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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5,100 | Annealing Between Distributions by Averaging Moments Roger Grosse Comp. Sci. & AI Lab MIT Cambridge, MA 02139 Chris J. Maddison Dept. of Computer Science University of Toronto Toronto, ON M5S 3G4 Ruslan Salakhutdinov Depts. of Statistics and Comp. Sci., University of Toronto Toronto, ON M5S 3G... | 2013 | 352 |
5,101 | Blind Calibration in Compressed Sensing using Message Passing Algorithms Christophe Sch¨ulke Univ Paris Diderot, Sorbonne Paris Cit´e, ESPCI and CNRS UMR 7083 Paris 75005, France Francesco Caltagirone Institut de Physique Th´eorique CEA Saclay and CNRS URA 2306 91191 Gif-sur-Yvette, France Florent K... | 2013 | 353 |
5,102 | Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion Nguyen Viet Cuong Wee Sun Lee Nan Ye Department of Computer Science National University of Singapore {nvcuong,leews,yenan}@comp.nus.edu.sg Kian Ming A. Chai Hai Leong Chieu DSO National Laboratories, Singapore {ck... | 2013 | 354 |
5,103 | Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards Thomas Bonald∗ Department of Networking and Computer Science Telecom ParisTech Paris, France thomas.bonald@telecom-paristech.fr Alexandre Prouti`ere∗† Automatic Control Department KTH Stockholm, Sweden alepro@kth.se Abstract ... | 2013 | 355 |
5,104 | Learning to Prune in Metric and Non-Metric Spaces Leonid Boytsov Carnegie Mellon University Pittsburgh, PA, USA srchvrs@cmu.edu Bilegsaikhan Naidan Norwegian University of Science and Technology Trondheim, Norway bileg@idi.ntnu.no Abstract Our focus is on approximate nearest neighbor retrieval in me... | 2013 | 356 |
5,105 | Learning from Limited Demonstrations Beomjoon Kim School of Computer Science McGill University Montreal, Quebec, Canada Amir-massoud Farahmand School of Computer Science McGill University Montreal, Quebec, Canada Joelle Pineau School of Computer Science McGill University Montreal, Quebec, Canada... | 2013 | 357 |
5,106 | Aggregating Optimistic Planning Trees for Solving Markov Decision Processes Gunnar Kedenburg INRIA Lille - Nord Europe / idalab GmbH gunnar.kedenburg@inria.fr Raphaël Fonteneau University of Liège / INRIA Lille - Nord Europe raphael.fonteneau@ulg.ac.be Rémi Munos INRIA Lille - Nord Europe / Microsoft ... | 2013 | 358 |
5,107 | Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths Stefan Mathe1,3 and Cristian Sminchisescu2,1 1Institute of Mathematics of the Romanian Academy of Science 2Department of Mathematics, Faculty of Engineering, Lund University 3Department of Computer Science, Un... | 2013 | 359 |
5,108 | Extracting regions of interest from biological images with convolutional sparse block coding Marius Pachitariu1, Adam Packer2, Noah Pettit2, Henry Dagleish2, Michael Hausser2 and Maneesh Sahani1 1Gatsby Unit, UCL, UK {marius, maneesh}@gatsby.ucl.ac.uk 2The Wolfson Institute for Biomedical Research, UCL, UK {a... | 2013 | 36 |
5,109 | How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal Jacob Abernethy University of Michigan jabernet@umich.edu Peter L. Bartlett University of California at Berkeley and Queensland University of Technology bartlett@cs.berkeley.edu Rafael M. Frongillo Microsoft Resear... | 2013 | 360 |
5,110 | Training and Analyzing Deep Recurrent Neural Networks Michiel Hermans, Benjamin Schrauwen Ghent University, ELIS departement Sint Pietersnieuwstraat 41, 9000 Ghent, Belgium michiel.hermans@ugent.be Abstract Time series often have a temporal hierarchy, with information that is spread out over multiple ... | 2013 | 37 |
5,111 | Low-Rank Matrix and Tensor Completion via Adaptive Sampling Akshay Krishnamurthy Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 akshaykr@cs.cmu.edu Aarti Singh Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 aartisingh@cs.cmu.edu Abstract ... | 2013 | 38 |
5,112 | Fast Determinantal Point Process Sampling with Application to Clustering Byungkon Kang ∗ Samsung Advanced Institute of Technology Yongin, Korea bk05.kang@samsung.com Abstract Determinantal Point Process (DPP) has gained much popularity for modeling sets of diverse items. The gist of DPP is that the prob... | 2013 | 39 |
5,113 | Transportability from Multiple Environments with Limited Experiments Elias Bareinboim∗ UCLA Sanghack Lee∗ Penn State University Vasant Honavar Penn State University Judea Pearl UCLA Abstract This paper considers the problem of transferring experimental findings learned from multiple heterogeneous... | 2013 | 4 |
5,114 | Matrix factorization with Binary Components Martin Slawski, Matthias Hein and Pavlo Lutsik Saarland University {ms,hein}@cs.uni-saarland.de, p.lutsik@mx.uni-saarland.de Abstract Motivated by an application in computational biology, we consider low-rank matrix factorization with {0, 1}-constraints on one of th... | 2013 | 40 |
5,115 | Reshaping Visual Datasets for Domain Adaptation Boqing Gong U. of Southern California Los Angeles, CA 90089 boqinggo@usc.edu Kristen Grauman U. of Texas at Austin Austin, TX 78701 grauman@cs.utexas.edu Fei Sha U. of Southern California Los Angeles, CA 90089 feisha@usc.edu Abstract In visual ... | 2013 | 41 |
5,116 | Perfect Associative Learning with Spike-Timing-Dependent Plasticity Christian Albers Institute of Theoretical Physics University of Bremen 28359 Bremen, Germany calbers@neuro.uni-bremen.de Maren Westkott Institute of Theoretical Physics University of Bremen 28359 Bremen, Germany maren@neuro.uni-br... | 2013 | 42 |
5,117 | Tracking Time-varying Graphical Structure Erich Kummerfeld Carnegie Mellon University Pittsburgh, PA 15213 ekummerf@andrew.cmu.edu David Danks Carnegie Mellon University Pittsburgh, PA 15213 ddanks@andrew.cmu.edu Abstract Structure learning algorithms for graphical models have focused almost exclusi... | 2013 | 43 |
5,118 | Phase Retrieval using Alternating Minimization Praneeth Netrapalli Department of ECE The University of Texas at Austin Austin, TX 78712 praneethn@utexas.edu Prateek Jain Microsoft Research India Bangalore, India prajain@microsoft.com Sujay Sanghavi Department of ECE The University of Texas at Au... | 2013 | 44 |
5,119 | Unsupervised Structure Learning of Stochastic And-Or Grammars Kewei Tu Maria Pavlovskaia Song-Chun Zhu Center for Vision, Cognition, Learning and Art Departments of Statistics and Computer Science University of California, Los Angeles {tukw,mariapavl,sczhu}@ucla.edu Abstract Stochastic And-Or gramma... | 2013 | 45 |
5,120 | Learning Multi-level Sparse Representations Ferran Diego Fred A. Hamprecht Heidelberg Collaboratory for Image Processing (HCI) Interdisciplinary Center for Scientific Computing (IWR) University of Heidelberg, Heidelberg 69115, Germany {ferran.diego,fred.hamprecht}@iwr.uni-heidelberg.de Abstract Bilinear ... | 2013 | 46 |
5,121 | Estimation, Optimization, and Parallelism when Data is Sparse John C. Duchi1,2 Michael I. Jordan1 University of California, Berkeley1 Berkeley, CA 94720 {jduchi,jordan}@eecs.berkeley.edu H. Brendan McMahan2 Google, Inc.2 Seattle, WA 98103 mcmahan@google.com Abstract We study stochastic optimizat... | 2013 | 47 |
5,122 | Predictive PAC Learning and Process Decompositions Cosma Rohilla Shalizi Statistics Department Carnegie Mellon University Pittsburgh, PA 15213 USA cshalizi@cmu.edu Aryeh Kontorovich Computer Science Department Ben Gurion University Beer Sheva 84105 Israel karyeh@cs.bgu.ac.il Abstract We informal... | 2013 | 48 |
5,123 | Scalable Inference for Logistic-Normal Topic Models Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng and Bo Zhang State Key Lab of Intelligent Tech. & Systems; Tsinghua National TNList Lab; Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China {chenjf10,wangzi10}@mails.tsinghua.edu.... | 2013 | 49 |
5,124 | On model selection consistency of M-estimators with geometrically decomposable penalties Jason D. Lee, Yuekai Sun Institute for Computational and Mathematical Engineering Stanford University {jdl17,yuekai}@stanford.edu Jonathan E. Taylor Department of Statisticis Stanford University jonathan.taylor@st... | 2013 | 5 |
5,125 | A multi-agent control framework for co-adaptation in brain-computer interfaces ∗Josh Merel1, ∗Roy Fox2, Tony Jebara3, Liam Paninski4 1Department of Neurobiology and Behavior, 3Department of Computer Science, 4Department of Statistics, Columbia University, New York, NY 10027 2School of Computer Science and Eng... | 2013 | 50 |
5,126 | Conditional Random Fields via Univariate Exponential Families Eunho Yang Department of Computer Science University of Texas at Austin eunho@cs.utexas.edu Pradeep Ravikumar Department of Computer Science University of Texas at Austin pradeepr@cs.utexas.edu Genevera I. Allen Department of Statistics... | 2013 | 51 |
5,127 | Adaptivity to Local Smoothness and Dimension in Kernel Regression Samory Kpotufe Toyota Technological Institute-Chicago∗ samory@ttic.edu Vikas K Garg Toyota Technological Institute-Chicago vkg@ttic.edu Abstract We present the first result for kernel regression where the procedure adapts locally at a ... | 2013 | 52 |
5,128 | Online Learning with Costly Features and Labels Navid Zolghadr Department of Computing Science University of Alberta zolghadr@ualberta.ca G´abor Bart´ok Department of Computer Science ETH Z¨urich bartok@inf.ethz.ch Russell Greiner Andr´as Gy¨orgy Csaba Szepesv´ari Department of Computing Science... | 2013 | 53 |
5,129 | An Approximate, Efficient Solver for LP Rounding Srikrishna Sridhar1, Victor Bittorf1, Ji Liu1, Ce Zhang1 Christopher R´e2, Stephen J. Wright1 1Computer Sciences Department, University of Wisconsin-Madison, Madison, WI 53706 2Computer Science Department, Stanford University, Stanford, CA 94305 {srikris,vbittor... | 2013 | 54 |
5,130 | Regression-tree Tuning in a Streaming Setting Samory Kpotufe∗ Toyota Technological Institute at Chicago† firstname@ttic.edu Francesco Orabona∗ Toyota Technological Institute at Chicago francesco@orabona.com Abstract We consider the problem of maintaining the data-structures of a partition-based regres... | 2013 | 55 |
5,131 | Estimating LASSO Risk and Noise Level Mohsen Bayati Stanford University bayati@stanford.edu Murat A. Erdogdu Stanford University erdogdu@stanford.edu Andrea Montanari Stanford University montanar@stanford.edu Abstract We study the fundamental problems of variance and risk estimation in high dimens... | 2013 | 56 |
5,132 | Demixing odors — fast inference in olfaction Agnieszka Grabska-Barwi´nska Gatsby Computational Neuroscience Unit UCL agnieszka@gatsby.ucl.ac.uk Jeff Beck Duke University jeff@gatsby.ucl.ac.uk Alexandre Pouget University of Geneva Alexandre.Pouget@unige.ch Peter E. Latham Gatsby Computational Neu... | 2013 | 57 |
5,133 | Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Y. Ng Computer Science Department, Stanford University, Stanford, CA 94305, USA richard@socher.org, {mganjoo, manning}@stanford.edu, ang@cs.stanford.edu Abstract This work introduces a model that can ... | 2013 | 58 |
5,134 | Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result Paul Wagner Department of Information and Computer Science Aalto University FI-00076 Aalto, Finland paul.wagner@aalto.fi Abstract Approximate dynamic programming approaches to the reinforcement learning p... | 2013 | 59 |
5,135 | Robust Bloom Filters for Large Multilabel Classification Tasks Moustapha Ciss´e LIP6, UPMC Sorbonne Universit´e Paris, France first.last@lip6.fr Nicolas Usunier UT Compi`egne, CNRS Heudiasyc UMR 7253 Compi`egne, France nusunier@utc.fr Thierry Artieres, Patrick Gallinari LIP6, UPMC Sorbonne Un... | 2013 | 6 |
5,136 | Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC Roger Frigola1, Fredrik Lindsten2, Thomas B. Sch¨on2,3 and Carl E. Rasmussen1 1. Dept. of Engineering, University of Cambridge, UK, {rf342,cer54}@cam.ac.uk 2. Div. of Automatic Control, Link¨oping University, Sweden, linds... | 2013 | 60 |
5,137 | Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex Sam Patterson Gatsby Computational Neuroscience Unit University College London spatterson@gatsby.ucl.ac.uk Yee Whye Teh Department of Statistics University of Oxford y.w.teh@stats.ox.ac.uk Abstract In this paper we investi... | 2013 | 61 |
5,138 | When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Animashree Anandkumar University of California Irvine, CA a.anandkumar@uci.edu Daniel Hsu Columbia University New York, NY djhsu@cs.columbia.edu Majid Janzamin University of Califo... | 2013 | 62 |
5,139 | Sign Cauchy Projections and Chi-Square Kernel Ping Li Dept of Statistics & Biostat. Dept of Computer Science Rutgers University pingli@stat.rutgers.edu Gennady Samorodnitsky ORIE and Dept of Stat. Science Cornell University Ithaca, NY 14853 gs18@cornell.edu John Hopcroft Dept of Computer Science... | 2013 | 63 |
5,140 | Transfer Learning in a Transductive Setting Marcus Rohrbach Sandra Ebert Bernt Schiele Max Planck Institute for Informatics, Saarbr¨ucken, Germany {rohrbach,ebert,schiele}@mpi-inf.mpg.de Abstract Category models for objects or activities typically rely on supervised learning requiring sufficiently large ... | 2013 | 64 |
5,141 | Solving inverse problem of Markov chain with partial observations Tetsuro Morimura IBM Research - Tokyo tetsuro@jp.ibm.com Takayuki Osogami IBM Research - Tokyo osogami@jp.ibm.com Tsuyoshi Id´e IBM T.J. Watson Research Center tide@us.ibm.com Abstract The Markov chain is a convenient tool to repr... | 2013 | 65 |
5,142 | Wavelets on Graphs via Deep Learning Raif M. Rustamov & Leonidas Guibas Computer Science Department, Stanford University {rustamov,guibas}@stanford.edu Abstract An increasing number of applications require processing of signals defined on weighted graphs. While wavelets provide a flexible tool for signal proc... | 2013 | 66 |
5,143 | Stochastic Convex Optimization with Multiple Objectives Mehrdad Mahdavi Michigan State University mahdavim@cse.msu.edu Tianbao Yang NEC Labs America, Inc tyang@nec-labs.com Rong Jin Michigan State University rongjin@cse.msu.edu Abstract In this paper, we are interested in the development of effic... | 2013 | 67 |
5,144 | Bayesian Hierarchical Community Discovery Charles Blundell∗ DeepMind Technologies charles@deepmind.com Yee Whye Teh Department of Statistics, University of Oxford y.w.teh@stats.ox.ac.uk Abstract We propose an efficient Bayesian nonparametric model for discovering hierarchical community structure in soc... | 2013 | 68 |
5,145 | Contrastive Learning Using Spectral Methods James Zou Harvard University Daniel Hsu Columbia University David Parkes Harvard University Ryan Adams Harvard University Abstract In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand... | 2013 | 69 |
5,146 | On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation Harikrishna Narasimhan Shivani Agarwal Department of Computer Science and Automation Indian Institute of Science, Bangalore 560012, India {harikrishna,shivani}@csa.iisc.ernet.in Abstract We inv... | 2013 | 7 |
5,147 | Deep Fisher Networks for Large-Scale Image Classification Karen Simonyan Andrea Vedaldi Andrew Zisserman Visual Geometry Group, University of Oxford {karen,vedaldi,az}@robots.ox.ac.uk Abstract As massively parallel computations have become broadly available with modern GPUs, deep architectures trained ... | 2013 | 70 |
5,148 | Linear Convergence with Condition Number Independent Access of Full Gradients Lijun Zhang Mehrdad Mahdavi Rong Jin Department of Computer Science and Engineering Michigan State University, East Lansing, MI 48824, USA {zhanglij,mahdavim,rongjin}@msu.edu Abstract For smooth and strongly convex optimizat... | 2013 | 71 |
5,149 | Learning with Noisy Labels Nagarajan Natarajan Inderjit S. Dhillon Pradeep Ravikumar Department of Computer Science, University of Texas, Austin. {naga86,inderjit,pradeepr}@cs.utexas.edu Ambuj Tewari Department of Statistics, University of Michigan, Ann Arbor. tewaria@umich.edu Abstract In this pape... | 2013 | 72 |
5,150 | Variational Policy Search via Trajectory Optimization Sergey Levine Stanford University svlevine@cs.stanford.edu Vladlen Koltun Stanford University and Adobe Research vladlen@cs.stanford.edu Abstract In order to learn effective control policies for dynamical systems, policy search methods must be able... | 2013 | 73 |
5,151 | Dropout Training as Adaptive Regularization Stefan Wager⇤, Sida Wang†, and Percy Liang† Departments of Statistics⇤and Computer Science† Stanford University, Stanford, CA-94305 swager@stanford.edu, {sidaw, pliang}@cs.stanford.edu Abstract Dropout and other feature noising schemes control overfitting by artific... | 2013 | 74 |
5,152 | Prior-free and prior-dependent regret bounds for Thompson Sampling S´ebastien Bubeck, Che-Yu Liu Department of Operations Research and Financial Engineering, Princeton University sbubeck@princeton.edu, cheliu@princeton.edu Abstract We consider the stochastic multi-armed bandit problem with a prior distrib... | 2013 | 75 |
5,153 | Geometric optimisation on positive definite matrices with application to elliptically contoured distributions Suvrit Sra Max Planck Institute for Intelligent Systems T¨ubingen, Germany Reshad Hosseini School of ECE, College of Engineering University of Tehran, Tehran, Iran Abstract Hermitian positive d... | 2013 | 76 |
5,154 | Capacity of strong attractor patterns to model behavioural and cognitive prototypes Abbas Edalat Department of Computing Imperial College London London SW72RH, UK ae@ic.ac.uk Abstract We solve the mean field equations for a stochastic Hopfield network with temperature (noise) in the presence of strong, i.... | 2013 | 77 |
5,155 | Manifold-based Similarity Adaptation for Label Propagation Masayuki Karasuyama and Hiroshi Mamitsuka Bioionformatics Center, Institute for Chemical Research, Kyoto University, Japan {karasuyama,mami}@kuicr.kyoto-u.ac.jp Abstract Label propagation is one of the state-of-the-art methods for semi-supervised le... | 2013 | 78 |
5,156 | New Subsampling Algorithms for Fast Least Squares Regression Paramveer S. Dhillon1 Yichao Lu2 Dean Foster2 Lyle Ungar1 1Computer & Information Science, 2Statistics (Wharton School) University of Pennsylvania, Philadelphia, PA, U.S.A {dhillon|ungar}@cis.upenn.edu foster@wharton.upenn.edu, yichaolu@sas.... | 2013 | 79 |
5,157 | Sequential Transfer in Multi-armed Bandit with Finite Set of Models Mohammad Gheshlaghi Azar ⇤ School of Computer Science CMU Alessandro Lazaric † INRIA Lille - Nord Europe Team SequeL Emma Brunskill ⇤ School of Computer Science CMU Abstract Learning from prior tasks and transferring that experi... | 2013 | 8 |
5,158 | A message-passing algorithm for multi-agent trajectory planning Jos´e Bento ⇤ jbento@disneyresearch.com Nate Derbinsky nate.derbinsky@disneyresearch.com Javier Alonso-Mora jalonso@disneyresearch.com Jonathan Yedidia yedidia@disneyresearch.com Abstract We describe a novel approach for computing col... | 2013 | 80 |
5,159 | Solving the multi-way matching problem by permutation synchronization Deepti Pachauri,† Risi Kondor§ and Vikas Singh‡† †Dept. of Computer Sciences, University of Wisconsin–Madison ‡Dept. of Biostatistics & Medical Informatics, University of Wisconsin–Madison §Dept. of Computer Science and Dept. of Statistics,... | 2013 | 81 |
5,160 | Auditing: Active Learning with Outcome-Dependent Query Costs Sivan Sabato Microsoft Research New England sivan.sabato@microsoft.com Anand D. Sarwate TTI-Chicago asarwate@ttic.edu Nathan Srebro Technion-Israel Institute of Technology and TTI-Chicago nati@ttic.edu Abstract We propose a learning se... | 2013 | 82 |
5,161 | Restricting exchangeable nonparametric distributions Sinead A. Williamson University of Texas at Austin Steven N. MacEachern The Ohio State University Eric P. Xing Carnegie Mellon University Abstract Distributions over matrices with exchangeable rows and infinitely many columns are useful in constructi... | 2013 | 83 |
5,162 | On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization Ke Hou, Zirui Zhou, Anthony Man–Cho So Department of Systems Engineering & Engineering Management The Chinese University of Hong Kong Shatin, N. T., Hong Kong {khou,zrzhou,manchoso}@se.cuhk.edu.hk Zhi–Quan Luo Depart... | 2013 | 84 |
5,163 | Eluder Dimension and the Sample Complexity of Optimistic Exploration Daniel Russo Stanford University Stanford, CA 94305 djrusso@stanford.edu Benjamin Van Roy Stanford University Stanford, CA 94305 bvr@stanford.edu Abstract This paper considers the sample complexity of the multi-armed bandit with ... | 2013 | 85 |
5,164 | Efficient Algorithm for Privately Releasing Smooth Queries Ziteng Wang Key Laboratory of Machine Perception, MOE School of EECS Peking University wangzt@cis.pku.edu.cn Kai Fan Key Laboratory of Machine Perception, MOE School of EECS Peking University interfk@hotmail.com Jiaqi Zhang Key Laborato... | 2013 | 86 |
5,165 | Buy-in-Bulk Active Learning Liu Yang Machine Learning Department, Carnegie Mellon University liuy@cs.cmu.edu Jaime Carbonell Language Technologies Institute, Carnegie Mellon University jgc@cs.cmu.edu Abstract In many practical applications of active learning, it is more cost-effective to request lab... | 2013 | 87 |
5,166 | On Poisson Graphical Models Eunho Yang Department of Computer Science University of Texas at Austin eunho@cs.utexas.edu Pradeep Ravikumar Department of Computer Science University of Texas at Austin pradeepr@cs.utexas.edu Genevera I. Allen Department of Statistics and Electrical & Computer Enginee... | 2013 | 88 |
5,167 | On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations Tamir Hazan University of Haifa Subhransu Maji TTI Chicago Tommi Jaakkola CSAIL, MIT Abstract In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions. Specifically, we ... | 2013 | 89 |
5,168 | A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles Jinwoo Shin Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon, 305-701, Republic of Korea jinwoos@kaist.ac.kr Andrew E. Gelfand ∗ Department of Computer Science ... | 2013 | 9 |
5,169 | Factorized Asymptotic Bayesian Inference for Latent Feature Models Kohei Hayashi∗† ∗National Institute of Informatics †JST, ERATO, Kawarabayashi Large Graph Project kohei-h@nii.ac.jp Ryohei Fujimaki NEC Laboratories America rfujimaki@nec-labs.com Abstract This paper extends factorized asymptotic Bay... | 2013 | 90 |
5,170 | Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation Martin Azizyan Machine Learning Department Carnegie Mellon University mazizyan@cs.cmu.edu Aarti Singh Machine Learning Department Carnegie Mellon University aarti@cs.cmu.edu Larry Wasserman Department of Statistics ... | 2013 | 91 |
5,171 | Efficient Optimization for Sparse Gaussian Process Regression Yanshuai Cao1 Marcus A. Brubaker2 David J. Fleet1 Aaron Hertzmann1,3 1Department of Computer Science 2TTI-Chicago 3Adobe Research University of Toronto Abstract We propose an efficient optimization algorithm for selecting a subset of trai... | 2013 | 92 |
5,172 | Robust learning of low-dimensional dynamics from large neural ensembles David Pfau Eftychios A. Pnevmatikakis Liam Paninski Center for Theoretical Neuroscience Department of Statistics Grossman Center for the Statistics of Mind Columbia University, New York, NY pfau@neurotheory.columbia.edu {eftychi... | 2013 | 93 |
5,173 | Causal Inference on Time Series using Restricted Structural Equation Models Jonas Peters∗ Seminar for Statistics ETH Z¨urich, Switzerland peters@math.ethz.ch Dominik Janzing MPI for Intelligent Systems T¨ubingen, Germany janzing@tuebingen.mpg.de Bernhard Sch¨olkopf MPI for Intelligent Systems T¨... | 2013 | 94 |
5,174 | Better Approximation and Faster Algorithm Using the Proximal Average Yaoliang Yu Department of Computing Science, University of Alberta, Edmonton AB T6G 2E8, Canada yaoliang@cs.ualberta.ca Abstract It is a common practice to approximate “complicated” functions with more friendly ones. In large-scale mac... | 2013 | 95 |
5,175 | Robust Low Rank Kernel Embeddings of Multivariate Distributions Le Song, Bo Dai College of Computing, Georgia Institute of Technology lsong@cc.gatech.edu, bodai@gatech.edu Abstract Kernel embedding of distributions has led to many recent advances in machine learning. However, latent and low rank structure... | 2013 | 96 |
5,176 | Learning the Local Statistics of Optical Flow Dan Rosenbaum1, Daniel Zoran2, Yair Weiss1,2 1 CSE , 2 ELSC , Hebrew University of Jerusalem {danrsm,daniez,yweiss}@cs.huji.ac.il Abstract Motivated by recent progress in natural image statistics, we use newly available datasets with ground truth optical flow to ... | 2013 | 97 |
5,177 | Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis James Voss Ohio State University Computer Science and Engineering, 2015 Neil Avenue, Dreese Labs 586. Columbus, OH 43210 vossj@cse.ohio-state.edu Luis Rademacher Ohio State University Computer Science and Engineering, 2015... | 2013 | 98 |
5,178 | Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization Julien Mairal LEAR Project-Team - INRIA Grenoble julien.mairal@inria.fr Abstract Majorization-minimization algorithms consist of iteratively minimizing a majorizing surrogate of an objective function. Because of its simplicity and ... | 2013 | 99 |
5,179 | Two-Stream Convolutional Networks for Action Recognition in Videos Karen Simonyan Andrew Zisserman Visual Geometry Group, University of Oxford {karen,az}@robots.ox.ac.uk Abstract We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video... | 2014 | 1 |
5,180 | Compressive Sensing of Signals from a GMM with Sparse Precision Matrices 1Jianbo Yang 1Xuejun Liao 2Minhua Chen 1Lawrence Carin 1Department of Electrical and Computer Engineering, Duke University 2Department of Statistics & Department of Computer Science, University of Chicago {jianbo.yang;xjliao;lcarin... | 2014 | 10 |
5,181 | Learning Deep Features for Scene Recognition using Places Database Bolei Zhou1, Agata Lapedriza1,3, Jianxiong Xiao2, Antonio Torralba1, and Aude Oliva1 1Massachusetts Institute of Technology 2Princeton University 3Universitat Oberta de Catalunya Abstract Scene recognition is one of the hallmark tasks of c... | 2014 | 100 |
5,182 | Efficient Sampling for Learning Sparse Additive Models in High Dimensions Hemant Tyagi ETH Z¨urich htyagi@inf.ethz.ch Andreas Krause ETH Z¨urich krausea@ethz.ch Bernd G¨artner ETH Z¨urich gaertner@inf.ethz.ch Abstract We consider the problem of learning sparse additive models, i.e., functions of ... | 2014 | 101 |
5,183 | A framework for studying synaptic plasticity with neural spike train data Scott W. Linderman Harvard University Cambridge, MA 02138 swl@seas.harvard.edu Christopher H. Stock Harvard College Cambridge, MA 02138 cstock@post.harvard.edu Ryan P. Adams Harvard University Cambridge, MA 02138 rpa@sea... | 2014 | 102 |
5,184 | Real-Time Decoding of an Integrate and Fire Encoder Shreya Saxena and Munther Dahleh Department of Electrical Engineering and Computer Sciences Massachusetts Institute of Technology Cambridge, MA 02139 {ssaxena,dahleh}@mit.edu Abstract Neuronal encoding models range from the detailed biophysically-based H... | 2014 | 103 |
5,185 | Parallel Direction Method of Multipliers Huahua Wang , Arindam Banerjee , Zhi-Quan Luo University of Minnesota, Twin Cities {huwang,banerjee}@cs.umn.edu, luozq@umn.edu Abstract We consider the problem of minimizing block-separable (non-smooth) convex functions subject to linear constraints. While the Al... | 2014 | 104 |
5,186 | Spectral Methods for Supervised Topic Models Yining Wang† Jun Zhu‡ †Machine Learning Department, Carnegie Mellon University, yiningwa@cs.cmu.edu ‡Dept. of Comp. Sci. & Tech.; Tsinghua National TNList Lab; State Key Lab of Intell. Tech. & Sys., Tsinghua University, dcszj@mail.tsinghua.edu.cn Abstract Super... | 2014 | 105 |
5,187 | Exclusive Feature Learning on Arbitrary Structures via ℓ1,2-norm Deguang Kong1, Ryohei Fujimaki2, Ji Liu3, Feiping Nie1, Chris Ding1 1 Dept. of Computer Science, University of Texas Arlington, TX, 76019; 2 NEC Laboratories America, Cupertino, CA, 95014; 3 Dept. of Computer Science, University of Rochester, Ro... | 2014 | 106 |
5,188 | Provable Non-convex Robust PCA Praneeth Netrapalli 1∗ U N Niranjan2∗ Sujay Sanghavi3 Animashree Anandkumar2 Prateek Jain4 1Microsoft Research, Cambridge MA. 2The University of California at Irvine. 3The University of Texas at Austin. 4Microsoft Research, India. Abstract We propose a new method for rob... | 2014 | 107 |
5,189 | Expectation-Maximization for Learning Determinantal Point Processes Jennifer Gillenwater Computer and Information Science University of Pennsylvania jengi@cis.upenn.edu Alex Kulesza Computer Science and Engineering University of Michigan kulesza@umich.edu Emily Fox Statistics University of Washi... | 2014 | 108 |
5,190 | Estimation with Norm Regularization Arindam Banerjee Sheng Chen Farideh Fazayeli Vidyashankar Sivakumar Department of Computer Science & Engineering University of Minnesota, Twin Cities {banerjee,shengc,farideh,sivakuma}@cs.umn.edu Abstract Analysis of non-asymptotic estimation error and structured st... | 2014 | 109 |
5,191 | Recovery of Coherent Data via Low-Rank Dictionary Pursuit Guangcan Liu Department of Statistics and Biostatistics Department of Computer Science Rutgers University Piscataway, NJ 08854, USA gcliu@rutgers.edu Ping Li Department of Statistics and Biostatistics Department of Computer Science Rutgers ... | 2014 | 11 |
5,192 | Sparse Random Features Algorithm as Coordinate Descent in Hilbert Space Ian E.H. Yen 1 Ting-Wei Lin 2 Shou-De Lin 2 Pradeep Ravikumar 1 Inderjit S. Dhillon 1 Department of Computer Science 1: University of Texas at Austin, 2: National Taiwan University 1: {ianyen,pradeepr,inderjit}@cs.utexas.edu, ... | 2014 | 110 |
5,193 | Nonparametric Bayesian inference on multivariate exponential families William Vega-Brown, Marek Doniec, and Nicholas Roy Massachusetts Institute of Technology Cambridge, MA 02139 {wrvb, doniec, nickroy}@csail.mit.edu Abstract We develop a model by choosing the maximum entropy distribution from the set o... | 2014 | 111 |
5,194 | On Communication Cost of Distributed Statistical Estimation and Dimensionality Ankit Garg Department of Computer Science, Princeton University garg@cs.princeton.edu Tengyu Ma Department of Computer Science, Princeton University tengyu@cs.princeton.edu Huy L. Nguy˜ˆen Simons Institute, UC Berkeley hl... | 2014 | 112 |
5,195 | A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation Eran Treister∗† Computer Science, Technion, Israel and Earth and Ocean Sciences, UBC Vancouver, BC, V6T 1Z2, Canada eran@cs.technion.ac.il Javier Turek∗ Department of Computer Science Technion, Israel Institute of... | 2014 | 113 |
5,196 | Local Decorrelation for Improved Pedestrian Detection Woonhyun Nam∗ StradVision, Inc. woonhyun.nam@stradvision.com Piotr Doll´ar Microsoft Research pdollar@microsoft.com Joon Hee Han POSTECH, Republic of Korea joonhan@postech.ac.kr Abstract Even with the advent of more sophisticated, data-hungry m... | 2014 | 114 |
5,197 | Graph Clustering With Missing Data : Convex Algorithms and Analysis Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi Department of Electrical Engineering California Institute of Technology, Pasadena, CA 91125 {ramya, soymak}@caltech.edu, hassibi@systems.caltech.edu Abstract We consider the problem of fin... | 2014 | 115 |
5,198 | Spatio-temporal Representations of Uncertainty in Spiking Neural Networks Cristina Savin IST Austria Klosterneuburg, A-3400, Austria csavin@ist.ac.at Sophie Deneve Group for Neural Theory, ENS Paris Rue d’Ulm, 29, Paris, France sophie.deneve@ens.fr Abstract It has been long argued that, because of... | 2014 | 116 |
5,199 | Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models Michalis K. Titsias Department of Informatics Athens University of Economics and Business mtitsias@aueb.gr Christopher Yau Wellcome Trust Centre for Human Genetics University of Oxford cyau@well.ox.ac.uk Abstract We introduce a nov... | 2014 | 117 |
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