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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5,300 | Spectral Methods for Indian Buffet Process Inference Hsiao-Yu Fish Tung Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 Alexander J. Smola Machine Learning Department Carnegie Mellon University and Google Pittsburgh, PA 15213 Abstract The Indian Buffet Process is a versat... | 2014 | 208 |
5,301 | Deep Fragment Embeddings for Bidirectional Image Sentence Mapping Andrej Karpathy Armand Joulin Li Fei-Fei Department of Computer Science, Stanford University, Stanford, CA 94305, USA {karpathy,ajoulin,feifeili}@cs.stanford.edu Abstract We introduce a model for bidirectional retrieval of images and sent... | 2014 | 209 |
5,302 | Diverse Sequential Subset Selection for Supervised Video Summarization Boqing Gong∗ Department of Computer Science University of Southern California Los Angeles, CA 90089 boqinggo@usc.edu Wei-Lun Chao∗ Department of Computer Science University of Southern California Los Angeles, CA 90089 weilunc@u... | 2014 | 21 |
5,303 | Greedy Subspace Clustering Dohyung Park Dept. of Electrical and Computer Engineering The University of Texas at Austin dhpark@utexas.edu Constantine Caramanis Dept. of Electrical and Computer Engineering The University of Texas at Austin constantine@utexas.edu Sujay Sanghavi Dept. of Electrical and ... | 2014 | 210 |
5,304 | Feature Cross-Substitution in Adversarial Classification Bo Li and Yevgeniy Vorobeychik Electrical Engineering and Computer Science Vanderbilt University {bo.li.2,yevgeniy.vorobeychik}@vanderbilt.edu Abstract The success of machine learning, particularly in supervised settings, has led to numerous attemp... | 2014 | 211 |
5,305 | Distributed Balanced Clustering via Mapping Coresets MohammadHossein Bateni Google NYC bateni@google.com Aditya Bhaskara Google NYC bhaskaraaditya@google.com Silvio Lattanzi Google NYC silviol@google.com Vahab Mirrokni Google NYC mirrokni@google.com Abstract Large-scale clustering of data ... | 2014 | 212 |
5,306 | Stochastic Variational Inference for Hidden Markov Models Nicholas J. Foti†, Jason Xu†, Dillon Laird, and Emily B. Fox University of Washington {nfoti@stat,jasonxu@stat,dillonl2@cs,ebfox@stat}.washington.edu Abstract Variational inference algorithms have proven successful for Bayesian analysis in large da... | 2014 | 213 |
5,307 | Tight convex relaxations for sparse matrix factorization Emile Richard Electrical Engineering Stanford University Guillaume Obozinski Universit´e Paris-Est Ecole des Ponts - ParisTech Jean-Philippe Vert MINES ParisTech Institut Curie Abstract Based on a new atomic norm, we propose a new convex f... | 2014 | 214 |
5,308 | Extremal Mechanisms for Local Differential Privacy Peter Kairouz1 Sewoong Oh2 Pramod Viswanath1 1Department of Electrical & Computer Engineering 2Department of Industrial & Enterprise Systems Engineering University of Illinois Urbana-Champaign Urbana, IL 61801, USA {kairouz2,swoh,pramodv}@illinois.edu ... | 2014 | 215 |
5,309 | Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection Sang-Yun Oh Computational Research Division Lawrence Berkeley National Lab syoh@lbl.gov Onkar Dalal Stanford University onkar@alumni.stanford.edu Kshitij Khare Department of Statistics University of Florida kdkhare@stat.... | 2014 | 216 |
5,310 | Unsupervised Deep Haar Scattering on Graphs Xu Chen1,2, Xiuyuan Cheng2, and St´ephane Mallat2 1Department of Electrical Engineering, Princeton University, NJ, USA 2D´epartement d’Informatique, ´Ecole Normale Sup´erieure, Paris, France Abstract The classification of high-dimensional data defined on graphs is par... | 2014 | 217 |
5,311 | Communication-Efficient Distributed Dual Coordinate Ascent Martin Jaggi ∗ ETH Zurich Virginia Smith ∗ UC Berkeley Martin Tak´aˇc Lehigh University Jonathan Terhorst UC Berkeley Sanjay Krishnan UC Berkeley Thomas Hofmann ETH Zurich Michael I. Jordan UC Berkeley Abstract Communication rem... | 2014 | 218 |
5,312 | Learning convolution filters for inverse covariance estimation of neural network connectivity George O. Mohler∗ Department of Mathematics and Computer Science Santa Clara University University Santa Clara, CA, USA gmohler@scu.edu Abstract We consider the problem of inferring direct neural network connect... | 2014 | 219 |
5,313 | Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning Francesco Orabona∗ Yahoo! Labs New York, USA francesco@orabona.com Abstract Stochastic gradient descent algorithms for training linear and kernel predictors are gaining more and more importance, thanks to their scal... | 2014 | 22 |
5,314 | General Stochastic Networks for Classification Matthias Z¨ohrer and Franz Pernkopf Signal Processing and Speech Communication Laboratory Graz University of Technology matthias.zoehrer@tugraz.at, pernkopf@tugraz.at Abstract We extend generative stochastic networks to supervised learning of representations. ... | 2014 | 220 |
5,315 | Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations Xianjie Chen University of California, Los Angeles Los Angeles, CA 90024 cxj@ucla.edu Alan Yuille University of California, Los Angeles Los Angeles, CA 90024 yuille@stat.ucla.edu Abstract We present a method f... | 2014 | 221 |
5,316 | Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning Xiaoxiao Guo Computer Science and Eng. University of Michigan guoxiao@umich.edu Satinder Singh Computer Science and Eng. University of Michigan baveja@umich.edu Honglak Lee Computer Science and Eng. Univers... | 2014 | 222 |
5,317 | Near-optimal sample compression for nearest neighbors Lee-Ad Gottlieb Department of Computer Science and Mathematics, Ariel University Ariel, Israel. leead@ariel.ac.il Aryeh Kontorovich Computer Science Department, Ben Gurion University Beer Sheva, Israel. karyeh@cs.bgu.ac.il Pinhas Nisnevitch Departm... | 2014 | 223 |
5,318 | Factoring Variations in Natural Images with Deep Gaussian Mixture Models A¨aron van den Oord, Benjamin Schrauwen Electronics and Information Systems department (ELIS), Ghent University {aaron.vandenoord, benjamin.schrauwen}@ugent.be Abstract Generative models can be seen as the swiss army knives of machine ... | 2014 | 224 |
5,319 | Automated Variational Inference for Gaussian Process Models Trung V. Nguyen ANU & NICTA VanTrung.Nguyen@nicta.com.au Edwin V. Bonilla The University of New South Wales e.bonilla@unsw.edu.au Abstract We develop an automated variational method for approximate inference in Gaussian process (GP) models wh... | 2014 | 225 |
5,320 | Extreme bandits Alexandra Carpentier Statistical Laboratory, CMS University of Cambridge, UK a.carpentier@statslab.cam.ac.uk Michal Valko SequeL team INRIA Lille - Nord Europe, France michal.valko@inria.fr Abstract In many areas of medicine, security, and life sciences, we want to allocate limited r... | 2014 | 226 |
5,321 | Learning Mixed Multinomial Logit Model from Ordinal Data Sewoong Oh Dept. of Industrial and Enterprise Systems Engr. University of Illinois at Urbana-Champaign Urbana, IL 61801 swoh@illinois.edu Devavrat Shah Department of Electrical Engineering Massachussetts Institute of Technology Cambridge, MA 0... | 2014 | 227 |
5,322 | Elementary Estimators for Graphical Models Eunho Yang IBM T.J. Watson Research Center eunhyang@us.ibm.com Aur´elie C. Lozano IBM T.J. Watson Research Center aclozano@us.ibm.com Pradeep Ravikumar University of Texas at Austin pradeepr@cs.utexas.edu Abstract We propose a class of closed-form estimat... | 2014 | 228 |
5,323 | Efficient Minimax Strategies for Square Loss Games Wouter M. Koolen Queensland University of Technology and UC Berkeley wouter.koolen@qut.edu.au Alan Malek University of California, Berkeley malek@eecs.berkeley.edu Peter L. Bartlett University of California, Berkeley and Queensland University of Technolo... | 2014 | 229 |
5,324 | On the Number of Linear Regions of Deep Neural Networks Guido Mont´ufar Max Planck Institute for Mathematics in the Sciences montufar@mis.mpg.de Razvan Pascanu Universit´e de Montr´eal pascanur@iro.umontreal.ca Kyunghyun Cho Universit´e de Montr´eal kyunghyun.cho@umontreal.ca Yoshua Bengio Unive... | 2014 | 23 |
5,325 | Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion Yuanyuan Liu†, Fanhua Shang‡∗, Wei Fan§, James Cheng‡, Hong Cheng† †Dept. of Systems Engineering and Engineering Management, The Chinese University of Hong Kong ‡Dept. of Computer Science and Engineering, The Chinese Univers... | 2014 | 230 |
5,326 | Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets Adarsh Prasad UT Austin adarsh@cs.utexas.edu Stefanie Jegelka UC Berkeley stefje@eecs.berkeley.edu Dhruv Batra Virginia Tech dbatra@vt.edu Abstract To cope with the high level of ambiguity faced in d... | 2014 | 231 |
5,327 | Robust Bayesian Max-Margin Clustering Changyou Chen† Jun Zhu‡ Xinhua Zhang♯ †Dept. of Electrical and Computer Engineering, Duke University, Durham, NC, USA ‡State Key Lab of Intelligent Technology & Systems; Tsinghua National TNList Lab; ‡Dept. of Computer Science & Tech., Tsinghua University, Beijing 10008... | 2014 | 232 |
5,328 | Approximating Hierarchical MV-sets for Hierarchical Clustering Assaf Glazer Omer Weissbrod Michael Lindenbaum Shaul Markovitch Department of Computer Science, Technion - Israel Institute of Technology {assafgr,omerw,mic,shaulm}@cs.technion.ac.il Abstract The goal of hierarchical clustering is to const... | 2014 | 233 |
5,329 | Diverse Randomized Agents Vote to Win Albert Xin Jiang Trinity University xjiang@trinity.edu Leandro Soriano Marcolino USC sorianom@usc.edu Ariel D. Procaccia CMU arielpro@cs.cmu.edu Tuomas Sandholm CMU sandholm@cs.cmu.edu Nisarg Shah CMU nkshah@cs.cmu.edu Milind Tambe USC tambe@usc.... | 2014 | 234 |
5,330 | Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model Debarghya Ghoshdastidar Ambedkar Dukkipati Department of Computer Science & Automation Indian Institute of Science Bangalore – 560012, India {debarghya.g,ad}@csa.iisc.ernet.in Abstract Spectral graph partitioning... | 2014 | 235 |
5,331 | An Accelerated Proximal Coordinate Gradient Method Qihang Lin University of Iowa Iowa City, IA, USA qihang-lin@uiowa.edu Zhaosong Lu Simon Fraser University Burnaby, BC, Canada zhaosong@sfu.ca Lin Xiao Microsoft Research Redmond, WA, USA lin.xiao@microsoft.com Abstract We develop an accelera... | 2014 | 236 |
5,332 | Scalable Nonlinear Learning with Adaptive Polynomial Expansions Alekh Agarwal Microsoft Research alekha@microsoft.com Alina Beygelzimer Yahoo! Labs beygel@yahoo-inc.com Daniel Hsu Columbia University djhsu@cs.columbia.edu John Langford Microsoft Research jcl@microsoft.com Matus Telgarsky∗ ... | 2014 | 237 |
5,333 | Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition Hanie Sedghi Univ. of Southern California Los Angeles, CA 90089 hsedghi@usc.edu Anima Anandkumar University of California Irvine, CA 92697 a.anandkumar@uci.edu Edmond Jonckheere Univ. of S... | 2014 | 238 |
5,334 | Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards Omar Besbes Columbia University New York, NY ob2105@columbia.edu Yonatan Gur Stanford University Stanford, CA ygur@stanford.edu Assaf Zeevi Columbia University New York, NY assaf@gsb.columbia.edu Abstract In a multi-armed ba... | 2014 | 239 |
5,335 | Model-based Reinforcement Learning and the Eluder Dimension Ian Osband Stanford University iosband@stanford.edu Benjamin Van Roy Stanford University bvr@stanford.edu Abstract We consider the problem of learning to optimize an unknown Markov decision process (MDP). We show that, if the MDP can be param... | 2014 | 24 |
5,336 | Efficient Structured Matrix Rank Minimization Adams Wei Yu†, Wanli Ma†, Yaoliang Yu†, Jaime G. Carbonell†, Suvrit Sra‡ School of Computer Science, Carnegie Mellon University† Max Planck Institute for Intelligent Systems‡ {weiyu, mawanli, yaoliang, jgc}@cs.cmu.edu, suvrit@tuebingen.mpg.de Abstract We study th... | 2014 | 240 |
5,337 | Beyond Disagreement-based Agnostic Active Learning Chicheng Zhang University of California, San Diego 9500 Gilman Drive, La Jolla, CA 92093 chichengzhang@ucsd.edu Kamalika Chaudhuri University of California, San Diego 9500 Gilman Drive, La Jolla, CA 92093 kamalika@cs.ucsd.edu Abstract We study agn... | 2014 | 241 |
5,338 | Optimal Neural Codes for Control and Estimation Alex Susemihl1, Manfred Opper Methods of Artificial Intelligence Technische Universit¨at Berlin 1 Current affiliation: Google Ron Meir Department of Electrical Engineering Technion - Haifa Abstract Agents acting in the natural world aim at selecting approp... | 2014 | 242 |
5,339 | New Rules for Domain Independent Lifted MAP Inference Happy Mittal, Prasoon Goyal Dept. of Comp. Sci. & Engg. I.I.T. Delhi, Hauz Khas New Delhi, 110016, India happy.mittal@cse.iitd.ac.in prasoongoyal13@gmail.com Vibhav Gogate Dept. of Comp. Sci. Univ. of Texas Dallas Richardson, TX 75080, USA vg... | 2014 | 243 |
5,340 | Sparse Multi-Task Reinforcement Learning Daniele Calandriello ∗ Alessandro Lazaric∗ Team SequeL INRIA Lille – Nord Europe, France Marcello Restelli† DEIB Politecnico di Milano, Italy Abstract In multi-task reinforcement learning (MTRL), the objective is to simultaneously learn multiple tasks and exp... | 2014 | 244 |
5,341 | The limits of squared Euclidean distance regularization∗ Michał Derezi´nski Computer Science Department University of California, Santa Cruz CA 95064, U.S.A. mderezin@soe.ucsc.edu Manfred K. Warmuth Computer Science Department University of California, Santa Cruz CA 95064, U.S.A. manfred@cse.ucsc.... | 2014 | 245 |
5,342 | Finding a sparse vector in a subspace: Linear sparsity using alternating directions Qing Qu, Ju Sun, and John Wright {qq2105, js4038, jw2966}@columbia.edu Dept. of Electrical Engineering, Columbia University, New York City, NY, USA, 10027 Abstract We consider the problem of recovering the sparsest vector in... | 2014 | 246 |
5,343 | Scalable Kernel Methods via Doubly Stochastic Gradients Bo Dai1, Bo Xie1, Niao He1, Yingyu Liang2, Anant Raj1, Maria-Florina Balcan3, Le Song1 1Georgia Institute of Technology {bodai, bxie33, nhe6, araj34}@gatech.edu, lsong@cc.gatech.edu 2Princeton University 3Carnegie Mellon University yingyul@cs.princeton... | 2014 | 247 |
5,344 | Learning Mixtures of Ranking Models∗ Pranjal Awasthi Princeton University pawashti@cs.princeton.edu Avrim Blum Carnegie Mellon University avrim@cs.cmu.edu Or Sheffet Harvard University osheffet@seas.harvard.edu Aravindan Vijayaraghavan New York University vijayara@cims.nyu.edu Abstract This ... | 2014 | 248 |
5,345 | Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings Sebastian Stober, Daniel J. Cameron and Jessica A. Grahn Brain and Mind Institute, Department of Psychology, Western University London, Ontario, Canada, N6A 5B7 {sstober,dcamer25,jgrahn}@uwo.ca Abstract ... | 2014 | 249 |
5,346 | A Wild Bootstrap for Degenerate Kernel Tests Kacper Chwialkowski Department of Computer Science University College London London, Gower Street, WC1E 6BT kacper.chwialkowski@gmail.com Dino Sejdinovic Gatsby Computational Neuroscience Unit, UCL 17 Queen Square, London WC1N 3AR dino.sejdinovic@gmail.com ... | 2014 | 25 |
5,347 | Content-based recommendations with Poisson factorization Prem Gopalan Department of Computer Science Princeton University Princeton, NJ 08540 pgopalan@cs.princeton.edu Laurent Charlin Department of Computer Science Columbia University New York, NY 10027 lcharlin@cs.columbia.edu David M. Blei D... | 2014 | 250 |
5,348 | Optimizing Energy Production Using Policy Search and Predictive State Representations Yuri Grinberg Doina Precup School of Computer Science, McGill University Montreal, QC, Canada {ygrinb,dprecup}@cs.mcgill.ca Michel Gendreau∗ ´Ecole Polytechnique de Montr´eal Montreal, QC, Canada michel.gendreau@ci... | 2014 | 251 |
5,349 | Time–Data Tradeoffs by Aggressive Smoothing John J. Bruer1,* Joel A. Tropp1 Volkan Cevher2 Stephen R. Becker3 1Dept. of Computing + Mathematical Sciences, California Institute of Technology 2Laboratory for Information and Inference Systems, EPFL 3Dept. of Applied Mathematics, University of Colorado at Boul... | 2014 | 252 |
5,350 | Shaping Social Activity by Incentivizing Users Mehrdad Farajtabar∗ Nan Du∗ Manuel Gomez-Rodriguez† Isabel Valera‡ Hongyuan Zha∗ Le Song∗ Georgia Institute of Technology∗ MPI for Software Systems† Univ. Carlos III in Madrid‡ {mehrdad,dunan}@gatech.edu manuelgr@mpi-sws.org {zha,lsong}@cc.gatech.ed... | 2014 | 253 |
5,351 | Universal Option Models Hengshuai Yao, Csaba Szepesv´ari, Rich Sutton, Joseph Modayil Department of Computing Science University of Alberta Edmonton, AB, Canada, T6H 4M5 hengshua,szepesva,sutton,jmodayil@cs.ualberta.ca Shalabh Bhatnagar Department of Computer Science and Automation Indian Institute of S... | 2014 | 254 |
5,352 | Discovering, Learning and Exploiting Relevance Cem Tekin Electrical Engineering Department University of California Los Angeles cmtkn@ucla.edu Mihaela van der Schaar Electrical Engineering Department University of California Los Angeles mihaela@ee.ucla.edu Abstract In this paper we consider the prob... | 2014 | 255 |
5,353 | Stochastic Network Design in Bidirected Trees Xiaojian Wu1 Daniel Sheldon1,2 Shlomo Zilberstein1 1 School of Computer Science, University of Massachusetts Amherst 2 Department of Computer Science, Mount Holyoke College Abstract We investigate the problem of stochastic network design in bidirected trees. I... | 2014 | 256 |
5,354 | Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models Yarin Gal∗ Mark van der Wilk∗ University of Cambridge {yg279,mv310,cer54}@cam.ac.uk Carl E. Rasmussen Abstract Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They ... | 2014 | 257 |
5,355 | On the Convergence Rate of Decomposable Submodular Function Minimization Robert Nishihara, Stefanie Jegelka, Michael I. Jordan Electrical Engineering and Computer Science University of California Berkeley, CA 94720 {rkn,stefje,jordan}@eecs.berkeley.edu Abstract Submodular functions describe a variety of... | 2014 | 258 |
5,356 | Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials Shenlong Wang University of Toronto slwang@cs.toronto.edu Alexander G. Schwing University of Toronto aschwing@cs.toronto.edu Raquel Urtasun University of Toronto urtasun@cs.toronto.edu Abstract In this paper, we pro... | 2014 | 259 |
5,357 | Extracting Latent Structure From Multiple Interacting Neural Populations Jo˜ao D. Semedo1,2,3, Amin Zandvakili4, Adam Kohn4, ∗Christian K. Machens3, ∗Byron M. Yu1,5 1Department of Electrical and Computer Engineering, Carnegie Mellon University 2Department of Electrical and Computer Engineering, Instituto Supe... | 2014 | 26 |
5,358 | Metric Learning for Temporal Sequence Alignment Damien Garreau ∗† ENS damien.garreau@ens.fr R´emi Lajugie ∗† INRIA remi.lajugie@inria.fr Sylvain Arlot † CNRS sylvain.arlot@ens.fr Francis Bach † INRIA francis.bach@inria.fr Abstract In this paper, we propose to learn a Mahalanobis distance to ... | 2014 | 260 |
5,359 | Extended and Unscented Gaussian Processes Daniel M. Steinberg NICTA daniel.steinberg@nicta.com.au Edwin V. Bonilla The University of New South Wales e.bonilla@unsw.edu.au Abstract We present two new methods for inference in Gaussian process (GP) models with general nonlinear likelihoods. Inference is ... | 2014 | 261 |
5,360 | A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment Sahar Akram1,2, Jonathan Z. Simon1,2,3, Shihab Shamma1,2, and Behtash Babadi1,2 1 Department of Electrical and Computer Engineering, 2 Institute for Systems Research, 3 Department of Biology Universi... | 2014 | 262 |
5,361 | Sensory Integration and Density Estimation Joseph G. Makin and Philip N. Sabes Center for Integrative Neuroscience/Department of Physiology University of California, San Francisco San Francisco, CA 94143-0444 USA makin, sabes @phy.ucsf.edu Abstract The integration of partially redundant information from m... | 2014 | 263 |
5,362 | Causal Strategic Inference in Networked Microfinance Economies Mohammad T. Irfan Department of Computer Science Bowdoin College Brunswick, ME 04011 mirfan@bowdoin.edu Luis E. Ortiz Department of Computer Science Stony Brook University Stony Brook, NY 11794 leortiz@cs.stonybrook.edu Abstract Per... | 2014 | 264 |
5,363 | Exact Post Model Selection Inference for Marginal Screening Jason D. Lee Computational and Mathematical Engineering Stanford University Stanford, CA 94305 jdl17@stanford.edu Jonathan E. Taylor Department of Statistics Stanford University Stanford, CA 94305 jonathan.taylor@stanford.edu Abstract ... | 2014 | 265 |
5,364 | Sequence to Sequence Learning with Neural Networks Ilya Sutskever Google ilyasu@google.com Oriol Vinyals Google vinyals@google.com Quoc V. Le Google qvl@google.com Abstract Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Althou... | 2014 | 266 |
5,365 | Information-based learning by agents in unbounded state spaces Shariq A. Mobin, James A. Arnemann, Friedrich T. Sommer Redwood Center for Theoretical Neuroscience University of California, Berkeley Berkeley, CA 94720 shariqmobin@berkeley.edu, arnemann@berkeley.edu, fsommer@berkeley.edu Abstract The idea... | 2014 | 267 |
5,366 | Multi-Resolution Cascades for Multiclass Object Detection Mohammad Saberian Yahoo! Labs saberian@yahoo-inc.com Nuno Vasconcelos Statistical Visual Computing Laboratory University of California, San Diego nuno@ucsd.edu Abstract An algorithm for learning fast multiclass object detection cascades is in... | 2014 | 268 |
5,367 | Generalized Dantzig Selector: Application to the k-support norm Soumyadeep Chatterjee∗ Sheng Chen∗ Arindam Banerjee Dept. of Computer Science & Engg. University of Minnesota, Twin Cities {chatter,shengc,banerjee}@cs.umn.edu Abstract We propose a Generalized Dantzig Selector (GDS) for linear models, in... | 2014 | 269 |
5,368 | Variational Gaussian Process State-Space Models Roger Frigola, Yutian Chen and Carl E. Rasmussen Department of Engineering University of Cambridge {rf342,yc373,cer54}@cam.ac.uk Abstract State-space models have been successfully used for more than fifty years in different areas of science and engineering. We ... | 2014 | 27 |
5,369 | Conditional Swap Regret and Conditional Correlated Equilibrium Mehryar Mohri Courant Institute and Google 251 Mercer Street New York, NY 10012 mohri@cims nyu edu Scott Yang Courant Institute 251 Mercer Street New York, NY 10012 yangs@cims nyu edu Abstract We introduce a natural extension of th... | 2014 | 270 |
5,370 | Gaussian Process Volatility Model Yue Wu Cambridge University wu5@post.harvard.edu Jos´e Miguel Hern´andez Lobato Cambridge University jmh233@cam.ac.uk Zoubin Ghahramani Cambridge University zoubin@eng.cam.ac.uk Abstract The prediction of time-changing variances is an important task in the modelin... | 2014 | 271 |
5,371 | Fast Sampling-Based Inference in Balanced Neuronal Networks Guillaume Hennequin1 gjeh2@cam.ac.uk Laurence Aitchison2 laurence@gatsby.ucl.ac.uk M´at´e Lengyel1 m.lengyel@eng.cam.ac.uk 1Computational & Biological Learning Lab, Dept. of Engineering, University of Cambridge, UK 2Gatsby Computational Neuro... | 2014 | 272 |
5,372 | Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models Yichuan Zhang School of Informatics University of Edinburgh Y.Zhang-60@sms.ed.ac.uk Charles Sutton School of Informatics University of Edinburgh c.sutton@inf.ed.ac.uk Abstract Sampling from hierarchical Bayesian m... | 2014 | 273 |
5,373 | Predicting Useful Neighborhoods for Lazy Local Learning Aron Yu University of Texas at Austin aron.yu@utexas.edu Kristen Grauman University of Texas at Austin grauman@cs.utexas.edu Abstract Lazy local learning methods train a classifier “on the fly” at test time, using only a subset of the training in... | 2014 | 274 |
5,374 | (Almost) No Label No Cry Giorgio Patrini1,2, Richard Nock1,2, Paul Rivera1,2, Tiberio Caetano1,3,4 Australian National University1, NICTA2, University of New South Wales3, Ambiata4 Sydney, NSW, Australia {name.surname}@anu.edu.au Abstract In Learning with Label Proportions (LLP), the objective is to learn a... | 2014 | 275 |
5,375 | Learning Mixtures of Submodular Functions for Image Collection Summarization Sebastian Tschiatschek Department of Electrical Engineering Graz University of Technology tschiatschek@tugraz.at Rishabh Iyer Department of Electrical Engineering University of Washington rkiyer@u.washington.edu Haochen Wei... | 2014 | 276 |
5,376 | Distributed Bayesian Posterior Sampling via Moment Sharing Minjie Xu1∗, Balaji Lakshminarayanan2, Yee Whye Teh3, Jun Zhu1, and Bo Zhang1 1State Key Lab of Intelligent Technology and Systems; Tsinghua National TNList Lab 1Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China ... | 2014 | 277 |
5,377 | Proximal Quasi-Newton for Computationally Intensive ℓ1-regularized M-estimators Kai Zhong 1 Ian E.H. Yen 2 Inderjit S. Dhillon 2 Pradeep Ravikumar 2 1 Institute for Computational Engineering & Sciences 2 Department of Computer Science University of Texas at Austin zhongkai@ices.utexas.edu, {ianyen,ind... | 2014 | 278 |
5,378 | Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning Mohammad Taha Bahadori∗ Dept. of Electrical Engineering Univ. of Southern California Los Angeles, CA 90089 mohammab@usc.edu Qi (Rose) Yu∗ Dept. of Computer Science Univ. of Southern California Los Angeles, CA 90089 qiyu@usc.ed... | 2014 | 279 |
5,379 | Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations Zhenyao Zhu1,3 Ping Luo3,1 Xiaogang Wang2,3 Xiaoou Tang1,3 1Department of Information Engineering, The Chinese University of Hong Kong 2Department of Electronic Engineering, The Chinese University of Hong Kong 3Shenz... | 2014 | 28 |
5,380 | Multi-scale Graphical Models for Spatio-Temporal Processes Firdaus Janoos∗ Huseyin Denli Niranjan Subrahmanya ExxonMobil Corporate Strategic Research Annandale, NJ 08801 Abstract Learning the dependency structure between spatially distributed observations of a spatio-temporal process is an important p... | 2014 | 280 |
5,381 | Bregman Alternating Direction Method of Multipliers Huahua Wang, Arindam Banerjee Dept of Computer Science & Engg, University of Minnesota, Twin Cities {huwang,banerjee}@cs.umn.edu Abstract The mirror descent algorithm (MDA) generalizes gradient descent by using a Bregman divergence to replace squared Euc... | 2014 | 281 |
5,382 | Bounded Regret for Finite-Armed Structured Bandits Tor Lattimore Department of Computing Science University of Alberta, Canada tlattimo@ualberta.ca R´emi Munos INRIA Lille, France1 remi.munos@inria.fr Abstract We study a new type of K-armed bandit problem where the expected return of one arm may d... | 2014 | 282 |
5,383 | Exponential Concentration of a Density Functional Estimator Shashank Singh Statistics & Machine Learning Departments Carnegie Mellon University Pittsburgh, PA 15213 sss1@andrew.cmu.edu Barnab´as P´oczos Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 bapoczos@cs.cmu.edu... | 2014 | 283 |
5,384 | Decomposing Parameter Estimation Problems Khaled S. Refaat, Arthur Choi, Adnan Darwiche Computer Science Department University of California, Los Angeles {krefaat,aychoi,darwiche}@cs.ucla.edu Abstract We propose a technique for decomposing the parameter learning problem in Bayesian networks into independe... | 2014 | 284 |
5,385 | Convex Optimization Procedure for Clustering: Theoretical Revisit Changbo Zhu Department of Electrical and Computer Engineering Department of Mathematics National University of Singapore elezhuc@nus.edu.sg Huan Xu Department of Mechanical Engineering National University of Singapore mpexuh@nus.edu.s... | 2014 | 285 |
5,386 | Submodular Attribute Selection for Action Recognition in Video Jinging Zheng UMIACS, University of Maryland College Park, MD, USA zjngjng@umiacs.umd.edu Zhuolin Jiang Noah’s Ark Lab Huawei Technologies zhuolin.jiang@huawei.com Rama Chellappa UMIACS, University of Maryland College Park, MD, USA ... | 2014 | 286 |
5,387 | Quantized Estimation of Gaussian Sequence Models in Euclidean Balls Yuancheng Zhu John Lafferty Department of Statistics University of Chicago Abstract A central result in statistical theory is Pinsker’s theorem, which characterizes the minimax rate in the normal means model of nonparametric estimation.... | 2014 | 287 |
5,388 | Large-Margin Convex Polytope Machine Alex Kantchelian Michael Carl Tschantz Ling Huang† Peter L. Bartlett Anthony D. Joseph J. D. Tygar UC Berkeley – {akant|mct|bartlett|adj|tygar}@cs.berkeley.edu †Datavisor – ling.huang@datavisor.com Abstract We present the Convex Polytope Machine (CPM), a novel no... | 2014 | 288 |
5,389 | A provable SVD-based algorithm for learning topics in dominant admixture corpus Trapit Bansal†, C. Bhattacharyya‡∗ Department of Computer Science and Automation Indian Institute of Science Bangalore -560012, India †trapitbansal@gmail.com ‡chiru@csa.iisc.ernet.in Ravindran Kannan Microsoft Research I... | 2014 | 289 |
5,390 | Global Belief Recursive Neural Networks Romain Paulus, Richard Socher∗ MetaMind Palo Alto, CA {romain,richard}@metamind.io Christopher D. Manning Stanford University 353 Serra Mall Stanford, CA 94305 manning@stanford.edu Abstract Recursive Neural Networks have recently obtained state of the art pe... | 2014 | 29 |
5,391 | Divide-and-Conquer Learning by Anchoring a Conical Hull Tianyi Zhou†, Jeff Bilmes‡, Carlos Guestrin† †Computer Science & Engineering, ‡Electrical Engineering, University of Washington, Seattle {tianyizh, bilmes, guestrin}@u.washington.edu Abstract We reduce a broad class of fundamental machine learning prob... | 2014 | 290 |
5,392 | Discriminative Metric Learning by Neighborhood Gerrymandering Shubhendu Trivedi, David McAllester, Gregory Shakhnarovich Toyota Technological Institute Chicago, IL - 60637 {shubhendu,mcallester,greg}@ttic.edu Abstract We formulate the problem of metric learning for k nearest neighbor classification a... | 2014 | 291 |
5,393 | Learning on graphs using Orthonormal Representation is Statistically Consistent Rakesh S Department of Electrical Engineering Indian Institute of Science Bangalore, 560012, INDIA rakeshsmysore@gmail.com Chiranjib Bhattacharyya Department of CSA Indian Institute of Science Bangalore, 560012, INDIA ... | 2014 | 292 |
5,394 | Generalized Unsupervised Manifold Alignment Zhen Cui1,2 Hong Chang1 Shiguang Shan1 Xilin Chen1 1 Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China 2 School of Computer Science and Technology, Huaqiao University, Xiame... | 2014 | 293 |
5,395 | Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction Katerina Fragkiadaki EECS, University of California, Berkeley, CA 94720 katef@berkeley.edu Marta Salas Universidad de Zaragoza, Zaragoza, Spain msalasg@unizar.es Pablo Arbel´aez Universidad de los Andes, Bogot´a, Colombia pa.a... | 2014 | 294 |
5,396 | A statistical model for tensor PCA Andrea Montanari Statistics & Electrical Engineering Stanford University Emile Richard Electrical Engineering Stanford University Abstract We consider the Principal Component Analysis problem for large tensors of arbitrary order k under a single-spike (or rank-one plus... | 2014 | 295 |
5,397 | Making Pairwise Binary Graphical Models Attractive Nicholas Ruozzi Institute for Data Sciences and Engineering Columbia University New York, NY 10027 nr2493@columbia.edu Tony Jebara Department of Computer Science Columbia University New York, NY 10027 jebara@cs.columbia.edu Abstract Computing th... | 2014 | 296 |
5,398 | Subspace Embeddings for the Polynomial Kernel Haim Avron IBM T.J. Watson Research Center Yorktown Heights, NY 10598 haimav@us.ibm.com Huy L. Nguy˜ˆen Simons Institute, UC Berkeley Berkeley, CA 94720 hlnguyen@cs.princeton.edu David P. Woodruff IBM Almaden Research Center San Jose, CA 95120 dpwood... | 2014 | 297 |
5,399 | On the Relationship Between LFP & Spiking Data David E. Carlson1, Jana Schaich Borg2, Kafui Dzirasa2, and Lawrence Carin1 1Department of Electrical and Computer Engineering 2Department of Psychiatry and Behavioral Sciences Duke University Duham, NC 27701 {david.carlson, jana.borg, kafui.dzirasa, lcarin}@duk... | 2014 | 298 |
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