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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5,000 | What do row and column marginals reveal about your dataset? Behzad Golshan Boston University behzad@cs.bu.edu John W. Byers Boston University byers@cs.bu.edu Evimaria Terzi Boston University evimaria@cs.bu.edu Abstract Numerous datasets ranging from group memberships within social networks to ... | 2013 | 262 |
5,001 | Analyzing Hogwild Parallel Gaussian Gibbs Sampling Matthew J. Johnson EECS, MIT mattjj@mit.edu James Saunderson EECS, MIT jamess@mit.edu Alan S. Willsky EECS, MIT willsky@mit.edu Abstract Sampling inference methods are computationally difficult to scale for many models in part because global depend... | 2013 | 263 |
5,002 | Latent Structured Active Learning Wenjie Luo TTI Chicago wenjie.luo@ttic.edu Alexander G. Schwing ETH Zurich aschwing@inf.ethz.ch Raquel Urtasun TTI Chicago rurtasun@ttic.edu Abstract In this paper we present active learning algorithms in the context of structured prediction problems. To reduce ... | 2013 | 264 |
5,003 | Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models Adel Javanmard Stanford University Stanford, CA 94305 adelj@stanford.edu Andrea Montanari Stanford University Stanford, CA 94305 montanar@stanford.edu Abstract Fitting high-dimensional statistical models often requi... | 2013 | 265 |
5,004 | Stochastic blockmodel approximation of a graphon: Theory and consistent estimation Edoardo M. Airoldi Dept. Statistics Harvard University Thiago B. Costa SEAS, and Dept. Statistics Harvard University Stanley H. Chan SEAS, and Dept. Statistics Harvard University Abstract Non-parametric approaches... | 2013 | 266 |
5,005 | More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server †Qirong Ho, †James Cipar, §Henggang Cui, †Jin Kyu Kim, †Seunghak Lee, ‡Phillip B. Gibbons, †Garth A. Gibson, §Gregory R. Ganger, †Eric P. Xing †School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 qho@, j... | 2013 | 267 |
5,006 | On Algorithms for Sparse Multi-factor NMF Siwei Lyu Xin Wang Computer Science Department University at Albany, SUNY Albany, NY 12222 {slyu,xwang26}@albany.edu Abstract Nonnegative matrix factorization (NMF) is a popular data analysis method, the objective of which is to approximate a matrix with all n... | 2013 | 268 |
5,007 | Efficient Exploration and Value Function Generalization in Deterministic Systems Zheng Wen Stanford University zhengwen@stanford.edu Benjamin Van Roy Stanford University bvr@stanford.edu Abstract We consider the problem of reinforcement learning over episodes of a finitehorizon deterministic system and ... | 2013 | 269 |
5,008 | A Gang of Bandits Nicol`o Cesa-Bianchi Universit`a degli Studi di Milano, Italy nicolo.cesa-bianchi@unimi.it Claudio Gentile University of Insubria, Italy claudio.gentile@uninsubria.it Giovanni Zappella Universit`a degli Studi di Milano, Italy giovanni.zappella@unimi.it Abstract Multi-armed bandit... | 2013 | 27 |
5,009 | Parallel Sampling of DP Mixture Models using Sub-Clusters Splits Jason Chang∗ CSAIL, MIT jchang7@csail.mit.edu John W. Fisher III∗ CSAIL, MIT fisher@csail.mit.edu Abstract We present an MCMC sampler for Dirichlet process mixture models that can be parallelized to achieve significant computational gai... | 2013 | 270 |
5,010 | Context-sensitive active sensing in humans Sheeraz Ahmad Department of Computer Science and Engineering University of California San Diego 9500 Gilman Drive La Jolla, CA 92093 sahmad@cs.ucsd.edu He Huang Department of Cognitive Science University of California San Diego 9500 Gilman Drive La Jolla, CA ... | 2013 | 271 |
5,011 | On the Sample Complexity of Subspace Learning Alessandro Rudi Robotics Brain and Cognitive Science Istituto Italiano di Tecnologia alessandro.rudi@iit.it Guillermo D. Canas Massachussetss Institute of Technology guilledc@mit.edu Lorenzo Rosasco Universita’ degli Studi di Genova, LCSL, Massachusetts ... | 2013 | 272 |
5,012 | Non-Linear Domain Adaptation with Boosting Carlos Becker∗ C. Mario Christoudias Pascal Fua CVLab, ´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland firstname.lastname@epfl.ch Abstract A common assumption in machine vision is that the training and test samples are drawn from the same distribution.... | 2013 | 273 |
5,013 | Learning Trajectory Preferences for Manipulators via Iterative Improvement Ashesh Jain, Brian Wojcik, Thorsten Joachims, Ashutosh Saxena Department of Computer Science, Cornell University. {ashesh,bmw75,tj,asaxena}@cs.cornell.edu Abstract We consider the problem of learning good trajectories for manipulatio... | 2013 | 274 |
5,014 | Learning Chordal Markov Networks by Constraint Satisfaction Jukka Corander∗† University of Helsinki Finland Tomi Janhunen∗‡ Aalto University Finland Jussi Rintanen∗‡§ Aalto University Finland Henrik Nyman¶ ˚Abo Akademi University Finland Johan Pensar¶ ˚Abo Akademi University Finland Ab... | 2013 | 275 |
5,015 | Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions Rishabh Iyer†, Stefanie Jegelka∗, Jeff Bilmes† † University of Washington, Dept. of EE, Seattle, U.S.A. ∗University of California, Dept. of EECS, Berkeley, U.S.A. rkiyer@uw.edu, stefje@eecs.berkeley.edu, bilmes@uw.edu Abstract... | 2013 | 276 |
5,016 | A New Convex Relaxation for Tensor Completion Bernardino Romera-Paredes Department of Computer Science and UCL Interactive Centre University College London Malet Place, London WC1E 6BT, UK B.RomeraParedes@cs.ucl.ac.uk Massimiliano Pontil Department of Computer Science and Centre for Computational Stat... | 2013 | 277 |
5,017 | DESPOT: Online POMDP Planning with Regularization Adhiraj Somani Nan Ye David Hsu Wee Sun Lee Department of Computer Science National University of Singapore adhirajsomani@gmail.com, {yenan,dyhsu,leews}@comp.nus.edu.sg Abstract POMDPs provide a principled framework for planning under uncertainty, but ... | 2013 | 278 |
5,018 | Speeding up Permutation Testing in Neuroimaging ∗ Chris Hinrichs† Vamsi K. Ithapu† Qinyuan Sun† Sterling C. Johnson§† Vikas Singh† §William S. Middleton Memorial VA Hospital †University of Wisconsin–Madison {hinrichs,vamsi}@cs.wisc.edu {qsun28}@wisc.edu {scj}@medicine.wisc.edu {vsingh}@biostat.wisc.... | 2013 | 279 |
5,019 | Multiclass Total Variation Clustering Xavier Bresson University of Lausanne Lausanne, Switzerland xavier.bresson@unil.ch Thomas Laurent Loyola Marymount University Los Angeles, CA 90045 tlaurent@lmu.edu David Uminsky University of San Francisco San Francisco, CA 94117 duminsky@usfca.edu James ... | 2013 | 28 |
5,020 | Neural representation of action sequences: how far can a simple snippet-matching model take us? Cheston Tan Institute for Infocomm Research Singapore cheston@mit.edu Jedediah M. Singer Boston Children’s Hospital Boston, MA 02115 jedediah.singer@childrens.harvard.edu Thomas Serre David Sheinberg ... | 2013 | 280 |
5,021 | Modeling Clutter Perception using Parametric Proto-object Partitioning Chen-Ping Yu Department of Computer Science Stony Brook University cheyu@cs.stonybrook.edu Wen-Yu Hua Department of Statistics Pennsylvania State University wxh182@psu.edu Dimitris Samaras Department of Computer Science Stony... | 2013 | 281 |
5,022 | Relevance Topic Model for Unstructured Social Group Activity Recognition Fang Zhao Yongzhen Huang Liang Wang Tieniu Tan Center for Research on Intelligent Perception and Computing Institute of Automation, Chinese Academy of Sciences {fang.zhao,yzhuang,wangliang,tnt}@nlpr.ia.ac.cn Abstract Unstructur... | 2013 | 282 |
5,023 | Generalized Random Utility Models with Multiple Types Hossein Azari Soufiani Hansheng Diao Zhenyu Lai David C. Parkes SEAS Mathematics Department Economics Department SEAS Harvard University Harvard University Harvard University Harvard University azari@fas.harvard.edu diao@fas.harvard.edu ... | 2013 | 283 |
5,024 | (Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings Adam Smith∗ Pennsylvania State University asmith@cse.psu.edu Abhradeep Thakurta† Stanford University and Microsoft Research Silicon Valley Campus b-abhrag@microsoft.com Abstract We give differentially pri... | 2013 | 284 |
5,025 | The Fast Convergence of Incremental PCA Akshay Balsubramani UC San Diego abalsubr@cs.ucsd.edu Sanjoy Dasgupta UC San Diego dasgupta@cs.ucsd.edu Yoav Freund UC San Diego yfreund@cs.ucsd.edu Abstract We consider a situation in which we see samples Xn ∈Rd drawn i.i.d. from some distribution with me... | 2013 | 285 |
5,026 | Point Based Value Iteration with Optimal Belief Compression for Dec-POMDPs Liam MacDermed College of Computing Georgia Institute of Technology Atlanta, GA 30332 liam@cc.gatech.edu Charles L. Isbell College of Computing Georgia Institute of Technology Atlanta, GA 30332 isbell@cc.gatech.edu Abstra... | 2013 | 286 |
5,027 | Summary Statistics for Partitionings and Feature Allocations Is¸ık Barıs¸ Fidaner Computer Engineering Department Bo˘gazic¸i University, Istanbul fidaner@alternatifbilisim.org Ali Taylan Cemgil Computer Engineering Department Bo˘gazic¸i University, Istanbul taylan.cemgil@boun.edu.tr Abstract Infini... | 2013 | 287 |
5,028 | Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition Tzu-Kuo Huang Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 tzukuoh@cs.cmu.edu Jeff Schneider Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 schneide@cs.cmu.edu Abstrac... | 2013 | 288 |
5,029 | On Flat versus Hierarchical Classification in Large-Scale Taxonomies Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini Université Joseph Fourier, Laboratoire Informatique de Grenoble BP 53 - F-38041 Grenoble Cedex 9 firstname.lastname@imag.fr Abstract We study in this paper flat and hierarchi... | 2013 | 289 |
5,030 | Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors Xiaoqin Zhang, Di Wang Institute of Intelligent System and Decision Wenzhou University zhangxiaoqinnan@gmail.com, wangdi@wzu.edu.cn Zhengyuan Zhou Department of Electrical Engineering Stanford University zyzhou@stanford.ed... | 2013 | 29 |
5,031 | Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? Qiang Liu Dept. of Computer Science Univ. of California, Irvine qliu1@uci.edu Mark Steyvers Dept. of Cognitive Sciences Univ. of California, Irvine mark.steyvers@uci.edu Alexander Ihler Dept. of Computer Science Univ. of Ca... | 2013 | 290 |
5,032 | Cluster Trees on Manifolds Sivaraman Balakrishnan† sbalakri@cs.cmu.edu Srivatsan Narayanan† srivatsa@cs.cmu.edu Alessandro Rinaldo‡ arinaldo@stat.cmu.edu Aarti Singh† aarti@cs.cmu.edu Larry Wasserman‡ larry@stat.cmu.edu School of Computer Science† and Department of Statistics‡ Carnegie Mellon Un... | 2013 | 291 |
5,033 | Bayesian inference as iterated random functions with applications to sequential inference in graphical models Arash A. Amini Department of Statistics University of Michigan Ann Arbor, Michigan 48109 aaamini@umich.edu XuanLong Nguyen Department of Statistics University of Michigan Ann Arbor, Michig... | 2013 | 292 |
5,034 | Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems Hesham Mostafa, Lorenz K. M¨uller, and Giacomo Indiveri Institute for Neuroinformatics University of Zurich and ETH Zurich {hesham,lorenz,giacomo}@ini.uzh.ch Abstract We present a recurrent neuronal netw... | 2013 | 293 |
5,035 | Rapid Distance-Based Outlier Detection via Sampling Mahito Sugiyama1 Karsten M. Borgwardt1,2 1Machine Learning and Computational Biology Research Group, MPIs T¨ubingen, Germany 2Zentrum f¨ur Bioinformatik, Eberhard Karls Universit¨at T¨ubingen, Germany {mahito.sugiyama,karsten.borgwardt}@tuebingen.mpg.de Ab... | 2013 | 294 |
5,036 | Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies Yangqing Jia1, Joshua Abbott2, Joseph Austerweil3, Thomas Griffiths2, Trevor Darrell1 1UC Berkeley EECS 2Dept of Psychology, UC Berkeley 3Dept of Cognitive, Linguistics, and Psychological Sciences, Brown Univer... | 2013 | 295 |
5,037 | Memoized Online Variational Inference for Dirichlet Process Mixture Models Michael C. Hughes and Erik B. Sudderth Department of Computer Science, Brown University, Providence, RI 02912 mhughes@cs.brown.edu, sudderth@cs.brown.edu Abstract Variational inference algorithms provide the most effective framework ... | 2013 | 296 |
5,038 | Locally Adaptive Bayesian Multivariate Time Series Daniele Durante Department of Statistical Sciences University of Padua Via Cesare Battisti 241, 35121, Padua, Italy durante@stat.unipd.it Bruno Scarpa Department of Statistical Sciences University of Padua Via Cesare Battisti 241, 35121, Padua, Italy ... | 2013 | 297 |
5,039 | When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements Divyanshu Vats Rice University Houston, TX 77251 dvats@rice.edu Richard Baraniuk Rice University Houston, TX 77251 richb@rice.edu Abstract We consider the problem of accurately estimating a high-dimensional sparse v... | 2013 | 298 |
5,040 | Information-theoretic lower bounds for distributed statistical estimation with communication constraints Yuchen Zhang1 John C. Duchi1 Michael I. Jordan1,2 Martin J. Wainwright1,2 1Department of Electrical Engineering and Computer Science and 2Department of Statistics University of California, Berkeley B... | 2013 | 299 |
5,041 | Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections Matthew Lawlor Applied Mathematics Yale University New Haven, CT 06520 matthew.lawlor@yale.edu Steven W. Zucker Computer Science Yale University New Haven, CT 06520 zucker@cs.yale.edu Abstract Association fie... | 2013 | 3 |
5,042 | BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables Cho-Jui Hsieh, M´aty´as A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar Department of Computer Science University of Texas at Austin {cjhsieh,sustik,inderjit,pradeepr}@cs.utexas.edu Russell A. Poldrack Department of Psychology and ... | 2013 | 30 |
5,043 | Learning Stochastic Feedforward Neural Networks Yichuan Tang Department of Computer Science University of Toronto Toronto, Ontario, Canada. tang@cs.toronto.edu Ruslan Salakhutdinov Department of Computer Science and Statistics University of Toronto Toronto, Ontario, Canada. rsalakhu@cs.toronto.edu ... | 2013 | 300 |
5,044 | Robust Transfer Principal Component Analysis with Rank Constraints Yuhong Guo Department of Computer and Information Sciences Temple University, Philadelphia, PA 19122, USA yuhong@temple.edu Abstract Principal component analysis (PCA), a well-established technique for data analysis and processing, provide... | 2013 | 301 |
5,045 | A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data Jasper Snoek∗ Harvard University jsnoek@seas.harvard.edu Ryan P. Adams Harvard University rpa@seas.harvard.edu Richard S. Zemel University of Toronto zemel@cs.toronto.edu Abstract Point processes are popula... | 2013 | 302 |
5,046 | Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively Wen-hao Zhang1,2,3, Si Wu1 1State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, China. 2Institute of Neuroscience, Chinese Academ... | 2013 | 303 |
5,047 | Structured Learning via Logistic Regression Justin Domke NICTA and The Australian National University justin.domke@nicta.com.au Abstract A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between upda... | 2013 | 304 |
5,048 | Learning word embeddings efficiently with noise-contrastive estimation Andriy Mnih DeepMind Technologies andriy@deepmind.com Koray Kavukcuoglu DeepMind Technologies koray@deepmind.com Abstract Continuous-valued word embeddings learned by neural language models have recently been shown to capture semant... | 2013 | 305 |
5,049 | Generalized Method-of-Moments for Rank Aggregation Hossein Azari Soufiani SEAS Harvard University azari@fas.harvard.edu William Z. Chen Statistics Department Harvard University wchen@college.harvard.edu David C. Parkes SEAS Harvard University parkes@eecs.harvard.edu Lirong Xia Computer Scie... | 2013 | 306 |
5,050 | Reconciling “priors” & “priors” without prejudice? R´emi Gribonval ∗ Inria Centre Inria Rennes - Bretagne Atlantique remi.gribonval@inria.fr Pierre Machart Inria Centre Inria Rennes - Bretagne Atlantique pierre.machart@inria.fr Abstract There are two major routes to address linear inverse problems. ... | 2013 | 307 |
5,051 | Learning a Deep Compact Image Representation for Visual Tracking Naiyan Wang Dit-Yan Yeung Department of Computer Science and Engineering Hong Kong University of Science and Technology winsty@gmail.com dyyeung@cse.ust.hk Abstract In this paper, we study the challenging problem of tracking the trajecto... | 2013 | 308 |
5,052 | Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent Tianbao Yang NEC Labs America, Cupertino, CA 95014 tyang@nec-labs.com Abstract We present and study a distributed optimization algorithm by employing a stochastic dual coordinate ascent method. Stochastic dual coordinate as... | 2013 | 309 |
5,053 | Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching Marcelo Fiori Universidad de la Rep´ublica, Uruguay mfiori@fing.edu.uy Pablo Sprechmann Duke University Durham, NC 27708 pablo.sprechmann@duke.edu Joshua Vogelstein Duke University Durham, NC 27708 jovo@math.duke.edu Pablo ... | 2013 | 31 |
5,054 | Projected Natural Actor-Critic Philip S. Thomas, William Dabney, Sridhar Mahadevan, and Stephen Giguere School of Computer Science University of Massachusetts Amherst Amherst, MA 01003 {pthomas,wdabney,mahadeva,sgiguere}@cs.umass.edu Abstract Natural actor-critics form a popular class of policy search alg... | 2013 | 310 |
5,055 | Minimax Optimal Algorithms for Unconstrained Linear Optimization H. Brendan McMahan Google Reasearch Seattle, WA mcmahan@google.com Jacob Abernethy⇤ Computer Science and Engineering University of Michigan jabernet@umich.edu Abstract We design and analyze minimax-optimal algorithms for online linea... | 2013 | 311 |
5,056 | Policy Shaping: Integrating Human Feedback with Reinforcement Learning Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles L. Isbell, and Andrea Thomaz College of Computing Georgia Institute of Technology, Atlanta, GA 30332, USA {sgriffith7, kausubbu, jkscholz}@gatech.edu, {isbell, athomaz}@cc.gat... | 2013 | 312 |
5,057 | Regret based Robust Solutions for Uncertain Markov Decision Processes Asrar Ahmed Singapore Management University masrara@smu.edu.sg Pradeep Varakantham Singapore Management University pradeepv@smu.edu.sg Yossiri Adulyasak Massachusetts Institute of Technology yossiri@smart.mit.edu Patrick Jaillet... | 2013 | 313 |
5,058 | Understanding variable importances in forests of randomized trees Gilles Louppe, Louis Wehenkel, Antonio Sutera and Pierre Geurts Dept. of EE & CS, University of Li`ege, Belgium {g.louppe, l.wehenkel, a.sutera, p.geurts}@ulg.ac.be Abstract Despite growing interest and practical use in various scientific area... | 2013 | 314 |
5,059 | Linear Decision Rule as Aspiration for Simple Decision Heuristics ¨Ozg¨ur S¸ims¸ek Center for Adaptive Behavior and Cognition Max Planck Institute for Human Development Lentzeallee 94, 14195 Berlin, Germany ozgur@mpib-berlin.mpg.de Abstract Several attempts to understand the success of simple decision h... | 2013 | 315 |
5,060 | Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising Forest Agostinelli Michael R. Anderson Honglak Lee Division of Computer Science and Engineering University of Michigan Ann Arbor, MI 48109, USA {agostifo,mrander,honglak}@umich.edu Abstract Stacked sparse denoising... | 2013 | 316 |
5,061 | Probabilistic Movement Primitives Alexandros Paraschos, Christian Daniel, Jan Peters, and Gerhard Neumann Intelligent Autonomous Systems, Technische Universität Darmstadt Hochschulstr. 10, 64289 Darmstadt, Germany {paraschos,daniel,peters,neumann}@ias.tu-darmstadt.de Abstract Movement Primitives (MP) are a ... | 2013 | 317 |
5,062 | Speedup Matrix Completion with Side Information: Application to Multi-Label Learning Miao Xu1 Rong Jin2 Zhi-Hua Zhou1 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2Department of Computer Science and Engineering, Michigan State University, East Lansing... | 2013 | 318 |
5,063 | Message Passing Inference with Chemical Reaction Networks Nils Napp Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge, MA 02138 nnapp@wyss.harvard.edu Ryan Prescott Adams School of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 rpa@seas.harv... | 2013 | 319 |
5,064 | Optimal integration of visual speed across different spatiotemporal frequency channels Matjaˇz Jogan and Alan A. Stocker Department of Psychology University of Pennsylvania Philadelphia, PA 19104 {mjogan,astocker}@sas.upenn.edu Abstract How do humans perceive the speed of a coherent motion stimulus that... | 2013 | 32 |
5,065 | Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent Yuening Hu1, Jordan Boyd-Graber2, Hal Daum`e III3, Z. Irene Ying4 1, 3: Computer Science, 2: iSchool and UMIACS, 4: Agricultural Research Service 1, 2, 3: University of Maryland, 4: Department of Agriculture ynhu@cs.umd.edu, {jbg,hal}@... | 2013 | 320 |
5,066 | A Stability-based Validation Procedure for Differentially Private Machine Learning Kamalika Chaudhuri Department of Computer Science and Engineering UC San Diego, La Jolla CA 92093 kamalika@cs.ucsd.edu Staal Vinterbo Division of Biomedical Informatics UC San Diego, La Jolla CA 92093 sav@ucsd.edu Abs... | 2013 | 321 |
5,067 | Unsupervised Spectral Learning of FSTs Rapha¨el Bailly Xavier Carreras Ariadna Quattoni Universitat Politecnica de Catalunya Barcelona, 08034 rbailly,carreras,aquattoni@lsi.upc.edu Abstract Finite-State Transducers (FST) are a standard tool for modeling paired inputoutput sequences and are used in numer... | 2013 | 322 |
5,068 | One-shot learning and big data with n = 2 Lee H. Dicker Rutgers University Piscataway, NJ ldicker@stat.rutgers.edu Dean P. Foster University of Pennsylvania Philadelphia, PA dean@foster.net Abstract We model a “one-shot learning” situation, where very few observations y1, ..., yn ∈R are available.... | 2013 | 323 |
5,069 | Mapping cognitive ontologies to and from the brain Yannick Schwartz, Bertrand Thirion, and Gael Varoquaux Parietal Team, Inria Saclay Ile-de-France Saclay, France firstname.lastname@inria.fr Abstract Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies. Due ... | 2013 | 324 |
5,070 | Density estimation from unweighted k-nearest neighbor graphs: a roadmap Ulrike von Luxburg and Morteza Alamgir Department of Computer Science University of Hamburg, Germany {luxburg,alamgir}@informatik.uni-hamburg.de Abstract Consider an unweighted k-nearest neighbor graph on n points that have been s... | 2013 | 325 |
5,071 | Actor-Critic Algorithms for Risk-Sensitive MDPs Prashanth L.A. INRIA Lille - Team SequeL Mohammad Ghavamzadeh∗ INRIA Lille - Team SequeL & Adobe Research Abstract In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in rewards in addition to maxi... | 2013 | 326 |
5,072 | Probabilistic Principal Geodesic Analysis Miaomiao Zhang School of Computing University of Utah Salt Lake City, UT miaomiao@sci.utah.edu P. Thomas Fletcher School of Computing University of Utah Salt Lake City, UT fletcher@sci.utah.edu Abstract Principal geodesic analysis (PGA) is a generalizati... | 2013 | 327 |
5,073 | k-Prototype Learning for 3D Rigid Structures ⋆ Hu Ding Department of Computer Science and Engineering State University of New York at Buffalo Buffalo, NY14260 huding@buffalo.edu Ronald Berezney Department of Biological Sciences State University of New York at Buffalo Buffalo, NY14260 berezney@buffal... | 2013 | 328 |
5,074 | Learning Adaptive Value of Information for Structured Prediction David Weiss University of Pennsylvania Philadelphia, PA djweiss@cis.upenn.edu Ben Taskar University of Washington Seattle, WA taskar@cs.washington.edu Abstract Discriminative methods for learning structured models have enabled wide-s... | 2013 | 329 |
5,075 | Translating Embeddings for Modeling Multi-relational Data Antoine Bordes, Nicolas Usunier, Alberto Garcia-Dur´an Universit´e de Technologie de Compi`egne – CNRS Heudiasyc UMR 7253 Compi`egne, France {bordesan, nusunier, agarciad}@utc.fr Jason Weston, Oksana Yakhnenko Google 111 8th avenue New York, ... | 2013 | 33 |
5,076 | Spike train entropy-rate estimation using hierarchical Dirichlet process priors Karin Knudson Department of Mathematics kknudson@math.utexas.edu Jonathan W. Pillow Center for Perceptual Systems Departments of Psychology & Neuroscience The University of Texas at Austin pillow@mail.utexas.edu Abstract... | 2013 | 330 |
5,077 | Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima Po-Ling Loh Department of Statistics University of California, Berkeley Berkeley, CA 94720 ploh@berkeley.edu Martin J. Wainwright Departments of Statistics and EECS University of California, Berkeley Berk... | 2013 | 331 |
5,078 | Robust Data-Driven Dynamic Programming Grani A. Hanasusanto Imperial College London London SW7 2AZ, UK g.hanasusanto11@imperial.ac.uk Daniel Kuhn École Polytechnique Fédérale de Lausanne CH-1015 Lausanne, Switzerland daniel.kuhn@epfl.ch Abstract In stochastic optimal control the distribution of the ... | 2013 | 332 |
5,079 | Provable Subspace Clustering: When LRR meets SSC Yu-Xiang Wang School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 USA yuxiangw@cs.cmu.edu Huan Xu Dept. of Mech. Engineering National Univ. of Singapore Singapore, 117576 mpexuh@nus.edu.sg Chenlei Leng Department of Statis... | 2013 | 333 |
5,080 | Optimization, Learning, and Games with Predictable Sequences Alexander Rakhlin University of Pennsylvania Karthik Sridharan University of Pennsylvania Abstract We provide several applications of Optimistic Mirror Descent, an online learning algorithm based on the idea of predictable sequences. First, we... | 2013 | 334 |
5,081 | Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search Anshumali Shrivastava Department of Computer Science Computing and Information Science Cornell University Ithaca, NY 14853, USA anshu@cs.cornell.edu Ping Li Department of Statistics & Biostatistics Department of Comput... | 2013 | 335 |
5,082 | Firing rate predictions in optimal balanced networks David G.T. Barrett Group for Neural Theory ´Ecole Normale Sup´erieure Paris, France david.barrett@ens.fr Sophie Den`eve Group for Neural Theory ´Ecole Normale Sup´erieure Paris, France sophie.deneve@ens.fr Christian K. Machens Champalimaud Neu... | 2013 | 336 |
5,083 | Multi-Task Bayesian Optimization Kevin Swersky Department of Computer Science University of Toronto kswersky@cs.toronto.edu Jasper Snoek∗ School of Engineering and Applied Sciences Harvard University jsnoek@seas.harvard.edu Ryan P. Adams School of Engineering and Applied Sciences Harvard Universit... | 2013 | 337 |
5,084 | Using multiple samples to learn mixture models Jason Lee∗ Stanford University Stanford, USA jdl17@stanford.edu Ran Gilad-Bachrach Microsoft Research Redmond, USA rang@microsoft.com Rich Caruana Microsoft Research Redmond, USA rcaruana@microsoft.com Abstract In the mixture models problem it... | 2013 | 338 |
5,085 | First-Order Decomposition Trees Nima Taghipour Jesse Davis Hendrik Blockeel Department of Computer Science, KU Leuven Celestijnenlaan 200A, B-3001 Heverlee, Belgium Abstract Lifting attempts to speedup probabilistic inference by exploiting symmetries in the model. Exact lifted inference methods, like th... | 2013 | 339 |
5,086 | Synthesizing Robust Plans under Incomplete Domain Models Tuan A. Nguyen Arizona State University natuan@asu.edu Subbarao Kambhampati Arizona State University rao@asu.edu Minh Do NASA Ames Research Center minh.do@nasa.gov Abstract Most current planners assume complete domain models and focus on g... | 2013 | 34 |
5,087 | Noise-Enhanced Associative Memories Amin Karbasi Swiss Federal Institute of Technology Zurich amin.karbasi@inf.ethz.ch Amir Hesam Salavati Ecole Polytechnique Federale de Lausanne hesam.salavati@epfl.ch Amin Shokrollahi Ecole Polytechnique Federale de Lausanne amin.shokrollahi@epfl.ch Lav R. Varshne... | 2013 | 340 |
5,088 | Adaptive Submodular Maximization in Bandit Setting Victor Gabillon INRIA Lille - team SequeL Villeneuve d’Ascq, France victor.gabillon@inria.fr Branislav Kveton Technicolor Labs Palo Alto, CA branislav.kveton@technicolor.com Zheng Wen Electrical Engineering Department Stanford University zhengwe... | 2013 | 341 |
5,089 | Approximate inference in latent Gaussian-Markov models from continuous time observations Botond Cseke1 Manfred Opper2 Guido Sanguinetti1 1School of Informatics University of Edinburgh, U.K. {bcseke,gsanguin}@inf.ed.ac.uk 2Computer Science TU Berlin, Germany manfred.opper@tu-berlin.de Abstract We... | 2013 | 342 |
5,090 | Lexical and Hierarchical Topic Regression Viet-An Nguyen Computer Science University of Maryland College Park, MD vietan@cs.umd.edu Jordan Boyd-Graber iSchool & UMIACS University of Maryland College Park, MD jbg@umiacs.umd.edu Philip Resnik Linguistics & UMIACS University of Maryland College... | 2013 | 343 |
5,091 | Adaptive Step–Size for Policy Gradient Methods Matteo Pirotta Dept. Elect., Inf., and Bio. Politecnico di Milano, ITALY matteo.pirotta@polimi.it Marcello Restelli Dept. Elect., Inf., and Bio. Politecnico di Milano, ITALY marcello.restelli@polimi.it Luca Bascetta Dept. Elect., Inf., and Bio. Polite... | 2013 | 344 |
5,092 | Mixed Optimization for Smooth Functions Mehrdad Mahdavi Lijun Zhang Rong Jin Department of Computer Science and Engineering, Michigan State University, MI, USA {mahdavim,zhanglij,rongjin}@msu.edu Abstract It is well known that the optimal convergence rate for stochastic optimization of smooth functions ... | 2013 | 345 |
5,093 | Deep Neural Networks for Object Detection Christian Szegedy Alexander Toshev Dumitru Erhan Google, Inc. {szegedy, toshev, dumitru}@google.com Abstract Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification tasks [14]. In this paper we go one step further and addre... | 2013 | 346 |
5,094 | A simple example of Dirichlet process mixture inconsistency for the number of components Jeffrey W. Miller Division of Applied Mathematics Brown University Providence, RI 02912 jeffrey miller@brown.edu Matthew T. Harrison Division of Applied Mathematics Brown University Providence, RI 02912 matthe... | 2013 | 347 |
5,095 | Learning Kernels Using Local Rademacher Complexity Corinna Cortes Google Research 76 Ninth Avenue New York, NY 10011 corinna@google.com Marius Kloft⇤ Courant Institute & Sloan-Kettering Institute 251 Mercer Street New York, NY 10012 mkloft@cims.nyu.edu Mehryar Mohri Courant Institute & Goo... | 2013 | 348 |
5,096 | Bayesian entropy estimation for binary spike train data using parametric prior knowledge Evan Archer13, Il Memming Park123, Jonathan W. Pillow123 1. Center for Perceptual Systems, 2. Dept. of Psychology, 3. Division of Statistics & Scientific Computation The University of Texas at Austin {memming@austin., ea... | 2013 | 349 |
5,097 | Learning Gaussian Graphical Models with Observed or Latent FVSs Ying Liu Department of EECS Massachusetts Institute of Technology liu_ying@mit.edu Alan S. Willsky Department of EECS Massachusetts Institute of Technology willsky@mit.edu Abstract Gaussian Graphical Models (GGMs) or Gauss Markov rand... | 2013 | 35 |
5,098 | Mid-level Visual Element Discovery as Discriminative Mode Seeking Carl Doersch Carnegie Mellon University cdoersch@cs.cmu.edu Abhinav Gupta Carnegie Mellon University abhinavg@cs.cmu.edu Alexei A. Efros UC Berkeley efros@cs.berkeley.edu Abstract Recent work on mid-level visual representations ai... | 2013 | 350 |
5,099 | Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs Vikash K. Mansinghka⇤1,2, Tejas D. Kulkarni⇤1,2, Yura N. Perov1,2,3, and Joshua B. Tenenbaum1,2 1Computer Science and Artificial Intelligence Laboratory, MIT 2Department of Brain and Cognitive Sciences, MIT 3Institute of... | 2013 | 351 |
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