikas Sindhwani IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights NY 10598,USA {aclozano,vsindhw}@us.ibm.com Abstract We consider multivariate regression problems involving ... | 2010 | 223 |
3,992 | Reverse Multi-Label Learning James Petterson NICTA, Australian National University Canberra, ACT, Australia james.petterson@nicta.com.au Tiberio Caetano NICTA, Australian National University Canberra, ACT, Australia tiberio.caetano@nicta.com.au Abstract Multi-label classification is the task of predi... | 2010 | 224 |
3,993 | Efficient Relational Learning with Hidden Variable Detection Ni Lao, Jun Zhu, Liu Liu, Yandong Liu, William W. Cohen Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213 {nlao,junzhu,liuliu,yandongl,wcohen}@cs.cmu.edu Abstract Markov networks (MNs) can incorporate arbitrarily complex feature... | 2010 | 225 |
3,994 | Learning from Logged Implicit Exploration Data Alexander L. Strehl ∗ Facebook Inc. 1601 S California Ave Palo Alto, CA 94304 astrehl@facebook.com John Langford Yahoo! Research 111 West 40th Street, 9th Floor New York, NY, USA 10018 jl@yahoo-inc.com Lihong Li Yahoo! Research 4401 Great America ... | 2010 | 226 |
3,995 | Parametric Bandits: The Generalized Linear Case Sarah Filippi LTCI Telecom ParisTech et CNRS Paris, France filippi@telecom-paristech.fr Olivier Capp´e LTCI Telecom ParisTech et CNRS Paris, France cappe@telecom-paristech.fr Aur´elien Garivier LTCI Telecom ParisTech et CNRS Paris, France g... | 2010 | 227 |
3,996 | Basis Construction from Power Series Expansions of Value Functions Sridhar Mahadevan Department of Computer Science University of Massachusetts Amherst, MA 01003 mahadeva@cs.umass.edu Bo Liu Department of Computer Science University of Massachusetts Amherst, MA 01003 boliu@cs.umass.edu Abstract ... | 2010 | 228 |
3,997 | A Novel Kernel for Learning a Neuron Model from Spike Train Data Nicholas Fisher, Arunava Banerjee Department of Computer and Information Science and Engineering University of Florida Gainesville, FL 32611 {nfisher,arunava}@cise.ufl.edu Abstract From a functional viewpoint, a spiking neuron is a device ... | 2010 | 229 |
3,998 | 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 05... | 2010 | 23 |
3,999 | Nonparametric Bayesian Policy Priors for Reinforcement Learning Finale Doshi-Velez, David Wingate, Nicholas Roy and Joshua Tenenbaum Massachusetts Institute of Technology Cambridge, MA 02139 {finale,wingated,nickroy,jbt}@csail.mit.edu Abstract We consider reinforcement learning in partially observable dom... | 2010 | 230 |
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