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 |
|---|---|---|---|
4,900 | Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising Min Xu Machine Learning Department Carnegie Mellon University minx@cs.cmu.edu Tao Qin Microsoft Research Asia taoqin@microsoft.com Tie-Yan Liu Microsoft Research Asia tie-yan.liu@microsoft.com Abstract In search advertisin... | 2013 | 172 |
4,901 | Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions Tamir Hazan University of Haifa Subhransu Maji TTI Chicago Joseph Keshet Bar-Ilan university Tommi Jaakkola CSAIL, MIT Abstract In this work we develop efficient methods for learning random MAP predictors ... | 2013 | 173 |
4,902 | q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions Assaf Glazer Michael Lindenbaum Shaul Markovitch Department of Computer Science, Technion - Israel Institute of Technology {assafgr,mic,shaulm}@cs.technion.ac.il Abstract In this paper we introduce a novel method that can efficiently esti... | 2013 | 174 |
4,903 | Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA Vincent Q. Vu The Ohio State University vqv@stat.osu.edu Juhee Cho University of Wisconsin, Madison chojuhee@stat.wisc.edu Jing Lei Carnegie Mellon University leij09@gmail.com Karl Rohe University of Wisconsin, Madi... | 2013 | 175 |
4,904 | Fast Template Evaluation with Vector Quantization Mohammad Amin Sadeghi Department of Computer Science University of Illinois at Urbana-Champaign msadegh2@illinois.edu David Forsyth Department of Computer Science University of Illinois at Urbana-Champaign daf@illinois.edu Abstract Applying linear te... | 2013 | 176 |
4,905 | Sparse Additive Text Models with Low Rank Background Lei Shi Baidu.com, Inc. P.R. China shilei06@baidu.om Abstract The sparse additive model for text modeling involves the sum-of-exp computing, whose cost is consuming for large scales. Moreover, the assumption of equal background across all classes/topi... | 2013 | 177 |
4,906 | Correlated random features for fast semi-supervised learning Brian McWilliams ETH Z¨urich, Switzerland brian.mcwilliams@inf.ethz.ch David Balduzzi ETH Z¨urich, Switzerland david.balduzzi@inf.ethz.ch Joachim M. Buhmann ETH Z¨urich, Switzerland jbuhmann@inf.ethz.ch Abstract This paper presents Cor... | 2013 | 178 |
4,907 | Variational Planning for Graph-based MDPs Qiang Cheng† Qiang Liu‡ Feng Chen† Alexander Ihler‡ †Department of Automation, Tsinghua University ‡Department of Computer Science, University of California, Irvine †{cheng-q09@mails., chenfeng@mail.}tsinghua.edu.cn ‡{qliu1@,ihler@ics.}uci.edu Abstract Marko... | 2013 | 179 |
4,908 | Graphical Models for Inference with Missing Data Karthika Mohan Judea Pearl Jin Tian Dept. of Computer Science Dept. of Computer Science Dept. of Computer Science Univ. of California, Los Angeles Univ. of California, Los Angeles Iowa State University Los Angeles, CA 90095 Los Angeles, CA 90095 Ame... | 2013 | 18 |
4,909 | Adaptive Anonymity via b-Matching Krzysztof Choromanski Columbia University kmc2178@columbia.edu Tony Jebara Columbia University tj2008@columbia.edu Kui Tang Columbia University kt2384@columbia.edu Abstract The adaptive anonymity problem is formalized where each individual shares their data alon... | 2013 | 180 |
4,910 | Statistical Active Learning Algorithms Maria Florina Balcan Georgia Institute of Technology ninamf@cc.gatech.edu Vitaly Feldman IBM Research - Almaden vitaly@post.harvard.edu Abstract We describe a framework for designing efficient active learning algorithms that are tolerant to random classification no... | 2013 | 181 |
4,911 | The Power of Asymmetry in Binary Hashing Behnam Neyshabur Payman Yadollahpour Yury Makarychev Toyota Technological Institute at Chicago [btavakoli,pyadolla,yury]@ttic.edu Ruslan Salakhutdinov Departments of Statistics and Computer Science University of Toronto rsalakhu@cs.toronto.edu Nathan Srebro ... | 2013 | 182 |
4,912 | Bayesian Mixture Modeling and Inference based Thompson Sampling in Monte-Carlo Tree Search Aijun Bai Univ. of Sci. & Tech. of China baj@mail.ustc.edu.cn Feng Wu University of Southampton fw6e11@ecs.soton.ac.uk Xiaoping Chen Univ. of Sci. & Tech. of China xpchen@ustc.edu.cn Abstract Monte-Carlo t... | 2013 | 183 |
4,913 | Distributed Submodular Maximization: Identifying Representative Elements in Massive Data Baharan Mirzasoleiman ETH Zurich Amin Karbasi ETH Zurich Rik Sarkar University of Edinburgh Andreas Krause ETH Zurich Abstract Many large-scale machine learning problems (such as clustering, non-parametric l... | 2013 | 184 |
4,914 | Analyzing the Harmonic Structure in Graph-Based Learning Xiao-Ming Wu1, Zhenguo Li3, and Shih-Fu Chang1,2 1Department of Electrical Engineering, Columbia University 2Department of Computer Science, Columbia University 3Huawei Noah’s Ark Lab, Hong Kong {xmwu, sfchang}@ee.columbia.edu, li.zhenguo@huawei.com... | 2013 | 185 |
4,915 | Near-optimal Anomaly Detection in Graphs using Lov´asz Extended Scan Statistic James Sharpnack Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 jsharpna@gmail.com Akshay Krishnamurthy Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 akshaykr@cs... | 2013 | 186 |
4,916 | Bellman Error Based Feature Generation using Random Projections on Sparse Spaces Mahdi Milani Fard, Yuri Grinberg, Amir massoud Farahmand, Joelle Pineau, Doina Precup School of Computer Science McGill University Montreal, Canada {mmilan1,ygrinb,amirf,jpineau,dprecup}@cs.mcgill.ca Abstract This paper add... | 2013 | 187 |
4,917 | Similarity Component Analysis Soravit Changpinyo∗ Dept. of Computer Science U. of Southern California Los Angeles, CA 90089 schangpi@usc.edu Kuan Liu∗ Dept. of Computer Science U. of Southern California Los Angeles, CA 90089 kuanl@usc.edu Fei Sha Dept. of Computer Science U. of Southern Califo... | 2013 | 188 |
4,918 | Matrix Completion From any Given Set of Observations Troy Lee Nanyang Technological University and Centre for Quantum Technologies troyjlee@gmail.com Adi Shraibman Department of Computer Science Tel Aviv-Yaffo Academic College adi.shribman@gmail.com Abstract In the matrix completion problem the ai... | 2013 | 189 |
4,919 | Convex Tensor Decomposition via Structured Schatten Norm Regularization Ryota Tomioka Toyota Technological Institute at Chicago Chicago, IL 60637 tomioka@ttic.edu Taiji Suzuki Department of Mathematical and Computing Sciences Tokyo Institute of Technology Tokyo 152-8552, Japan s-taiji@is.titech.ac... | 2013 | 19 |
4,920 | A Deep Architecture for Matching Short Texts Zhengdong Lu Noah’s Ark Lab Huawei Technologies Co. Ltd. Sha Tin, Hong Kong Lu.Zhengdong@huawei.com Hang Li Noah’s Ark Lab Huawei Technologies Co. Ltd. Sha Tin, Hong Kong HangLi.HL@huawei.com Abstract Many machine learning problems can be interpreted ... | 2013 | 190 |
4,921 | Sensor Selection in High-Dimensional Gaussian Trees with Nuisances Daniel Levine MIT LIDS dlevine@mit.edu Jonathan P. How MIT LIDS jhow@mit.edu Abstract We consider the sensor selection problem on multivariate Gaussian distributions where only a subset of latent variables is of inferential interest.... | 2013 | 191 |
4,922 | The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited Matthias Hein, Simon Setzer, Leonardo Jost and Syama Sundar Rangapuram Department of Computer Science Saarland University Abstract Hypergraphs allow one to encode higher-order relationships in data and are thus a very flexible modeling ... | 2013 | 192 |
4,923 | Lasso Screening Rules via Dual Polytope Projection Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye Computer Science and Engineering Arizona State University, Tempe, AZ 85287 {jie.wang.ustc, jiayu.zhou, peter.wonka, jieping.ye}@asu.edu Abstract Lasso is a widely used regression technique to find sparse represen... | 2013 | 193 |
4,924 | Multiscale Dictionary Learning for Estimating Conditional Distributions Francesca Petralia Department of Genetics and Genomic Sciences Icahn School of Medicine at Mt Sinai New York, NY 10128, U.S.A. francesca.petralia@mssm.edu Joshua Vogelstein Child Mind Institute Department of Statistical Science ... | 2013 | 194 |
4,925 | Dirty Statistical 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 Abstract We provide a unified framework for the high-dimensional analysis of “su... | 2013 | 195 |
4,926 | Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation Dahua Lin Toyota Technological Institute at Chicago dhlin@ttic.edu Abstract Reliance on computationally expensive algorithms for inference has been limiting the use of Bayesian nonparametric models in large scale appl... | 2013 | 196 |
4,927 | Exact and Stable Recovery of Pairwise Interaction Tensors Shouyuan Chen Michael R. Lyu Irwin King The Chinese University of Hong Kong {sychen,lyu,king}@cse.cuhk.edu.hk Zenglin Xu Purdue University xu218@purdue.edu Abstract Tensor completion from incomplete observations is a problem of significant p... | 2013 | 197 |
4,928 | A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables Jing Xiang Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 jingx@cs.cmu.edu Seyoung Kim Lane Center for Computational Biology Carnegie Mellon University Pittsburgh, PA 15213 sssykim@cs.cm... | 2013 | 198 |
4,929 | High-Dimensional Gaussian Process Bandits Josip Djolonga ETH Z¨urich josipd@ethz.ch Andreas Krause ETH Z¨urich krausea@ethz.ch Volkan Cevher EPFL volkan.cevher@epfl.ch Abstract Many applications in machine learning require optimizing unknown functions defined over a high-dimensional space from no... | 2013 | 199 |
4,930 | Approximate Gaussian process inference for the drift of stochastic differential equations Andreas Ruttor Computer Science, TU Berlin andreas.ruttor@tu-berlin.de Philipp Batz Computer Science, TU Berlin philipp.batz@tu-berlin.de Manfred Opper Computer Science, TU Berlin manfred.opper@tu-berlin.de A... | 2013 | 2 |
4,931 | Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression Michalis K. Titsias Department of Informatics Athens University of Economics and Business mtitsias@aueb.gr Miguel L´azaro-Gredilla Dpt. Signal Processing & Communications Universidad Carlos III de Madrid - Spain migu... | 2013 | 20 |
4,932 | Generalizing Analytic Shrinkage for Arbitrary Covariance Structures Daniel Bartz Department of Computer Science TU Berlin, Berlin, Germany daniel.bartz@tu-berlin.de Klaus-Robert M¨uller TU Berlin, Berlin, Germany Korea University, Korea, Seoul klaus-robert.mueller@tu-berlin.de Abstract Analytic sh... | 2013 | 200 |
4,933 | Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation Vibhav Vineet Oxford Brookes University, UK vibhav.vineet@gmail.com Carsten Rother TU Dresden, Germany carsten.rother@tu-dresden.de Philip H.S. Torr University of Oxford, UK philip.torr@eng.ox.ac.uk Abstract Many m... | 2013 | 201 |
4,934 | Online Robust PCA via Stochastic Optimization Jiashi Feng ECE Department National University of Singapore jiashi@nus.edu.sg Huan Xu ME Department National University of Singapore mpexuh@nus.edu.sg Shuicheng Yan ECE Department National University of Singapore eleyans@nus.edu.sg Abstract Robus... | 2013 | 202 |
4,935 | Compete to Compute Rupesh Kumar Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Jürgen Schmidhuber IDSIA, USI-SUPSI Manno–Lugano, Switzerland {rupesh, jonathan, sohrob, tino, juergen}@idsia.ch Abstract Local competition among neighboring neurons is common in biological neural networks (NNs... | 2013 | 203 |
4,936 | Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms Yu Zhang Department of Computer Science, Hong Kong Baptist University yuzhang@comp.hkbu.edu.hk Abstract All the existing multi-task local learning methods are defined on homogeneous neighborhood which consists of all data points from onl... | 2013 | 204 |
4,937 | Scalable Influence Estimation in Continuous-Time Diffusion Networks Nan Du∗ Le Song∗ Manuel Gomez-Rodriguez† Hongyuan Zha∗ Georgia Institute of Technology∗ MPI for Intelligent Systems† dunan@gatech.edu lsong@cc.gatech.edu manuelgr@tue.mpg.de zha@cc.gatech.edu Abstract If a piece of information ... | 2013 | 205 |
4,938 | More data speeds up training time in learning halfspaces over sparse vectors Amit Daniely Department of Mathematics The Hebrew University Jerusalem, Israel Nati Linial School of CS and Eng. The Hebrew University Jerusalem, Israel Shai Shalev-Shwartz School of CS and Eng. The Hebrew University ... | 2013 | 206 |
4,939 | Top-Down Regularization of Deep Belief Networks Hanlin Goh∗, Nicolas Thome, Matthieu Cord Laboratoire d’Informatique de Paris 6 UPMC – Sorbonne Universit´es, Paris, France {Firstname.Lastname}@lip6.fr Joo-Hwee Lim† Institute for Infocomm Research A*STAR, Singapore joohwee@i2r.a-star.edu.sg Abstract ... | 2013 | 207 |
4,940 | Polar Operators for Structured Sparse Estimation Xinhua Zhang Machine Learning Research Group National ICT Australia and ANU xinhua.zhang@anu.edu.au Yaoliang Yu and Dale Schuurmans Department of Computing Science, University of Alberta Edmonton, Alberta T6G 2E8, Canada {yaoliang,dale}@cs.ualberta.ca A... | 2013 | 208 |
4,941 | Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space Xinhua Zhang Machine Learning Research Group National ICT Australia and ANU xinhua.zhang@nicta.com.au Wee Sun Lee Department of Computer Science National University of Singapore leews@comp.nus.edu.sg Yee Whye Teh D... | 2013 | 209 |
4,942 | Efficient Online Inference for Bayesian Nonparametric Relational Models Dae Il Kim1, Prem Gopalan2, David M. Blei2, and Erik B. Sudderth1 1Department of Computer Science, Brown University, {daeil,sudderth}@cs.brown.edu 2Department of Computer Science, Princeton University, {pgopalan,blei}@cs.princeton.edu Abst... | 2013 | 21 |
4,943 | Real-Time Inference for a Gamma Process Model of Neural Spiking 1David Carlson, 2Vinayak Rao, 2Joshua Vogelstein, 1Lawrence Carin 1Electrical and Computer Engineering Department, Duke University 2Statistics Department, Duke University {dec18,lcarin}@duke.edu, {var11,jovo}@stat.duke.edu Abstract With simul... | 2013 | 210 |
4,944 | Direct 0-1 Loss Minimization and Margin Maximization with Boosting Shaodan Zhai, Tian Xia, Ming Tan and Shaojun Wang Kno.e.sis Center Department of Computer Science and Engineering Wright State University {zhai.6,xia.7,tan.6,shaojun.wang}@wright.edu Abstract We propose a boosting method, DirectBoost, a ... | 2013 | 211 |
4,945 | Marginals-to-Models Reducibility Tim Roughgarden Stanford University tim@cs.stanford.edu Michael Kearns University of Pennsylvania mkearns@cis.upenn.edu Abstract We consider a number of classical and new computational problems regarding marginal distributions, and inference in models specifying a full... | 2013 | 212 |
4,946 | Sketching Structured Matrices for Faster Nonlinear Regression Haim Avron Vikas Sindhwani IBM T.J. Watson Research Center Yorktown Heights, NY 10598 {haimav,vsindhw}@us.ibm.com David P. Woodruff IBM Almaden Research Center San Jose, CA 95120 dpwoodru@us.ibm.com Abstract Motivated by the desire to... | 2013 | 213 |
4,947 | Variance Reduction for Stochastic Gradient Optimization Chong Wang Xi Chen∗ Alex Smola Eric P. Xing Carnegie Mellon University, University of California, Berkeley∗ {chongw,xichen,epxing}@cs.cmu.edu alex@smola.org Abstract Stochastic gradient optimization is a class of widely used algorithms for tr... | 2013 | 214 |
4,948 | On Decomposing the Proximal Map Yaoliang Yu Department of Computing Science, University of Alberta, Edmonton AB T6G 2E8, Canada yaoliang@cs.ualberta.ca Abstract The proximal map is the key step in gradient-type algorithms, which have become prevalent in large-scale high-dimensional problems. For simple functi... | 2013 | 215 |
4,949 | Documents as multiple overlapping windows into a grid of counts Alessandro Perina1 Nebojsa Jojic1 Manuele Bicego2 Andrzej Turski1 1Microsoft Corporation, Redmond, WA 2University of Verona, Italy Abstract In text analysis documents are often represented as disorganized bags of words; models of such c... | 2013 | 216 |
4,950 | Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model Fang Han Department of Biostatistics Johns Hopkins University Baltimore, MD 21210 fhan@jhsph.edu Han Liu Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544 h... | 2013 | 217 |
4,951 | Optimizing Instructional Policies Robert V. Lindsey⋆, Michael C. Mozer⋆, William J. Huggins⋆, Harold Pashler† ⋆Department of Computer Science, University of Colorado, Boulder † Department of Psychology, University of California, San Diego Abstract Psychologists are interested in developing instructional polic... | 2013 | 218 |
4,952 | Adaptive Market Making via Online Learning Jacob Abernethy⇤ Computer Science and Engineering University of Michigan jabernet@umich.edu Satyen Kale IBM T. J. Watson Research Center sckale@us.ibm.com Abstract We consider the design of strategies for market making in an exchange. A market maker general... | 2013 | 219 |
4,953 | Convergence of Monte Carlo Tree Search in Simultaneous Move Games Viliam Lis´y1 Vojtˇech Kovaˇr´ık1 Marc Lanctot2 Branislav Boˇsansk´y1 1Agent Technology Center Dept. of Computer Science and Engineering FEE, Czech Technical University in Prague <name>.<surname> @agents.fel.cvut.cz 2Department of K... | 2013 | 22 |
4,954 | Learning Prices for Repeated Auctions with Strategic Buyers Kareem Amin University of Pennsylvania akareem@cis.upenn.edu Afshin Rostamizadeh Google Research rostami@google.com Umar Syed Google Research usyed@google.com Abstract Inspired by real-time ad exchanges for online display advertising, w... | 2013 | 220 |
4,955 | Multilinear Dynamical Systems for Tensor Time Series Mark Rogers Lei Li Stuart Russell EECS Department, University of California, Berkeley markrogersjr@berkeley.edu, {leili,russell}@cs.berkeley.edu Abstract Data in the sciences frequently occur as sequences of multidimensional arrays called tensors. H... | 2013 | 221 |
4,956 | Computing the Stationary Distribution, Locally Christina E. Lee LIDS, Department of EECS Massachusetts Institute of Technology celee@mit.edu Asuman Ozdaglar LIDS, Department of EECS Massachusetts Institute of Technology asuman@mit.edu Devavrat Shah Department of EECS Massachusetts Institute of Tec... | 2013 | 222 |
4,957 | Latent Maximum Margin Clustering Guang-Tong Zhou, Tian Lan, Arash Vahdat, and Greg Mori School of Computing Science Simon Fraser University {gza11,tla58,avahdat,mori}@cs.sfu.ca Abstract We present a maximum margin framework that clusters data using latent variables. Using latent representations enables our ... | 2013 | 223 |
4,958 | Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream Daniel Yamins∗ McGovern Institute of Brain Research Massachusetts Institute of Technology Cambridge, MA 02139 yamins@mit.edu Ha Hong∗ McGovern Institute of Brain Research... | 2013 | 224 |
4,959 | Online PCA for Contaminated Data Jiashi Feng ECE Department National University of Singapore jiashi@nus.edu.sg Huan Xu ME Department National University of Singapore mpexuh@nus.edu.sg Shie Mannor EE Department Technion shie@ee.technion.ac.il Shuicheng Yan ECE Department National University... | 2013 | 225 |
4,960 | Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov Google Inc. Mountain View mikolov@google.com Ilya Sutskever Google Inc. Mountain View ilyasu@google.com Kai Chen Google Inc. Mountain View kai@google.com Greg Corrado Google Inc. Mountain View gco... | 2013 | 226 |
4,961 | Learning Multiple Models via Regularized Weighting Daniel Vainsencher Department of Electrical Engineering Technion, Haifa, Israel danielv@tx.technion.ac.il Shie Mannor Department of Electrical Engineering Technion, Haifa, Israel shie@ee.technion.ac.il Huan Xu Mechanical Engineering Department Nat... | 2013 | 227 |
4,962 | Discriminative Transfer Learning with Tree-based Priors Nitish Srivastava Department of Computer Science University of Toronto nitish@cs.toronto.edu Ruslan Salakhutdinov Department of Computer Science and Statistics University of Toronto rsalakhu@cs.toronto.edu Abstract High capacity classifiers, s... | 2013 | 228 |
4,963 | Machine Teaching for Bayesian Learners in the Exponential Family Xiaojin Zhu Department of Computer Sciences, University of Wisconsin-Madison Madison, WI, USA 53706 jerryzhu@cs.wisc.edu Abstract What if there is a teacher who knows the learning goal and wants to design good training data for a machine l... | 2013 | 229 |
4,964 | Learning to Pass Expectation Propagation Messages Nicolas Heess∗ Gatsby Unit, UCL Daniel Tarlow Microsoft Research John Winn Microsoft Research Abstract Expectation Propagation (EP) is a popular approximate posterior inference algorithm that often provides a fast and accurate alternative to sampling-bas... | 2013 | 23 |
4,965 | Online Learning with Switching Costs and Other Adaptive Adversaries Nicol`o Cesa-Bianchi Universit`a degli Studi di Milano Italy Ofer Dekel Microsoft Research USA Ohad Shamir Microsoft Research and the Weizmann Institute Abstract We study the power of different types of adaptive (nonoblivious) a... | 2013 | 230 |
4,966 | From Bandits to Experts: A Tale of Domination and Independence Noga Alon Tel-Aviv University, Israel nogaa@tau.ac.il Nicol`o Cesa-Bianchi Universit`a degli Studi di Milano, Italy nicolo.cesabianchi@unimi.it Claudio Gentile University of Insubria, Italy claudio.gentile@uninsubria.it Yishay Mansour... | 2013 | 231 |
4,967 | Which Space Partitioning Tree to Use for Search? P. Ram Georgia Tech. / Skytree, Inc. Atlanta, GA 30308 p.ram@gatech.edu A. G. Gray Georgia Tech. Atlanta, GA 30308 agray@cc.gatech.edu Abstract We consider the task of nearest-neighbor search with the class of binary-spacepartitioning trees, which inc... | 2013 | 232 |
4,968 | Small-Variance Asymptotics for Hidden Markov Models Anirban Roychowdhury, Ke Jiang, Brian Kulis Department of Computer Science and Engineering The Ohio State University roychowdhury.7@osu.edu, {jiangk,kulis}@cse.ohio-state.edu Abstract Small-variance asymptotics provide an emerging technique for obtaining... | 2013 | 233 |
4,969 | Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis Nikhil S. Rao† nrao2@wisc.edu Christopher R. Cox# crcox@wisc.edu Robert D. Nowak† nowak@ece.wisc.edu Timothy T. Rogers# ttrogers@wisc.edu † Department of Electrical and Computer Engineering, # Department of Psy... | 2013 | 234 |
4,970 | Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints Rishabh Iyer Department of Electrical Engineering University of Washington rkiyer@u.washington.edu Jeff Bilmes Department of Electrical Engineering University of Washington bilmes@u.washington.edu Abstract We investi... | 2013 | 235 |
4,971 | Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition Adel Javanmard Stanford University Stanford, CA 94305 adelj@stanford.edu Andrea Montanari Stanford University Stanford, CA 94305 montanar@stanford.edu Abstract In the high-dimensional regression mode... | 2013 | 236 |
4,972 | Scalable kernels for graphs with continuous attributes Aasa Feragen, Niklas Kasenburg Machine Learning and Computational Biology Group Max Planck Institutes T¨ubingen and DIKU, University of Copenhagen {aasa,niklas.kasenburg}@diku.dk Jens Petersen1, Marleen de Bruijne1,2 1DIKU, University of Copenhagen ... | 2013 | 237 |
4,973 | Bayesian optimization explains human active search Ali Borji Department of Computer Science USC, Los Angeles, 90089 borji@usc.edu Laurent Itti Departments of Neuroscience and Computer Science USC, Los Angeles, 90089 itti@usc.edu Abstract Many real-world problems have complicated objective functions.... | 2013 | 238 |
4,974 | B-tests: Low Variance Kernel Two-Sample Tests Wojciech Zaremba Center for Visual Computing ´Ecole Centrale Paris Chˆatenay-Malabry, France Arthur Gretton Gatsby Unit University College London United Kingdom Matthew Blaschko ´Equipe GALEN Inria Saclay Chˆatenay-Malabry, France {woj.zaremba,arth... | 2013 | 239 |
4,975 | Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits Ben Shababo ∗ Department of Biological Sciences Columbia University, New York, NY 10027 bms2156@columbia.edu Brooks Paige ∗ Department of Engineering Science University of Oxford, Oxford OX1 3PJ, UK brooks@robots.ox.ac.... | 2013 | 24 |
4,976 | Moment-based Uniform Deviation Bounds for k-means and Friends Matus Telgarsky Sanjoy Dasgupta Computer Science and Engineering, UC San Diego {mtelgars,dasgupta}@cs.ucsd.edu Abstract Suppose k centers are fit to m points by heuristically minimizing the k-means cost; what is the corresponding fit over the s... | 2013 | 240 |
4,977 | Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses Harish G. Ramaswamy Computer Science & Automation Indian Institute of Science harish gurup@csa.iisc.ernet.in Shivani Agarwal Computer Science & Automation Indian Institute of Science shivani@csa.iisc.ern... | 2013 | 241 |
4,978 | Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions Ari Pakman and Liam Paninski Department of Statistics Center for Theoretical Neuroscience Grossman Center for the Statistics of Mind Columbia University New York, NY, 10027 Abstract We present a new approach to sample f... | 2013 | 242 |
4,979 | Spectral methods for neural characterization using generalized quadratic models Il Memming Park∗123, Evan Archer∗13, Nicholas Priebe14, & Jonathan W. Pillow123 1. Center for Perceptual Systems, 2. Dept. of Psychology, 3. Division of Statistics & Scientific Computation, 4. Section of Neurobiology, The Universit... | 2013 | 243 |
4,980 | A Latent Source Model for Nonparametric Time Series Classification George H. Chen MIT georgehc@mit.edu Stanislav Nikolov Twitter snikolov@twitter.com Devavrat Shah MIT devavrat@mit.edu Abstract For classifying time series, a nearest-neighbor approach is widely used in practice with performance ... | 2013 | 244 |
4,981 | PAC-Bayes-Empirical-Bernstein Inequality Ilya Tolstikhin Computing Centre Russian Academy of Sciences iliya.tolstikhin@gmail.com Yevgeny Seldin Queensland University of Technology UC Berkeley yevgeny.seldin@gmail.com Abstract We present a PAC-Bayes-Empirical-Bernstein inequality. The inequality is b... | 2013 | 245 |
4,982 | Convex Two-Layer Modeling ¨Ozlem Aslan Hao Cheng Dale Schuurmans Department of Computing Science, University of Alberta Edmonton, AB T6G 2E8, Canada {ozlem,hcheng2,dale}@cs.ualberta.ca Xinhua Zhang Machine Learning Research Group National ICT Australia and ANU xinhua.zhang@anu.edu.au Abstract La... | 2013 | 246 |
4,983 | The Randomized Dependence Coefficient David Lopez-Paz, Philipp Hennig, Bernhard Sch¨olkopf Max Planck Institute for Intelligent Systems Spemannstraße 38, T¨ubingen, Germany {dlopez,phennig,bs}@tue.mpg.de Abstract We introduce the Randomized Dependence Coefficient (RDC), a measure of nonlinear dependence betwe... | 2013 | 247 |
4,984 | Sparse Precision Matrix Estimation with Calibration Tuo Zhao Department of Computer Science Johns Hopkins University Han Liu Department of Operations Research and Financial Engineering Princeton University Abstract We propose a semiparametric method for estimating sparse precision matrix of high dimen... | 2013 | 248 |
4,985 | Thompson Sampling for 1-Dimensional Exponential Family Bandits Nathaniel Korda INRIA Lille - Nord Europe, Team SequeL nathaniel.korda@inria.fr Emilie Kaufmann Institut Mines-Telecom; Telecom ParisTech kaufmann@telecom-paristech.fr Remi Munos INRIA Lille - Nord Europe, Team SequeL remi.munos@inria.fr ... | 2013 | 249 |
4,986 | Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization Nataliya Shapovalova∗ Michalis Raptis† Leonid Sigal‡ Greg Mori∗ ∗Simon Fraser University †Comcast ‡Disney Research {nshapova,mori}@cs.sfu.ca mraptis@cable.comcast.com lsigal@disneyresearch.com Abs... | 2013 | 25 |
4,987 | Accelerating Stochastic Gradient Descent using Predictive Variance Reduction Rie Johnson RJ Research Consulting Tarrytown NY, USA Tong Zhang Baidu Inc., Beijing, China Rutgers University, New Jersey, USA Abstract Stochastic gradient descent is popular for large scale optimization but has slow conver... | 2013 | 250 |
4,988 | Multisensory Encoding, Decoding, and Identification Aurel A. Lazar Department of Electrical Engineering Columbia University New York, NY 10027 aurel@ee.columbia.edu Yevgeniy B. Slutskiy∗ Department of Electrical Engineering Columbia University New York, NY 10027 ys2146@columbia.edu Abstract We in... | 2013 | 251 |
4,989 | Learning invariant representations and applications to face verification Qianli Liao, Joel Z Leibo, and Tomaso Poggio Center for Brains, Minds and Machines McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge MA 02139 lql@mit.edu, jzleibo@mit.edu, tp@ai.mit.edu Abstract ... | 2013 | 252 |
4,990 | Optimistic Concurrency Control for Distributed Unsupervised Learning Xinghao Pan1 Joseph Gonzalez1 Stefanie Jegelka1 Tamara Broderick1,2 Michael I. Jordan1,2 1Department of Electrical Engineering and Computer Science, and 2Department of Statistics University of California, Berkeley Berkeley, CA USA 94720 {x... | 2013 | 253 |
4,991 | Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. Michel Besserve MPI for Intelligent Systems and MPI for Biological Cybernetics, T¨ubingen, Germany michel.besserve@tuebingen.mpg.de Nikos K. Logothetis MPI for Biological Cybernetics, T¨ubingen nikos.logothetis@tuebi... | 2013 | 254 |
4,992 | Sinkhorn Distances: Lightspeed Computation of Optimal Transport Marco Cuturi Graduate School of Informatics, Kyoto University mcuturi@i.kyoto-u.ac.jp Abstract Optimal transport distances are a fundamental family of distances for probability measures and histograms of features. Despite their appealing theo... | 2013 | 255 |
4,993 | Nonparametric Multi-group Membership Model for Dynamic Networks Myunghwan Kim Stanford University Stanford, CA 94305 mykim@stanford.edu Jure Leskovec Stanford University Stanford, CA 94305 jure@cs.stanford.edu Relational data—like graphs, networks, and matrices—is often dynamic, where the relational... | 2013 | 256 |
4,994 | EDML for Learning Parameters in Directed and Undirected Graphical Models Khaled S. Refaat, Arthur Choi, Adnan Darwiche Computer Science Department University of California, Los Angeles {krefaat,aychoi,darwiche}@cs.ucla.edu Abstract EDML is a recently proposed algorithm for learning parameters in Bayesian ... | 2013 | 257 |
4,995 | Flexible sampling of discrete data correlations without the marginal distributions Alfredo Kalaitzis Department of Statistical Science and CSML University College London a.kalaitzis@ucl.ac.uk Ricardo Silva Department of Statistical Science and CSML University College London ricardo@stats.ucl.ac.uk A... | 2013 | 258 |
4,996 | Designed Measurements for Vector Count Data 1Liming Wang, 1David Carlson, 2Miguel Dias Rodrigues, 3David Wilcox, 1Robert Calderbank and 1Lawrence Carin 1Department of Electrical and Computer Engineering, Duke University 2Department of Electronic and Electrical Engineering, University College London 3Departmen... | 2013 | 259 |
4,997 | Integrated Non-Factorized Variational Inference Shaobo Han Duke University Durham, NC 27708 shaobo.han@duke.edu Xuejun Liao Duke University Durham, NC 27708 xjliao@duke.edu Lawrence Carin Duke University Durham, NC 27708 lcarin@duke.edu Abstract We present a non-factorized variational method... | 2013 | 26 |
4,998 | Reasoning With Neural Tensor Networks for Knowledge Base Completion Richard Socher∗, Danqi Chen*, Christopher D. Manning, Andrew Y. Ng Computer Science Department, Stanford University, Stanford, CA 94305, USA richard@socher.org, {danqi,manning}@stanford.edu, ang@cs.stanford.edu Abstract Knowledge bases are ... | 2013 | 260 |
4,999 | Deep content-based music recommendation A¨aron van den Oord, Sander Dieleman, Benjamin Schrauwen Electronics and Information Systems department (ELIS), Ghent University {aaron.vandenoord, sander.dieleman, benjamin.schrauwen}@ugent.be Abstract Automatic music recommendation has become an increasingly relevant ... | 2013 | 261 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.