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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5,600 | M-Best-Diverse Labelings for Submodular Energies and Beyond Alexander Kirillov1 Dmitrij Schlesinger1 Dmitry Vetrov2 Carsten Rother1 Bogdan Savchynskyy1 1 TU Dresden, Dresden, Germany 2 Skoltech, Moscow, Russia alexander.kirillov@tu-dresden.de Abstract We consider the problem of finding M best diver... | 2015 | 108 |
5,601 | BinaryConnect: Training Deep Neural Networks with binary weights during propagations Matthieu Courbariaux ´Ecole Polytechnique de Montr´eal matthieu.courbariaux@polymtl.ca Yoshua Bengio Universit´e de Montr´eal, CIFAR Senior Fellow yoshua.bengio@gmail.com Jean-Pierre David ´Ecole Polytechnique de Mont... | 2015 | 109 |
5,602 | Differentially Private Subspace Clustering Yining Wang, Yu-Xiang Wang and Aarti Singh Machine Learning Department, Carnegie Mellon Universty, Pittsburgh, USA {yiningwa,yuxiangw,aarti}@cs.cmu.edu Abstract Subspace clustering is an unsupervised learning problem that aims at grouping data points into multiple ... | 2015 | 11 |
5,603 | No-Regret Learning in Bayesian Games Jason Hartline Northwestern University Evanston, IL hartline@northwestern.edu Vasilis Syrgkanis Microsoft Research New York, NY vasy@microsoft.com ´Eva Tardos Cornell University Ithaca, NY eva@cs.cornell.edu Abstract Recent price-of-anarchy analyses of ga... | 2015 | 110 |
5,604 | Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso 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 Abstract Gaussian Graphical Models (GGMs) are popular tools for studying network structures. However... | 2015 | 111 |
5,605 | Parallelizing MCMC with Random Partition Trees Xiangyu Wang Dept. of Statistical Science Duke University xw56@stat.duke.edu Fangjian Guo Dept. of Computer Science Duke University guo@cs.duke.edu Katherine A. Heller Dept. of Statistical Science Duke University kheller@stat.duke.edu David B. Dun... | 2015 | 112 |
5,606 | Convergence rates of sub-sampled Newton methods Murat A. Erdogdu Department of Statistics Stanford University erdogdu@stanford.edu Andrea Montanari Department of Statistics and Electrical Engineering Stanford University montanari@stanford.edu Abstract We consider the problem of minimizing a sum of... | 2015 | 113 |
5,607 | Learning Theory and Algorithms for Forecasting Non-Stationary Time Series Vitaly Kuznetsov Courant Institute New York, NY 10011 vitaly@cims.nyu.edu Mehryar Mohri Courant Institute and Google Research New York, NY 10011 mohri@cims.nyu.edu Abstract We present data-dependent learning bounds for the g... | 2015 | 114 |
5,608 | Equilibrated adaptive learning rates for non-convex optimization Yann N. Dauphin1 Universit´e de Montr´eal dauphiya@iro.umontreal.ca Harm de Vries1 Universit´e de Montr´eal devries@iro.umontreal.ca Yoshua Bengio Universit´e de Montr´eal yoshua.bengio@umontreal.ca Abstract Parameter-specific adapt... | 2015 | 115 |
5,609 | Optimal Linear Estimation under Unknown Nonlinear Transform Xinyang Yi The University of Texas at Austin yixy@utexas.edu Zhaoran Wang Princeton University zhaoran@princeton.edu Constantine Caramanis The University of Texas at Austin constantine@utexas.edu Han Liu Princeton University hanliu@pr... | 2015 | 116 |
5,610 | Analysis of Robust PCA via Local Incoherence Huishuai Zhang Department of EECS Syracuse University Syracuse, NY 13244 hzhan23@syr.edu Yi Zhou Department of EECS Syracuse University Syracuse, NY 13244 yzhou35@syr.edu Yingbin Liang Department of EECS Syracuse University Syracuse, NY 13244 yl... | 2015 | 117 |
5,611 | Probabilistic Variational Bounds for Graphical Models Qiang Liu Computer Science Dartmouth College qliu@cs.dartmouth.edu John Fisher III CSAIL MIT fisher@csail.mit.edu Alexander Ihler Computer Science Univ. of California, Irvine ihler@ics.uci.edu Abstract Variational algorithms such as tre... | 2015 | 118 |
5,612 | The Human Kernel Andrew Gordon Wilson CMU Christoph Dann CMU Christopher G. Lucas University of Edinburgh Eric P. Xing CMU Abstract Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexi... | 2015 | 119 |
5,613 | Matrix Completion with Noisy Side Information Kai-Yang Chiang∗ Cho-Jui Hsieh † Inderjit S. Dhillon ∗ ∗University of Texas at Austin † University of California at Davis ∗{kychiang,inderjit}@cs.utexas.edu † chohsieh@ucdavis.edu Abstract We study the matrix completion problem with side information. Sid... | 2015 | 12 |
5,614 | Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization Xiangru Lian, Yijun Huang, Yuncheng Li, and Ji Liu Department of Computer Science, University of Rochester {lianxiangru,huangyj0,raingomm,ji.liu.uwisc}@gmail.com Abstract Asynchronous parallel implementations of stochastic gradient (SG) ha... | 2015 | 120 |
5,615 | Evaluating the statistical significance of biclusters Jason D. Lee, Yuekai Sun, and Jonathan Taylor Institute of Computational and Mathematical Engineering Stanford University Stanford, CA 94305 {jdl17,yuekai,jonathan.taylor}@stanford.edu Abstract Biclustering (also known as submatrix localization) is a pr... | 2015 | 121 |
5,616 | Fast and Guaranteed Tensor Decomposition via Sketching Yining Wang, Hsiao-Yu Tung, Alex Smola Machine Learning Department Carnegie Mellon University, Pittsburgh, PA 15213 {yiningwa,htung}@cs.cmu.edu alex@smola.org Anima Anandkumar Department of EECS University of California Irvine Irvine, CA 92697 ... | 2015 | 122 |
5,617 | Inverse Reinforcement Learning with Locally Consistent Reward Functions Quoc Phong Nguyen†, Kian Hsiang Low†, and Patrick Jaillet§ Dept. of Computer Science, National University of Singapore, Republic of Singapore† Dept. of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, USA§... | 2015 | 123 |
5,618 | A hybrid sampler for Poisson-Kingman mixture models Mar´ıa Lomel´ı Gatsby Unit University College London mlomeli@gatsby.ucl.ac.uk Stefano Favaro Department of Economics and Statistics University of Torino and Collegio Carlo Alberto stefano.favaro@unito.it Yee Whye Teh Department of Statistics Un... | 2015 | 124 |
5,619 | Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen∗,† Aditya Krishna Menon†,∗ Robert C. Williamson∗,† ∗The Australian National University †National ICT Australia { brendan.vanrooyen, aditya.menon, bob.williamson }@nicta.com.au Abstract Convex potential minimisatio... | 2015 | 125 |
5,620 | VISALOGY: Answering Visual Analogy Questions Fereshteh Sadeghi University of Washington fsadeghi@cs.washington.edu C. Lawrence Zitnick Microsoft Research larryz@microsoft.com Ali Farhadi University of Washington, The Allen Institute for AI ali@cs.washington.edu Abstract In this paper, we study the... | 2015 | 126 |
5,621 | Cornering Stationary and Restless Mixing Bandits with Remix-UCB Julien Audiffren CMLA ENS Cachan, Paris Saclay University 94235 Cachan France audiffren@cmla.ens-cachan.fr Liva Ralaivola QARMA, LIF, CNRS Aix Marseille University F-13289 Marseille cedex 9, France liva.ralaivola@lif.univ-mrs.fr Abs... | 2015 | 127 |
5,622 | The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels Purnamrita Sarkar Department of Statistics University of Texas at Austin purnamritas@austin.utexas.edu Deepayan Chakrabarti IROM, McCombs School of Business University of Texas at Austin deepay@utexas.edu Peter Bickel ... | 2015 | 128 |
5,623 | On the Accuracy of Self-Normalized Log-Linear Models Jacob Andreas∗, Maxim Rabinovich∗, Michael I. Jordan, Dan Klein Computer Science Division, University of California, Berkeley {jda,rabinovich,jordan,klein}@cs.berkeley.edu Abstract Calculation of the log-normalizer is a major computational obstacle in app... | 2015 | 129 |
5,624 | Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations Kirthevasan Kandasamy Carnegie Mellon University kandasamy@cs.cmu.edu Akshay Krishnamurthy Microsoft Research, NY akshaykr@cs.cmu.edu Barnab´as P´oczos, Larry Wasserman Carnegie Mellon University bapoczos@cs.cmu.ed... | 2015 | 13 |
5,625 | Learnability of Influence in Networks Harikrishna Narasimhan David C. Parkes Yaron Singer Harvard University, Cambridge, MA 02138 hnarasimhan@seas.harvard.edu, {parkes, yaron}@seas.harvard.edu Abstract We show PAC learnability of influence functions for three common influence models, namely, the Linear Thres... | 2015 | 130 |
5,626 | Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes Ryan Giordano UC Berkeley rgiordano@berkeley.edu Tamara Broderick MIT tbroderick@csail.mit.edu Michael Jordan UC Berkeley jordan@cs.berkeley.edu Abstract Mean field variational Bayes (MFVB) is a popular po... | 2015 | 131 |
5,627 | Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization Fredrik D. Johansson Computer Science & Engineering Chalmers University of Technology G¨oteborg, SE-412 96, Sweden frejohk@chalmers.se Ankani Chattoraj∗ Brain & Cognitive Sciences University of Rochest... | 2015 | 132 |
5,628 | End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture Jianshu Chen⇤, Ji He†, Yelong Shen⇤, Lin Xiao⇤, Xiaodong He⇤, Jianfeng Gao⇤, Xinying Song⇤and Li Deng⇤ ⇤Microsoft Research, Redmond, WA 98052, USA, {jianshuc,yeshen,lin.xiao,xiaohe,jfgao,xinson,deng}@microsoft.com †Depart... | 2015 | 133 |
5,629 | Robust Spectral Inference for Joint Stochastic Matrix Factorization Moontae Lee, David Bindel Dept. of Computer Science Cornell University Ithaca, NY 14850 {moontae,bindel}@cs.cornell.edu David Mimno Dept. of Information Science Cornell University Ithaca, NY 14850 mimno@cornell.edu Abstract Sp... | 2015 | 134 |
5,630 | Minimax Time Series Prediction Wouter M. Koolen Centrum Wiskunde & Informatica wmkoolen@cwi.nl Alan Malek UC Berkeley malek@berkeley.edu Peter L. Bartlett UC Berkeley & QUT bartlett@cs.berkeley.edu Yasin Abbasi-Yadkori Queensland University of Technology yasin.abbasiyadkori@qut.edu.au Abstract... | 2015 | 135 |
5,631 | Learning to Segment Object Candidates Pedro O. Pinheiro∗ Ronan Collobert Piotr Doll´ar pedro@opinheiro.com locronan@fb.com pdollar@fb.com Facebook AI Research Abstract Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and ... | 2015 | 136 |
5,632 | A Theory of Decision Making Under Dynamic Context Michael Shvartsman Princeton Neuroscience Institute Princeton University Princeton, NJ, 08544 ms44@princeton.edu Vaibhav Srivastava Department of Mechanical and Aerospace Engineering Princeton University Princeton, NJ, 08544 vaibhavs@princeton.edu ... | 2015 | 137 |
5,633 | Particle Gibbs for Infinite Hidden Markov Models Nilesh Tripuraneni* University of Cambridge nt357@cam.ac.uk Shixiang Gu* University of Cambridge MPI for Intelligent Systems sg717@cam.ac.uk Hong Ge University of Cambridge hg344@cam.ac.uk Zoubin Ghahramani University of Cambridge zoubin@eng.cam.... | 2015 | 138 |
5,634 | Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff Ofer Dekel Microsoft Research Redmond, WA oferd@microsoft.com Ronen Eldan Weizmann Institute Rehovot, Israel roneneldan@gmail.com Tomer Koren Technion Haifa, Israel tomerk@technion.ac.il Abstract Bandit convex optimiza... | 2015 | 139 |
5,635 | Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Ga¨el Varoquaux INRIA, Parietal team, Saclay, France CEA, Neurospin, Gif-sur-Yvette, France firstname.lastname@inria.fr Abstract Imaging neuroscience links... | 2015 | 14 |
5,636 | Compressive spectral embedding: sidestepping the SVD Dinesh Ramasamy dineshr@ece.ucsb.edu ECE Department, UC Santa Barbara Upamanyu Madhow madhow@ece.ucsb.edu ECE Department, UC Santa Barbara Abstract Spectral embedding based on the Singular Value Decomposition (SVD) is a widely used “preprocessing”... | 2015 | 140 |
5,637 | Winner-Take-All Autoencoders Alireza Makhzani, Brendan Frey University of Toronto makhzani, frey@psi.toronto.edu Abstract In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion. We first introduce fully-connected winner-take-all autoen... | 2015 | 141 |
5,638 | Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen, for the ADNI∗ Department of Radiology and BRIC University of North Carolina at Chapel Hill, NC, 27599, USA {eadeli,khthung,le_an,fengshi,dgshen}@med.unc.edu A... | 2015 | 142 |
5,639 | COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution Mehrdad Farajtabar∗ Yichen Wang∗ Manuel Gomez-Rodriguez† Shuang Li∗ Hongyuan Zha∗ Le Song∗ Georgia Institute of Technology∗ MPI for Software Systems† {mehrdad,yichen.wang,sli370}@gatech.edu manuelgr@mpi-sws.or... | 2015 | 143 |
5,640 | Nearly-Optimal Private LASSO∗ Kunal Talwar Google Research kunal@google.com Abhradeep Thakurta (Previously) Yahoo! Labs guhathakurta.abhradeep@gmail.com Li Zhang Google Research liqzhang@google.com Abstract We present a nearly optimal differentially private version of the well known LASSO estima... | 2015 | 144 |
5,641 | Calibrated Structured Prediction Volodymyr Kuleshov Department of Computer Science Stanford University Stanford, CA 94305 Percy Liang Department of Computer Science Stanford University Stanford, CA 94305 Abstract In user-facing applications, displaying calibrated confidence measures— pr... | 2015 | 145 |
5,642 | Spectral Representations for Convolutional Neural Networks Oren Rippel Department of Mathematics Massachusetts Institute of Technology rippel@math.mit.edu Jasper Snoek Twitter and Harvard SEAS jsnoek@seas.harvard.edu Ryan P. Adams Twitter and Harvard SEAS rpa@seas.harvard.edu Abstract Discrete... | 2015 | 146 |
5,643 | On the consistency theory of high dimensional variable screening Xiangyu Wang Dept. of Statistical Science Duke University, USA xw56@stat.duke.edu Chenlei Leng Dept. of Statistics University of Warwick, UK C.Leng@warwick.ac.uk David B. Dunson Dept. of Statistical Science Duke University, USA d... | 2015 | 147 |
5,644 | Revenue Optimization against Strategic Buyers Mehryar Mohri Courant Institute of Mathematical Sciences 251 Mercer Street New York, NY, 10012 Andr´es Mu˜noz Medina⇤ Google Research 111 8th Avenue New York, NY, 10011 Abstract We present a revenue optimization algorithm for posted-price auctions when... | 2015 | 148 |
5,645 | The Population Posterior and Bayesian Modeling on Streams James McInerney Columbia University james@cs.columbia.edu Rajesh Ranganath Princeton University rajeshr@cs.princeton.edu David Blei Columbia University david.blei@columbia.edu Abstract Many modern data analysis problems involve inferences... | 2015 | 149 |
5,646 | Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung Department of Computer Science and Engineering Hong Kong University of Science and Technology {xshiab,zchenbb,hwangaz,dyyeung}@cse.ust.hk Wai-kin Wong Wang-chun Woo ... | 2015 | 15 |
5,647 | Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions Amar Shah Department of Engineering Cambridge University as793@cam.ac.uk Zoubin Ghahramani Department of Engineering University of Cambridge zoubin@eng.cam.ac.uk Abstract We develop parallel predictiv... | 2015 | 150 |
5,648 | The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors Dan Rosenbaum School of Computer Science and Engineering Hebrew University of Jerusalem Yair Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Abstract In r... | 2015 | 151 |
5,649 | Fighting Bandits with a New Kind of Smoothness Jacob Abernethy University of Michigan jabernet@umich.edu Chansoo Lee University of Michigan chansool@umich.edu Ambuj Tewari University of Michigan tewaria@umich.edu Abstract We provide a new analysis framework for the adversarial multi-armed bandit ... | 2015 | 152 |
5,650 | Sparse and Low-Rank Tensor Decomposition Parikshit Shah parikshit@yahoo-inc.com Nikhil Rao nikhilr@cs.utexas.edu Gongguo Tang gtang@mines.edu Abstract Motivated by the problem of robust factorization of a low-rank tensor, we study the question of sparse and low-rank tensor decomposition. We present an... | 2015 | 153 |
5,651 | Testing Closeness With Unequal Sized Samples Bhaswar B. Bhattacharya Department of Statistics Stanford University California, CA 94305 bhaswar@stanford.edu Gregory Valiant∗ Department of Computer Science Stanford University California, CA 94305 valiant@stanford.edu Abstract We consider the probl... | 2015 | 154 |
5,652 | Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach Yinlam Chow Stanford University ychow@stanford.edu Aviv Tamar UC Berkeley avivt@berkeley.edu Shie Mannor Technion shie@ee.technion.ac.il Marco Pavone Stanford University pavone@stanford.edu Abstract In this paper we ad... | 2015 | 155 |
5,653 | Fast Lifted MAP Inference via Partitioning Somdeb Sarkhel The University of Texas at Dallas Parag Singla I.I.T. Delhi Vibhav Gogate The University of Texas at Dallas Abstract Recently, there has been growing interest in lifting MAP inference algorithms for Markov logic networks (MLNs). A key advantage... | 2015 | 156 |
5,654 | Algorithmic Stability and Uniform Generalization Ibrahim Alabdulmohsin King Abdullah University of Science and Technology Thuwal 23955, Saudi Arabia ibrahim.alabdulmohsin@kaust.edu.sa Abstract One of the central questions in statistical learning theory is to determine the conditions under which agents can l... | 2015 | 157 |
5,655 | Learning with Group Invariant Features: A Kernel Perspective. Youssef Mroueh IBM Watson Group mroueh@us.ibm.com Stephen Voinea∗ CBMM, MIT. voinea@mit.edu ∗Co-first author Tomaso Poggio CBMM, MIT . tp@ai.mit.edu Abstract We analyze in this paper a random feature map based on a theory of invarian... | 2015 | 158 |
5,656 | Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number Janne H. Korhonen Helsinki Institute for Information Technology HIIT Department of Computer Science University of Helsinki janne.h.korhonen@helsinki.fi Pekka Parviainen Helsinki Institute for Information Technology HIIT Dep... | 2015 | 159 |
5,657 | Infinite Factorial Dynamical Model Isabel Valera∗ Max Planck Institute for Software Systems ivalera@mpi-sws.org Francisco J. R. Ruiz∗ Department of Computer Science Columbia University f.ruiz@columbia.edu Lennart Svensson Department of Signals and Systems Chalmers University of Technology lennart... | 2015 | 16 |
5,658 | Convergence Analysis of Prediction Markets via Randomized Subspace Descent Rafael Frongillo Department of Computer Science University of Colorado, Boulder raf@colorado.edu Mark D. Reid Research School of Computer Science The Australian National University & NICTA mark.reid@anu.edu.au Abstract Pred... | 2015 | 160 |
5,659 | SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk Guillaume Papa, St´ephan Cl´emenc¸on LTCI, CNRS, T´el´ecom ParisTech Universit´e Paris-Saclay, 75013 Paris, France first.last@telecom-paristech.fr Aur´elien Bellet Magnet Team, INRIA Lille - Nord Europe 59650 Vil... | 2015 | 161 |
5,660 | Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection Jie Wang1, Jieping Ye1,2 1Computational Medicine and Bioinformatics 2Department of Electrical Engineering and Computer Science University of Michigan, Ann Arbor, MI 48109 {jwangumi, jpye}@umich.edu Abstract Tree st... | 2015 | 162 |
5,661 | From random walks to distances on unweighted graphs Tatsunori B. Hashimoto MIT EECS thashim@mit.edu Yi Sun MIT Mathematics yisun@mit.edu Tommi S. Jaakkola MIT EECS tommi@mit.edu Abstract Large unweighted directed graphs are commonly used to capture relations between entities. A fundamental probl... | 2015 | 163 |
5,662 | Tensorizing Neural Networks Alexander Novikov1,4 Dmitry Podoprikhin1 Anton Osokin2 Dmitry Vetrov1,3 1Skolkovo Institute of Science and Technology, Moscow, Russia 2INRIA, SIERRA project-team, Paris, France 3National Research University Higher School of Economics, Moscow, Russia 4Institute of Numerical Ma... | 2015 | 164 |
5,663 | On some provably correct cases of variational inference for topic models Pranjal Awasthi Department of Computer Science Rutgers University New Brunswick, NJ 08901 pranjal.awasthi@rutgers.edu Andrej Risteski Department of Computer Science Princeton University Princeton, NJ 08540 risteski@cs.princet... | 2015 | 165 |
5,664 | GAP Safe screening rules for sparse multi-task and multi-class models Eugene Ndiaye Olivier Fercoq Alexandre Gramfort Joseph Salmon LTCI, CNRS, T´el´ecom ParisTech, Universit´e Paris-Saclay Paris, 75013, France firstname.lastname@telecom-paristech.fr Abstract High dimensional regression benefits from... | 2015 | 166 |
5,665 | The Pareto Regret Frontier for Bandits Tor Lattimore Department of Computing Science University of Alberta, Canada tor.lattimore@gmail.com Abstract Given a multi-armed bandit problem it may be desirable to achieve a smallerthan-usual worst-case regret for some special actions. I show that the price for su... | 2015 | 167 |
5,666 | Measuring Sample Quality with Stein’s Method Jackson Gorham Department of Statistics Stanford University Lester Mackey Department of Statistics Stanford University Abstract To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased Markov chain Monte Carlo procedures that ... | 2015 | 168 |
5,667 | Predtron: A Family of Online Algorithms for General Prediction Problems Prateek Jain Microsoft Research, INDIA prajain@microsoft.com Nagarajan Natarajan University of Texas at Austin, USA naga86@cs.utexas.edu Ambuj Tewari University of Michigan, Ann Arbor, USA tewaria@umich.edu Abstract Modern p... | 2015 | 169 |
5,668 | Dependent Multinomial Models Made Easy: Stick Breaking with the P´olya-Gamma Augmentation Scott W. Linderman∗ Harvard University Cambridge, MA 02138 swl@seas.harvard.edu Matthew J. Johnson∗ Harvard University Cambridge, MA 02138 mattjj@csail.mit.edu Ryan P. Adams Twitter & Harvard University Cam... | 2015 | 17 |
5,669 | MCMC for Variationally Sparse Gaussian Processes James Hensman CHICAS, Lancaster University james.hensman@lancaster.ac.uk Alexander G. de G. Matthews University of Cambridge am554@cam.ac.uk Maurizio Filippone EURECOM maurizio.filippone@eurecom.fr Zoubin Ghahramani University of Cambridge zoubin@... | 2015 | 170 |
5,670 | Action-Conditional Video Prediction using Deep Networks in Atari Games Junhyuk Oh Xiaoxiao Guo Honglak Lee Richard Lewis Satinder Singh University of Michigan, Ann Arbor, MI 48109, USA {junhyuk,guoxiao,honglak,rickl,baveja}@umich.edu Abstract Motivated by vision-based reinforcement learning (RL) pro... | 2015 | 171 |
5,671 | Unified View of Matrix Completion under General Structural Constraints Suriya Gunasekar UT at Austin, USA suriya@utexas.edu Arindam Banerjee UMN Twin Cities, USA banerjee@cs.umn.edu Joydeep Ghosh UT at Austin, USA ghosh@ece.utexas.edu Abstract Matrix completion problems have been widely studied u... | 2015 | 172 |
5,672 | When are Kalman-Filter Restless Bandits Indexable? Christopher Dance and Tomi Silander Xerox Research Centre Europe 6 chemin de Maupertuis, Meylan, Is`ere, France {dance,silander}@xrce.xerox.com Abstract We study the restless bandit associated with an extremely simple scalar Kalman filter model in discrete... | 2015 | 173 |
5,673 | 3D Object Proposals for Accurate Object Class Detection Xiaozhi Chen1 Kaustav Kundu 2 Yukun Zhu2 Andrew Berneshawi2 Huimin Ma1 Sanja Fidler2 Raquel Urtasun2 1Department of Electronic Engineering Tsinghua University 2Department of Computer Science University of Toronto chenxz12@mails.tsinghua.e... | 2015 | 174 |
5,674 | Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm Qinqing Zheng University of Chicago qinqing@cs.uchicago.edu Ryota Tomioka Toyota Technological Institute at Chicago tomioka@ttic.edu Abstract We consider the problem of recovering a low-rank tensor from its noisy observati... | 2015 | 175 |
5,675 | Biologically Inspired Dynamic Textures for Probing Motion Perception Jonathan Vacher CNRS UNIC and Ceremade Univ. Paris-Dauphine 75775 Paris Cedex 16, FRANCE vacher@ceremade.dauphine.fr Andrew Isaac Meso Institut de Neurosciences de la Timone UMR 7289 CNRS/Aix-Marseille Universit´e 13385 Marseille C... | 2015 | 176 |
5,676 | Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling Xiaocheng Shang∗ University of Edinburgh x.shang@ed.ac.uk Zhanxing Zhu∗ University of Edinburgh zhanxing.zhu@ed.ac.uk Benedict Leimkuhler University of Edinburgh b.leimkuhler@ed.ac.uk Amos J. Storkey University ... | 2015 | 177 |
5,677 | Semi-supervised Sequence Learning Andrew M. Dai Google Inc. adai@google.com Quoc V. Le Google Inc. qvl@google.com Abstract We present two approaches to use unlabeled data to improve Sequence Learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a l... | 2015 | 178 |
5,678 | Non-convex Statistical Optimization for Sparse Tensor Graphical Model Wei Sun Yahoo Labs Sunnyvale, CA sunweisurrey@yahoo-inc.com Zhaoran Wang Department of Operations Research and Financial Engineering Princeton University Princeton, NJ zhaoran@princeton.edu Han Liu Department of Operations R... | 2015 | 179 |
5,679 | Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent Ian E.H. Yen ∗ Kai Zhong ∗ Cho-Jui Hsieh † Pradeep Ravikumar ∗ Inderjit S. Dhillon ∗ ∗University of Texas at Austin † University of California at Davis ∗{ianyen,pradeepr,inderjit}@cs.utexas.edu zhongkai@ices.utexas.edu † ... | 2015 | 18 |
5,680 | Lifted Symmetry Detection and Breaking for MAP Inference Tim Kopp University of Rochester Rochester, NY tkopp@cs.rochester.edu Parag Singla I.I.T. Delhi Hauz Khas, New Delhi parags@cse.iitd.ac.in Henry Kautz University of Rochester Rochester, NY kautz@cs.rochester.edu Abstract Symmetry bre... | 2015 | 180 |
5,681 | Private Graphon Estimation for Sparse Graphs∗ Christian Borgs Jennifer T. Chayes Microsoft Research New England Cambridge, MA, USA. {cborgs,jchayes}@microsoft.com Adam Smith Pennsylvania State University University Park, PA, USA. asmith@psu.edu Abstract We design algorithms for fitting a high-dimen... | 2015 | 181 |
5,682 | Online Learning with Adversarial Delays Kent Quanrud∗and Daniel Khashabi† Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801 {quanrud2,khashab2}@illinois.edu Abstract We study the performance of standard online learning algorithms when the feedback is delayed by an ... | 2015 | 182 |
5,683 | Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems Yuxin Chen Department of Statistics Stanford University Stanford, CA 94305 yxchen@stanfor.edu Emmanuel J. Candès Department of Mathematics and Department of Statistics Stanford University Stanford, CA 94305 c... | 2015 | 183 |
5,684 | Statistical Topological Data Analysis – A Kernel Perspective Roland Kwitt Department of Computer Science University of Salzburg rkwitt@gmx.at Stefan Huber IST Austria stefan.huber@ist.ac.at Marc Niethammer Department of Computer Science and BRIC UNC Chapel Hill mn@cs.unc.edu Weili Lin Depart... | 2015 | 184 |
5,685 | A Structural Smoothing Framework For Robust Graph-Comparison Pinar Yanardag Department of Computer Science Purdue University West Lafayette, IN, 47906, USA ypinar@purdue.edu S.V.N. Vishwanathan Department of Computer Science University of California Santa Cruz, CA, 95064, USA vishy@ucsc.edu Abst... | 2015 | 185 |
5,686 | Bandits with Unobserved Confounders: A Causal Approach Elias Bareinboim∗ Department of Computer Science Purdue University eb@purdue.edu Andrew Forney∗ Department of Computer Science University of California, Los Angeles forns@cs.ucla.edu Judea Pearl Department of Computer Science University of C... | 2015 | 186 |
5,687 | Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients Bo Xie1, Yingyu Liang2, Le Song1 1Georgia Institute of Technology bo.xie@gatech.edu, lsong@cc.gatech.edu 2Princeton University yingyul@cs.princeton.edu Abstract Nonlinear component analysis such as kernel Principle Component Analysis... | 2015 | 187 |
5,688 | A Market Framework for Eliciting Private Data Bo Waggoner Harvard SEAS bwaggoner@fas.harvard.edu Rafael Frongillo University of Colorado raf@colorado.edu Jacob Abernethy University of Michigan jabernet@umich.edu Abstract We propose a mechanism for purchasing information from a sequence of particip... | 2015 | 188 |
5,689 | A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice Tasuku Soma The University of Tokyo tasuku soma@mist.i.u-tokyo.ac.jp Yuichi Yoshida National Institute of Informatics, and Preferred Infrastructure, Inc. yyoshida@nii.ac.jp Abstract We consider a generali... | 2015 | 189 |
5,690 | Data Generation as Sequential Decision Making Philip Bachman McGill University, School of Computer Science phil.bachman@gmail.com Doina Precup McGill University, School of Computer Science dprecup@cs.mcgill.ca Abstract We connect a broad class of generative models through their shared reliance on sequ... | 2015 | 19 |
5,691 | Space-Time Local Embeddings Ke Sun1∗ Jun Wang2 Alexandros Kalousis3,1 St´ephane Marchand-Maillet1 1 Viper Group, Computer Vision and Multimedia Laboratory, University of Geneva sunk.edu@gmail.com, Stephane.Marchand-Maillet@unige.ch, and 2 Expedia, Switzerland, jwang1@expedia.com, and 3 Business Informatic... | 2015 | 190 |
5,692 | Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path Daniel Hsu Columbia University djhsu@cs.columbia.edu Aryeh Kontorovich Ben-Gurion University karyeh@cs.bgu.ac.il Csaba Szepesv´ari University of Alberta szepesva@cs.ualberta.ca Abstract This article provides the first pr... | 2015 | 191 |
5,693 | Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach Bal´azs Sz¨or´enyi Technion, Haifa, Israel / MTA-SZTE Research Group on Artificial Intelligence, Hungary szorenyibalazs@gmail.com R´obert Busa-Fekete, Adil Paul, Eyke H¨ullermeier Department of Computer Science University of Paderbor... | 2015 | 192 |
5,694 | Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets Pascal Vincent∗, Alexandre de Brébisson, Xavier Bouthillier Département d’Informatique et de Recherche Opérationnelle Université de Montréal, Montréal, Québec, CANADA ∗and CIFAR Abstract An important class of problems... | 2015 | 193 |
5,695 | A Gaussian Process Model of Quasar Spectral Energy Distributions Andrew Miller∗, Albert Wu School of Engineering and Applied Sciences Harvard University acm@seas.harvard.edu, awu@college.harvard.edu Jeffrey Regier, Jon McAuliffe Department of Statistics University of California, Berkeley {jeff, jon}@s... | 2015 | 194 |
5,696 | Fast Convergence of Regularized Learning in Games Vasilis Syrgkanis Microsoft Research New York, NY vasy@microsoft.com Alekh Agarwal Microsoft Research New York, NY alekha@microsoft.com Haipeng Luo Princeton University Princeton, NJ haipengl@cs.princeton.edu Robert E. Schapire Microsoft Rese... | 2015 | 195 |
5,697 | Communication Complexity of Distributed Convex Learning and Optimization Yossi Arjevani Weizmann Institute of Science Rehovot 7610001, Israel yossi.arjevani@weizmann.ac.il Ohad Shamir Weizmann Institute of Science Rehovot 7610001, Israel ohad.shamir@weizmann.ac.il Abstract We study the fundamental... | 2015 | 196 |
5,698 | Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings Piyush Rai†∗, Changwei Hu∗, Ricardo Henao∗, Lawrence Carin∗ †CSE Dept, IIT Kanpur ∗ECE Dept, Duke University piyush@cse.iitk.ac.in, {ch237,r.henao,lcarin}@duke.edu Abstract We present a scalable Bayesian multi-label learning model ... | 2015 | 197 |
5,699 | Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci and Philipp Hennig Max Planck Institute for Intelligent Systems Spemannstraße 38, 72076 T¨ubingen, Germany [mmahsereci|phennig]@tue.mpg.de Abstract In deterministic optimization, line searches are a standard tool ensuring stability ... | 2015 | 198 |
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