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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6,000 | The Multi-fidelity Multi-armed Bandit Kirthevasan Kandasamy ♮, Gautam Dasarathy ♦, Jeff Schneider ♮, Barnabás Póczos ♮ ♮Carnegie Mellon University, ♦Rice University {kandasamy, schneide, bapoczos}@cs.cmu.edu, gautamd@rice.edu Abstract We study a variant of the classical stochastic K-armed bandit where ... | 2016 | 105 |
6,001 | Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition Théodore Bluche A2iA SAS 39 rue de la Bienfaisance 75008 Paris tb@a2ia.com Abstract Offline handwriting recognition systems require cropped text line images for both training and recognition. On the one hand, the... | 2016 | 106 |
6,002 | k⇤-Nearest Neighbors: From Global to Local Oren Anava The Voleon Group oren@voleon.com Kfir Y. Levy ETH Zurich yehuda.levy@inf.ethz.ch Abstract The weighted k-nearest neighbors algorithm is one of the most fundamental nonparametric methods in pattern recognition and machine learning. The question of se... | 2016 | 107 |
6,003 | Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images Vladimir Golkov1, Marcin J. Skwark2, Antonij Golkov3, Alexey Dosovitskiy4, Thomas Brox4, Jens Meiler2, and Daniel Cremers1 1 Technical University of Munich, Germany 2 Vanderbilt University, Nashville... | 2016 | 108 |
6,004 | Learnable Visual Markers Oleg Grinchuk1, Vadim Lebedev1,2, and Victor Lempitsky1 1Skolkovo Institute of Science and Technology, Moscow, Russia 2Yandex, Moscow, Russia Abstract We propose a new approach to designing visual markers (analogous to QR-codes, markers for augmented reality, and robotic fiducial tag... | 2016 | 109 |
6,005 | Generating Videos with Scene Dynamics Carl Vondrick MIT vondrick@mit.edu Hamed Pirsiavash UMBC hpirsiav@umbc.edu Antonio Torralba MIT torralba@mit.edu Abstract We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. act... | 2016 | 11 |
6,006 | Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators Shashank Singh Statistics & Machine Learning Departments Carnegie Mellon University sss1@andrew.cmu.edu Barnabás Póczos Machine Learning Departments Carnegie Mellon University bapoczos@cs.cmu.edu Abstract We provide fi... | 2016 | 110 |
6,007 | Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution Christopher W. Lynn Department of Physics and Astronomy University of Pennsylvania chlynn@sas.upenn.edu Daniel D. Lee Department of Electrical and Systems Engineering University of Pennsylvania ddlee@seas.upenn.edu Abstract Infl... | 2016 | 111 |
6,008 | 2016 | 112 | |
6,009 | Adaptive Concentration Inequalities for Sequential Decision Problems Shengjia Zhao Tsinghua University zhaosj12@stanford.edu Enze Zhou Tsinghua University zhouez_thu_12@126.com Ashish Sabharwal Allen Institute for AI AshishS@allenai.org Stefano Ermon Stanford University ermon@cs.stanford.edu ... | 2016 | 113 |
6,010 | Refined Lower Bounds for Adversarial Bandits Sébastien Gerchinovitz Institut de Mathématiques de Toulouse Université Toulouse 3 Paul Sabatier Toulouse, 31062, France sebastien.gerchinovitz@math.univ-toulouse.fr Tor Lattimore Department of Computing Science University of Alberta Edmonton, Canada tor.l... | 2016 | 114 |
6,011 | Structure-Blind Signal Recovery Dmitry Ostrovsky∗Zaid Harchaoui† Anatoli Juditsky∗Arkadi Nemirovski‡ firstname.lastname@imag.fr Abstract We consider the problem of recovering a signal observed in Gaussian noise. If the set of signals is convex and compact, and can be specified beforehand, one can use classic... | 2016 | 115 |
6,012 | Reward Augmented Maximum Likelihood for Neural Structured Prediction Mohammad Norouzi Samy Bengio Zhifeng Chen Navdeep Jaitly Mike Schuster Yonghui Wu Dale Schuurmans {mnorouzi, bengio, zhifengc, ndjaitly}@google.com {schuster, yonghui, schuurmans}@google.com Google Brain Abstract A key proble... | 2016 | 116 |
6,013 | Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning Mehdi Sajjadi Mehran Javanmardi Tolga Tasdizen Department of Electrical and Computer Engineering University of Utah {mehdi, mehran, tolga}@sci.utah.edu Abstract Effective convolutional neural networks ar... | 2016 | 117 |
6,014 | An Online Sequence-to-Sequence Model Using Partial Conditioning Navdeep Jaitly Google Brain ndjaitly@google.com David Sussillo Google Brain sussillo@google.com Quoc V. Le Google Brain qvl@google.com Oriol Vinyals Google DeepMind vinyals@google.com Ilya Sutskever Open AI∗ ilyasu@openai.co... | 2016 | 118 |
6,015 | Interaction Networks for Learning about Objects, Relations and Physics Anonymous Author(s) Affiliation Address email Abstract Reasoning about objects, relations, and physics is central to human intelligence, and 1 a key goal of artificial intelligence. Here we introduce the interaction network, a 2 ... | 2016 | 119 |
6,016 | Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods Yuanzhi Li Department of Computer Science Princeton University Princeton, NJ, 08450 yuanzhil@cs.princeton.edu Andrej Risteski Department of Computer Science Princeton University Princeto... | 2016 | 12 |
6,017 | Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian Victor Picheny MIAT, Université de Toulouse, INRA Castanet-Tolosan, France victor.picheny@toulouse.inra.fr Robert B. Gramacy Virginia Tech Blacksburg, VA, USA rbg@vt.edu Stefan Wild Argonne National Laboratory... | 2016 | 120 |
6,018 | Combinatorial Energy Learning for Image Segmentation Jeremy Maitin-Shepard UC Berkeley Google jbms@google.com Viren Jain Google viren@google.com Michal Januszewski Google mjanusz@google.com Peter Li Google phli@google.com Pieter Abbeel UC Berkeley pabbeel@cs.berkeley.edu Abstract W... | 2016 | 121 |
6,019 | Bayesian Optimization for Probabilistic Programs Tom Rainforth† Tuan Anh Le† Jan-Willem van de Meent‡ Michael A. Osborne† Frank Wood† † Department of Engineering Science, University of Oxford ‡ College of Computer and Information Science, Northeastern University {twgr,tuananh,mosb,fwood}@robots.ox.ac.uk... | 2016 | 122 |
6,020 | Coin Betting and Parameter-Free Online Learning Francesco Orabona Stony Brook University, Stony Brook, NY francesco@orabona.com D´avid P´al Yahoo Research, New York, NY dpal@yahoo-inc.com Abstract In the recent years, a number of parameter-free algorithms have been developed for online linear optimiza... | 2016 | 123 |
6,021 | Learning Deep Embeddings with Histogram Loss Evgeniya Ustinova and Victor Lempitsky Skolkovo Institute of Science and Technology (Skoltech) Moscow, Russia Abstract We suggest a loss for learning deep embeddings. The new loss does not introduce parameters that need to be tuned and results in very good embedd... | 2016 | 124 |
6,022 | An Efficient Streaming Algorithm for the Submodular Cover Problem Ashkan Norouzi-Fard ⇤ ashkan.norouzifard@epfl.ch Abbas Bazzi ⇤ abbas.bazzi@epfl.ch Marwa El Halabi † marwa.elhalabi@epfl.ch Ilija Bogunovic † ilija.bogunovic@epfl.ch Ya-Ping Hsieh † ya-ping.hsieh@epfl.ch Volkan Cevher † volkan.ce... | 2016 | 125 |
6,023 | Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing Farshad Lahouti Electrical Engineering Department, California Institute of Technology lahouti@caltech.edu Babak Hassibi Electrical Engineering Department, California Institute of Technology hassibi@caltech.edu Abstract Digital crow... | 2016 | 126 |
6,024 | Beyond Exchangeability: The Chinese Voting Process Moontae Lee Dept. of Computer Science Cornell University Ithaca, NY 14853 moontae@cs.cornell.edu Seok Hyun Jin Dept. of Computer Science Cornell University Ithaca, NY 14853 sj372@cornell.edu David Mimno Dept. of Information Science Cornell Uni... | 2016 | 127 |
6,025 | Robust Spectral Detection of Global Structures in the Data by Learning a Regularization Pan Zhang Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China panzhang@itp.ac.cn Abstract Spectral methods are popular in detecting global structures in the given data that can be repre... | 2016 | 128 |
6,026 | Optimal spectral transportation with application to music transcription Rémi Flamary Université Côte d’Azur, CNRS, OCA remi.flamary@unice.fr Cédric Févotte CNRS, IRIT, Toulouse cedric.fevotte@irit.fr Nicolas Courty Université de Bretagne Sud, CNRS, IRISA courty@univ-ubs.fr Valentin Emiya Aix-Mar... | 2016 | 129 |
6,027 | Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering Michaël Defferrard Xavier Bresson Pierre Vandergheynst EPFL, Lausanne, Switzerland {michael.defferrard,xavier.bresson,pierre.vandergheynst}@epfl.ch Abstract In this work, we are interested in generalizing convolutional neural... | 2016 | 13 |
6,028 | MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild Grégory Rogez Cordelia Schmid Inria Grenoble Rhône-Alpes, Laboratoire Jean Kuntzmann, France Abstract This paper addresses the problem of 3D human pose estimation in the wild. A significant challenge is the lack of training data, i.e., 2D imag... | 2016 | 130 |
6,029 | A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ Dennis Wei IBM Research Yorktown Heights, NY 10598, USA dwei@us.ibm.com Abstract This paper studies the k-means++ algorithm for clustering as well as the class of Dℓ sampling algorithms to which k-means++ belongs. It is shown that for a... | 2016 | 131 |
6,030 | CNNpack: Packing Convolutional Neural Networks in the Frequency Domain Yunhe Wang1,3, Chang Xu2, Shan You1,3, Dacheng Tao2, Chao Xu1,3 1Key Laboratory of Machine Perception (MOE), School of EECS, Peking University 2Centre for Quantum Computation and Intelligent Systems, School of Software, University of Techn... | 2016 | 132 |
6,031 | Feature-distributed sparse regression: a screen-and-clean approach Jiyan Yang† Michael W. Mahoney‡ Michael A. Saunders† Yuekai Sun§ † Stanford University ‡ University of California at Berkeley § University of Michigan jiyan@stanford.edu mmahoney@stat.berkeley.edu saunders@stanford.edu yuekai@umi... | 2016 | 133 |
6,032 | Generating Images with Perceptual Similarity Metrics based on Deep Networks Alexey Dosovitskiy and Thomas Brox University of Freiburg {dosovits, brox}@cs.uni-freiburg.de Abstract We propose a class of loss functions, which we call deep perceptual similarity metrics (DeePSiM), allowing to generate sharp hi... | 2016 | 134 |
6,033 | Residual Networks Behave Like Ensembles of Relatively Shallow Networks Andreas Veit Michael Wilber Serge Belongie Department of Computer Science & Cornell Tech Cornell University {av443, mjw285, sjb344}@cornell.edu Abstract In this work we propose a novel interpretation of residual networks showing th... | 2016 | 135 |
6,034 | Low-Rank Regression with Tensor Responses Guillaume Rabusseau and Hachem Kadri Aix Marseille Univ, CNRS, LIF, Marseille, France {firstname.lastname}@lif.univ-mrs.fr Abstract This paper proposes an efficient algorithm (HOLRR) to handle regression tasks where the outputs have a tensor structure. We formulate th... | 2016 | 136 |
6,035 | Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent Chi Jin UC Berkeley chijin@cs.berkeley.edu Sham M. Kakade University of Washington sham@cs.washington.edu Praneeth Netrapalli Microsoft Research India praneeth@microsoft.com Abstract Matrix completion, where we... | 2016 | 137 |
6,036 | Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences Chi Jin UC Berkeley chijin@cs.berkeley.edu Yuchen Zhang UC Berkeley yuczhang@berkeley.edu Sivaraman Balakrishnan Carnegie Mellon University siva@stat.cmu.edu Martin J. Wainwright UC Berke... | 2016 | 138 |
6,037 | Diffusion-Convolutional Neural Networks James Atwood and Don Towsley College of Information and Computer Science University of Massachusetts Amherst, MA, 01003 {jatwood|towsley}@cs.umass.edu Abstract We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Throu... | 2016 | 139 |
6,038 | Fast Distributed Submodular Cover: Public-Private Data Summarization Baharan Mirzasoleiman Morteza Zadimoghaddam Amin Karbasi ETH Zurich Google Research Yale University Abstract In this paper, we introduce the public-private framework of data summarization motivated by privacy concerns in personaliz... | 2016 | 14 |
6,039 | Completely random measures for modelling block-structured sparse networks Tue Herlau Mikkel N. Schmidt Morten Mørup DTU Compute Technical University of Denmark Richard Petersens plads 31, 2800 Lyngby, Denmark {tuhe,mns,mmor}@dtu.dk Abstract Statistical methods for network data often parameterize t... | 2016 | 140 |
6,040 | Pruning Random Forests for Prediction on a Budget Feng Nan Systems Engineering Boston University fnan@bu.edu Joseph Wang Electrical Engineering Boston University joewang@bu.edu Venkatesh Saligrama Electrical Engineering Boston University srv@bu.edu Abstract We propose to prune a random fores... | 2016 | 141 |
6,041 | Synthesis of MCMC and Belief Propagation Sungsoo Ahn∗ Michael Chertkov† Jinwoo Shin∗ ∗School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea †1 Theoretical Division, T-4 & Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA,... | 2016 | 142 |
6,042 | Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics Travis Monk Cluster of Excellence Hearing4all University of Oldenburg 26129 Oldenburg, Germany travis.monk@uol.de Cristina Savin IST Austria 3400 Klosterneuburg Austria csavin@ist.ac.at J¨org L¨ucke Cluster of Excel... | 2016 | 143 |
6,043 | Disease Trajectory Maps Peter Schulam Dept. of Computer Science Johns Hopkins University Baltimore, MD 21218 pschulam@cs.jhu.edu Raman Arora Dept. of Computer Science Johns Hopkins University Baltimore, MD 21218 arora@cs.jhu.edu Abstract Medical researchers are coming to appreciate that many dis... | 2016 | 144 |
6,044 | Bayesian optimization for automated model selection Gustavo Malkomes,† Chip Schaff,† Roman Garnett Department of Computer Science and Engineering Washington University in St. Louis St. Louis, MO 63130 {luizgustavo, cbschaff, garnett}@wustl.edu Abstract Despite the success of kernel-based nonparametric met... | 2016 | 145 |
6,045 | Designing smoothing functions for improved worst-case competitive ratio in online optimization Reza Eghbali Department of Electrical Engineering University of Washington Seattle, WA 98195 eghbali@uw.edu Maryam Fazel Department of Electrical Engineering University of Washington Seattle, WA 98195 mf... | 2016 | 146 |
6,046 | Towards Unifying Hamiltonian Monte Carlo and Slice Sampling Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin Duke University Durham, NC, 27708 {yz196,xw56,changyou.chen, ricardo.henao, kf96 , lcarin} @duke.edu Abstract We unify slice sampling and Hamiltonian Monte Carlo (HM... | 2016 | 147 |
6,047 | Multi-step learning and underlying structure in statistical models Maia Fraser Dept. of Mathematics and Statistics Brain and Mind Research Institute University of Ottawa Ottawa, ON K1N 6N5, Canada mfrase8@uottawa.ca Abstract In multi-step learning, where a final learning task is accomplished via a sequ... | 2016 | 148 |
6,048 | The non-convex Burer–Monteiro approach works on smooth semidefinite programs Nicolas Boumal⋆ Department of Mathematics Princeton University nboumal@math.princeton.edu Vladislav Voroninski⋆ Department of Mathematics Massachusetts Institute of Technology vvlad@math.mit.edu Afonso S. Bandeira Departme... | 2016 | 149 |
6,049 | Exponential Family Embeddings Maja Rudolph Columbia University Francisco J. R. Ruiz Univ. of Cambridge Columbia University Stephan Mandt Columbia University David M. Blei Columbia University Abstract Word embeddings are a powerful approach for capturing semantic similarity among terms in a vocab... | 2016 | 15 |
6,050 | Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre Bayen Electrical Engineering and Computer Sciences, UC Berkeley [balandat,walid,tomlin]@eecs.berkeley.edu, bayen@berkeley.edu Abstract We study a g... | 2016 | 150 |
6,051 | Spatiotemporal Residual Networks for Video Action Recognition Christoph Feichtenhofer Graz University of Technology feichtenhofer@tugraz.at Axel Pinz Graz University of Technology axel.pinz@tugraz.at Richard P. Wildes York University, Toronto wildes@cse.yorku.ca Abstract Two-stream Convolutional... | 2016 | 151 |
6,052 | Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes Jack W Rae⇤ jwrae Jonathan J Hunt⇤ jjhunt Tim Harley tharley Ivo Danihelka danihelka Andrew Senior andrewsenior Greg Wayne gregwayne Alex Graves gravesa Timothy P Lillicrap countzero Google DeepMind @google.com ... | 2016 | 152 |
6,053 | Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks Daniel Ritchie Stanford University Anna Thomas Stanford University Pat Hanrahan Stanford University Noah D. Goodman Stanford University Abstract Probabilistic inference algorithms such a... | 2016 | 153 |
6,054 | Reconstructing Parameters of Spreading Models from Partial Observations Andrey Y. Lokhov Center for Nonlinear Studies and Theoretical Division T-4 Los Alamos National Laboratory, Los Alamos, NM 87545, USA lokhov@lanl.gov Abstract Spreading processes are often modelled as a stochastic dynamics occurring on... | 2016 | 154 |
6,055 | Tracking the Best Expert in Non-stationary Stochastic Environments Chen-Yu Wei Yi-Te Hong Chi-Jen Lu Institute of Information Science Academia Sinica, Taiwan {bahh723, ted0504, cjlu}@iis.sinica.edu.tw Abstract We study the dynamic regret of multi-armed bandit and experts problem in nonstationary stoch... | 2016 | 155 |
6,056 | Statistical Inference for Pairwise Graphical Models Using Score Matching Ming Yu mingyu@chicagobooth.edu Varun Gupta varun.gupta@chicagobooth.edu Mladen Kolar⇤ mladen.kolar@chicagobooth.edu University of Chicago Booth School of Business Chicago, IL 60637 Abstract Probabilistic graphical models hav... | 2016 | 156 |
6,057 | Learning Structured Sparsity in Deep Neural Networks Wei Wen University of Pittsburgh wew57@pitt.edu Chunpeng Wu University of Pittsburgh chw127@pitt.edu Yandan Wang University of Pittsburgh yaw46@pitt.edu Yiran Chen University of Pittsburgh yic52@pitt.edu Hai Li University of Pittsburgh ... | 2016 | 157 |
6,058 | Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis Weiran Wang1∗ Jialei Wang2∗ Dan Garber1 Nathan Srebro1 1Toyota Technological Institute at Chicago 2University of Chicago {weiranwang,dgarber,nati}@ttic.edu jialei@uchicago.edu Abstract We study the stochastic o... | 2016 | 158 |
6,059 | How Deep is the Feature Analysis underlying Rapid Visual Categorization? Sven Eberhardt∗ Jonah Cader∗ Thomas Serre Department of Cognitive Linguistic & Psychological Sciences Brown Institute for Brain Sciences Brown University Providence, RI 02818 {sven2,jonah_cader,thomas_serre}@brown.edu Abstract ... | 2016 | 159 |
6,060 | A Non-parametric Learning Method for Confidently Estimating Patient’s Clinical State and Dynamics William Hoiles Department of Electrical Engineering University of California Los Angeles Los Angeles, CA 90024 whoiles@ucla.edu Mihaela van der Schaar Department of Electrical Engineering University of Cal... | 2016 | 16 |
6,061 | Regret of Queueing Bandits Subhashini Krishnasamy University of Texas at Austin Rajat Sen University of Texas at Austin Ramesh Johari Stanford University Sanjay Shakkottai University of Texas at Austin Abstract We consider a variant of the multiarmed bandit problem where jobs queue for service, and ... | 2016 | 160 |
6,062 | Dual Space Gradient Descent for Online Learning Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University, Australia {trung.l, tu.nguyen, v.nguyen, dinh.phung}@deakin.edu.au Abstract One crucial goal in kernel online learning is to bound the model si... | 2016 | 161 |
6,063 | Asynchronous Parallel Greedy Coordinate Descent Yang You ⇧, + XiangRu Lian†, + Ji Liu † Hsiang-Fu Yu ‡ Inderjit S. Dhillon ‡ James Demmel ⇧ Cho-Jui Hsieh ⇤ + equally contributed ⇤University of California, Davis † University of Rochester ‡ University of Texas, Austin ⇧University of California, Be... | 2016 | 162 |
6,064 | Catching heuristics are optimal control policies Boris Belousov*, Gerhard Neumann*, Constantin A. Rothkopf**, Jan Peters* *Department of Computer Science, TU Darmstadt **Cognitive Science Center & Department of Psychology, TU Darmstadt Abstract Two seemingly contradictory theories attempt to explain how human... | 2016 | 163 |
6,065 | Online Pricing with Strategic and Patient Buyers Michal Feldman Tel-Aviv University and MSR Herzliya michal.feldman@cs.tau.ac.il Tomer Koren⇤ Google Brain tkoren@google.com Roi Livni⇤ Princeton University rlivni@cs.princeton.edu Yishay Mansour⇤ Tel-Aviv University mansour@tau.ac.il Aviv Zohar⇤... | 2016 | 164 |
6,066 | Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Jiajun Wu* Chengkai Zhang* Tianfan Xue MIT CSAIL MIT CSAIL MIT CSAIL William T. Freeman Joshua B. Tenenbaum MIT CSAIL, Google Research MIT CSAIL Abstract We study the problem of 3D object generation. ... | 2016 | 165 |
6,067 | Optimistic Gittins Indices Eli Gutin Operations Research Center, MIT Cambridge, MA 02142 gutin@mit.edu Vivek F. Farias MIT Sloan School of Management Cambridge, MA 02142 vivekf@mit.edu Abstract Starting with the Thomspon sampling algorithm, recent years have seen a resurgence of interest in Bayesian... | 2016 | 166 |
6,068 | Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences Hongseok Namkoong Stanford University hnamk@stanford.edu John C. Duchi Stanford University jduchi@stanford.edu Abstract We develop efficient solution methods for a robust empirical risk minimization problem design... | 2016 | 167 |
6,069 | Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation Weihao Gao∗, Sewoong Oh†, and Pramod Viswanath∗ University of Illinois at Urbana-Champaign Urbana, IL 61801 {wgao9,swoh,pramodv}@illinois.edu Abstract Estimators of information theoretic measures such as entropy and mutual infor... | 2016 | 168 |
6,070 | Domain Separation Networks Konstantinos Bousmalis∗ Google Brain Mountain View, CA konstantinos@google.com George Trigeorgis∗† Imperial College London London, UK g.trigeorgis@imperial.ac.uk Nathan Silberman Google Research New York, NY nsilberman@google.com Dilip Krishnan Google Research Ca... | 2016 | 169 |
6,071 | Integrated Perception with Recurrent Multi-Task Neural Networks Hakan Bilen Andrea Vedaldi Visual Geometry Group, University of Oxford {hbilen,vedaldi}@robots.ox.ac.uk Abstract Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classificat... | 2016 | 17 |
6,072 | A Probabilistic Programming Approach To Probabilistic Data Analysis Feras Saad MIT Probabilistic Computing Project fsaad@mit.edu Vikash Mansinghka MIT Probabilistic Computing Project vkm@mit.edu Abstract Probabilistic techniques are central to data analysis, but different approaches can be challengi... | 2016 | 170 |
6,073 | Assortment Optimization Under the Mallows model Antoine Désir IEOR Department Columbia University antoine@ieor.columbia.edu Vineet Goyal IEOR Department Columbia University vgoyal@ieor.columbia.edu Srikanth Jagabathula IOMS Department NYU Stern School of Business sjagabat@stern.nyu.edu Danny S... | 2016 | 171 |
6,074 | An algorithm for ℓ1 nearest neighbor search via monotonic embedding Xinan Wang∗ UC San Diego xinan@ucsd.edu Sanjoy Dasgupta UC San Diego dasgupta@cs.ucsd.edu Abstract Fast algorithms for nearest neighbor (NN) search have in large part focused on ℓ2 distance. Here we develop an approach for ℓ1 distan... | 2016 | 172 |
6,075 | Multi-armed Bandits: Competing with Optimal Sequences Oren Anava The Voleon Group Berkeley, CA oren@voleon.com Zohar Karnin Yahoo! Research New York, NY zkarnin@yahoo-inc.com Abstract We consider sequential decision making problem in the adversarial setting, where regret is measured with respect... | 2016 | 173 |
6,076 | NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization Davood Hajinezhad, Mingyi Hong ∗ Tuo Zhao† Zhaoran Wang‡ Abstract We study a stochastic and distributed algorithm for nonconvex problems whose objective consists of a sum of N nonconvex Li/N-smooth functions, plus ... | 2016 | 174 |
6,077 | Probing the Compositionality of Intuitive Functions Eric Schulz University College London e.schulz@cs.ucl.ac.uk Joshua B. Tenenbaum MIT jbt@mit.edu David Duvenaud University of Toronto duvenaud@cs.toronto.edu Maarten Speekenbrink University College London m.speekenbrink@ucl.ac.uk Samuel J. Ger... | 2016 | 175 |
6,078 | Identification and Overidentification of Linear Structural Equation Models Bryant Chen University of California, Los Angeles Computer Science Department Los Angeles, CA, 90095-1596, USA Abstract In this paper, we address the problems of identifying linear structural equation models and discovering the con... | 2016 | 176 |
6,079 | An Architecture for Deep, Hierarchical Generative Models Philip Bachman phil.bachman@maluuba.com Maluuba Research Abstract We present an architecture which lets us train deep, directed generative models with many layers of latent variables. We include deterministic paths between all latent variables and... | 2016 | 177 |
6,080 | Towards Conceptual Compression Karol Gregor Google DeepMind karolg@google.com Frederic Besse Google DeepMind fbesse@google.com Danilo Jimenez Rezende Google DeepMind danilor@google.com Ivo Danihelka Google DeepMind danihelka@google.com Daan Wierstra Google DeepMind wierstra@google.com Ab... | 2016 | 178 |
6,081 | Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters∗ Zeyuan Allen-Zhu† Princeton University / IAS zeyuan@csail.mit.edu Yang Yuan† Cornell University yangyuan@cs.cornell.edu Karthik Sridharan Cornell University sridharan@cs.cornell.edu Abstract The amount of data available in... | 2016 | 179 |
6,082 | Dialog-based Language Learning Jason Weston Facebook AI Research, New York. jase@fb.com Abstract A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with su... | 2016 | 18 |
6,083 | Consistent Kernel Mean Estimation for Functions of Random Variables Carl-Johann Simon-Gabriel⇤, Adam ´Scibior⇤,†, Ilya Tolstikhin, Bernhard Schölkopf Department of Empirical Inference, Max Planck Institute for Intelligent Systems Spemanstraße 38, 72076 Tübingen, Germany ⇤joint first authors; † also with: Engin... | 2016 | 180 |
6,084 | Hierarchical Clustering via Spreading Metrics Aurko Roy1 and Sebastian Pokutta2 1College of Computing, Georgia Institute of Technology, Atlanta, GA, USA. Email: aurko@gatech.edu 2ISyE, Georgia Institute of Technology, Atlanta, GA, USA. Email: sebastian.pokutta@isye.gatech.edu Abstract We study the cost fu... | 2016 | 181 |
6,085 | Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation Jianxu Chen University of Notre Dame jchen16@nd.edu Lin Yang University of Notre Dame lyang5@nd.edu Yizhe Zhang University of Notre Dame yzhang29@nd.edu Mark Alber University of Notre Dame malber... | 2016 | 182 |
6,086 | SDP Relaxation with Randomized Rounding for Energy Disaggregation Kiarash Shaloudegi Imperial College London k.shaloudegi16@imperial.ac.uk András György Imperial College London a.gyorgy@imperial.ac.uk Csaba Szepesvári University of Alberta szepesva@ualberta.ca Wilsun Xu University of Alberta w... | 2016 | 183 |
6,087 | Finite Sample Prediction and Recovery Bounds for Ordinal Embedding Lalit Jain University of Michigan Ann Arbor, MI 48109 lalitj@umich.edu Kevin Jamieson University of California, Berkeley Berkeley, CA 94720 kjamieson@berkeley.edu Robert Nowak University of Wisconsin Madison, WI 53706 rdnowak@w... | 2016 | 184 |
6,088 | Search Improves Label for Active Learning Alina Beygelzimer Yahoo Research New York, NY beygel@yahoo-inc.com Daniel Hsu Columbia University New York, NY djhsu@cs.columbia.edu John Langford Microsoft Research New York, NY jcl@microsoft.com Chicheng Zhang UC San Diego La Jolla, CA chz038@c... | 2016 | 185 |
6,089 | A Simple Practical Accelerated Method for Finite Sums Aaron Defazio Ambiata, Sydney Australia Abstract We describe a novel optimization method for finite sums (such as empirical risk minimization problems) building on the recently introduced SAGA method. Our method achieves an accelerated convergence rate ... | 2016 | 186 |
6,090 | Coupled Generative Adversarial Networks Ming-Yu Liu Mitsubishi Electric Research Labs (MERL), mliu@merl.com Oncel Tuzel Mitsubishi Electric Research Labs (MERL), oncel@merl.com Abstract We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. ... | 2016 | 187 |
6,091 | Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels Ilya Tolstikhin Department of Empirical Inference MPI for Intelligent Systems Tübingen 72076, Germany ilya@tuebingen.mpg.de Bharath K. Sriperumbudur Department of Statistics Pennsylvania State University University Park, PA 16802, US... | 2016 | 188 |
6,092 | Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model Zhen Xu, Wen Dong, and Sargur Srihari Department of Computer Science and Engineering University at Buffalo {zxu8,wendong,srihari}@buffalo.edu Abstract Social dynamics is concerned primarily with int... | 2016 | 189 |
6,093 | A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Yarin Gal University of Cambridge {yg279,zg201}@cam.ac.uk Zoubin Ghahramani Abstract Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is... | 2016 | 19 |
6,094 | Multiple-Play Bandits in the Position-Based Model Paul Lagrée∗ LRI, Université Paris Sud Université Paris Saclay paul.lagree@u-psud.fr Claire Vernade∗ LTCI, CNRS, Télécom ParisTech Université Paris Saclay vernade@enst.fr Olivier Cappé LTCI, CNRS Télécom ParisTech Université Paris Saclay Abstra... | 2016 | 190 |
6,095 | Learning values across many orders of magnitude Hado van Hasselt Arthur Guez Matteo Hessel Google DeepMind Volodymyr Mnih David Silver Abstract Most learning algorithms are not invariant to the scale of the signal that is being approximated. We propose to adaptively normalize the targets used in the l... | 2016 | 191 |
6,096 | Attend, Infer, Repeat: Fast Scene Understanding with Generative Models S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton {aeslami,heess,theophane,tassa,dsz,korayk,geoffhinton}@google.com Google DeepMind, London, UK Abstract We present a ... | 2016 | 192 |
6,097 | Supervised Learning with Tensor Networks E. M. Stoudenmire Perimeter Institute for Theoretical Physics Waterloo, Ontario, N2L 2Y5, Canada David J. Schwab Department of Physics Northwestern University, Evanston, IL Abstract Tensor networks are approximations of high-order tensors which are efficient to ... | 2016 | 193 |
6,098 | Structured Prediction Theory Based on Factor Graph Complexity Corinna Cortes Google Research New York, NY 10011 corinna@google.com Vitaly Kuznetsov Google Research New York, NY 10011 vitaly@cims.nyu.edu Mehryar Mohrii Courant Institute and Google New York, NY 10012 mohri@cims.nyu.edu Scott Y... | 2016 | 194 |
6,099 | The Multiple Quantile Graphical Model Alnur Ali Machine Learning Department Carnegie Mellon University alnurali@cmu.edu J. Zico Kolter Computer Science Department Carnegie Mellon University zkolter@cs.cmu.edu Ryan J. Tibshirani Department of Statistics Carnegie Mellon University ryantibs@cmu.edu... | 2016 | 195 |
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