NeurIPS 2014
411 papers
(Almost) No Label No Cry
Giorgio Patrini, Richard Nock, Paul Rivera, Tiberio Caetano A Complete Variational Tracker
Ryan D Turner, Steven Bottone, Bhargav Avasarala A Safe Screening Rule for Sparse Logistic Regression
Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye A Wild Bootstrap for Degenerate Kernel Tests
Kacper P Chwialkowski, Dino Sejdinovic, Arthur Gretton A* Sampling
Chris J Maddison, Daniel Tarlow, Tom Minka Active Learning and Best-Response Dynamics
Maria-Florina F Balcan, Christopher Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, Le Song Algorithm Selection by Rational Metareasoning as a Model of Human Strategy Selection
Falk Lieder, Dillon Plunkett, Jessica B Hamrick, Stuart Russell, Nicholas Hay, Tom Griffiths An Autoencoder Approach to Learning Bilingual Word Representations
A P Sarath Chandar, Stanislas Lauly, Hugo Larochelle, Mitesh Khapra, Balaraman Ravindran, Vikas C. Raykar, Amrita Saha Analysis of Brain States from Multi-Region LFP Time-Series
Kyle R Ulrich, David E Carlson, Wenzhao Lian, Jana S Borg, Kafui Dzirasa, Lawrence Carin Approximating Hierarchical MV-Sets for Hierarchical Clustering
Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch Asynchronous Anytime Sequential Monte Carlo
Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh Augur: Data-Parallel Probabilistic Modeling
Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam C Pocock, Stephen Green, Guy L Steele Bayesian Sampling Using Stochastic Gradient Thermostats
Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D Skeel, Hartmut Neven Best-Arm Identification in Linear Bandits
Marta Soare, Alessandro Lazaric, Remi Munos Clustered Factor Analysis of Multineuronal Spike Data
Lars Buesing, Timothy A Machado, John P. Cunningham, Liam Paninski Combinatorial Pure Exploration of Multi-Armed Bandits
Shouyuan Chen, Tian Lin, Irwin King, Michael R Lyu, Wei Chen Communication-Efficient Distributed Dual Coordinate Ascent
Martin Jaggi, Virginia Smith, Martin Takac, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I Jordan Cone-Constrained Principal Component Analysis
Yash Deshpande, Andrea Montanari, Emile Richard Consistent Binary Classification with Generalized Performance Metrics
Oluwasanmi O Koyejo, Nagarajan Natarajan, Pradeep K Ravikumar, Inderjit S Dhillon Convex Deep Learning via Normalized Kernels
Özlem Aslan, Xinhua Zhang, Dale Schuurmans Convolutional Kernel Networks
Julien Mairal, Piotr Koniusz, Zaid Harchaoui, Cordelia Schmid Coresets for K-Segmentation of Streaming Data
Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher Iii, Daniela Rus Decomposing Parameter Estimation Problems
Khaled S Refaat, Arthur Choi, Adnan Darwiche Deep Joint Task Learning for Generic Object Extraction
Xiaolong Wang, Liliang Zhang, Liang Lin, Zhujin Liang, Wangmeng Zuo Deep Symmetry Networks
Robert Gens, Pedro M Domingos Dependent Nonparametric Trees for Dynamic Hierarchical Clustering
Kumar Avinava Dubey, Qirong Ho, Sinead A Williamson, Eric P Xing Design Principles of the Hippocampal Cognitive mAP
Kimberly L Stachenfeld, Matthew Botvinick, Samuel J Gershman Discrete Graph Hashing
Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang Distributed Balanced Clustering via Mapping Coresets
Mohammadhossein Bateni, Aditya Bhaskara, Silvio Lattanzi, Vahab Mirrokni Distributed Bayesian Posterior Sampling via Moment Sharing
Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang Diverse Randomized Agents Vote to Win
Albert Jiang, Leandro Soriano Marcolino, Ariel D Procaccia, Tuomas Sandholm, Nisarg Shah, Milind Tambe Do Convnets Learn Correspondence?
Jonathan L Long, Ning Zhang, Trevor Darrell Dynamic Rank Factor Model for Text Streams
Shaobo Han, Lin Du, Esther Salazar, Lawrence Carin Efficient Optimization for Average Precision SVM
Pritish Mohapatra, C.V. Jawahar, M. Pawan Kumar Efficient Partial Monitoring with Prior Information
Hastagiri P Vanchinathan, Gábor Bartók, Andreas Krause Efficient Structured Matrix Rank Minimization
Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime Carbonell, Suvrit Sra Elementary Estimators for Graphical Models
Eunho Yang, Aurelie C. Lozano, Pradeep K Ravikumar Estimation with Norm Regularization
Arindam Banerjee, Sheng Chen, Farideh Fazayeli, Vidyashankar Sivakumar Exploiting Easy Data in Online Optimization
Amir Sani, Gergely Neu, Alessandro Lazaric Extracting Latent Structure from Multiple Interacting Neural Populations
Joao Semedo, Amin Zandvakili, Adam Kohn, Christian K. Machens, Byron M. Yu Extreme Bandits
Alexandra Carpentier, Michal Valko Fast and Robust Least Squares Estimation in Corrupted Linear Models
Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M Buhmann Fast Kernel Learning for Multidimensional Pattern Extrapolation
Andrew G Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham Fast Training of Pose Detectors in the Fourier Domain
João F. Henriques, Pedro Martins, Rui F Caseiro, Jorge Batista Feedback Detection for Live Predictors
Stefan Wager, Nick Chamandy, Omkar Muralidharan, Amir Najmi Gaussian Process Volatility Model
Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani Generalized Unsupervised Manifold Alignment
Zhen Cui, Hong Chang, Shiguang Shan, Xilin Chen Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio Global Belief Recursive Neural Networks
Romain Paulus, Richard Socher, Christopher D. Manning Greedy Subspace Clustering
Dohyung Park, Constantine Caramanis, Sujay Sanghavi How Transferable Are Features in Deep Neural Networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson Identifying and Attacking the Saddle Point Problem in High-Dimensional Non-Convex Optimization
Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio Improved Distributed Principal Component Analysis
Yingyu Liang, Maria-Florina F Balcan, Vandana Kanchanapally, David Woodruff Incremental Local Gaussian Regression
Franziska Meier, Philipp Hennig, Stefan Schaal Just-in-Time Learning for Fast and Flexible Inference
S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn Kernel Mean Estimation via Spectral Filtering
Krikamol Muandet, Bharath Sriperumbudur, Bernhard Schölkopf Large-Margin Convex Polytope Machine
Alex Kantchelian, Michael C Tschantz, Ling Huang, Peter L Bartlett, Anthony D Joseph, J. D. Tygar Large-Scale L-BFGS Using MapReduce
Weizhu Chen, Zhenghao Wang, Jingren Zhou Learning a Concept Hierarchy from Multi-Labeled Documents
Viet-An Nguyen, Jordan L Ying, Philip Resnik, Jonathan Chang Learning Deep Features for Scene Recognition Using Places Database
Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva Learning Generative Models with Visual Attention
Charlie Tang, Nitish Srivastava, Ruslan Salakhutdinov Learning Mixtures of Ranking Models
Pranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan Learning Time-Varying Coverage Functions
Nan Du, Yingyu Liang, Maria-Florina F Balcan, Le Song Learning with Fredholm Kernels
Qichao Que, Mikhail Belkin, Yusu Wang Learning with Pseudo-Ensembles
Philip Bachman, Ouais Alsharif, Doina Precup Low-Rank Time-Frequency Synthesis
Cédric Févotte, Matthieu Kowalski LSDA: Large Scale Detection Through Adaptation
Judy Hoffman, Sergio Guadarrama, Eric S Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, Kate Saenko Magnitude-Sensitive Preference Formation`
Nisheeth Srivastava, Ed Vul, Paul R. Schrater Metric Learning for Temporal Sequence Alignment
Damien Garreau, Rémi Lajugie, Sylvain Arlot, Francis Bach Mind the Nuisance: Gaussian Process Classification Using Privileged Noise
Daniel Hernández-lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto Mondrian Forests: Efficient Online Random Forests
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh Multi-Class Deep Boosting
Vitaly Kuznetsov, Mehryar Mohri, Umar Syed Multi-Scale Spectral Decomposition of Massive Graphs
Si Si, Donghyuk Shin, Inderjit S Dhillon, Beresford N Parlett Multiscale Fields of Patterns
Pedro Felzenszwalb, John G Oberlin Near-Optimal Sample Compression for Nearest Neighbors
Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures
Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya, Ashkan Jafarpour New Rules for Domain Independent Lifted MAP Inference
Happy Mittal, Prasoon Goyal, Vibhav G Gogate, Parag Singla Non-Convex Robust PCA
Praneeth Netrapalli, U N Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain On Integrated Clustering and Outlier Detection
Lionel Ott, Linsey Pang, Fabio T Ramos, Sanjay Chawla On Multiplicative Multitask Feature Learning
Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun On the Information Theoretic Limits of Learning Ising Models
Rashish Tandon, Karthikeyan Shanmugam, Pradeep K Ravikumar, Alexandros G Dimakis On the Number of Linear Regions of Deep Neural Networks
Guido F. Montufar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio On the Relations of LFPs & Neural Spike Trains
David E Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin Optimal Teaching for Limited-Capacity Human Learners
Kaustubh R Patil, Xiaojin Zhu, Łukasz Kopeć, Bradley C. Love Optimizing F-Measures by Cost-Sensitive Classification
Shameem Puthiya Parambath, Nicolas Usunier, Yves Grandvalet Orbit Regularization
Renato Negrinho, Andre Martins PAC-Bayesian AUC Classification and Scoring
James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang Parallel Direction Method of Multipliers
Huahua Wang, Arindam Banerjee, Zhi-Quan Luo Parallel Double Greedy Submodular Maximization
Xinghao Pan, Stefanie Jegelka, Joseph E Gonzalez, Joseph K. Bradley, Michael I Jordan Parallel Feature Selection Inspired by Group Testing
Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q Ngo, Xuanlong Nguyen, Christopher Ré, Venu Govindaraju Partition-Wise Linear Models
Hidekazu Oiwa, Ryohei Fujimaki Positive Curvature and Hamiltonian Monte Carlo
Christof Seiler, Simon Rubinstein-Salzedo, Susan Holmes Probabilistic ODE Solvers with Runge-Kutta Means
Michael Schober, David K. Duvenaud, Philipp Hennig Quantized Kernel Learning for Feature Matching
Danfeng Qin, Xuanli Chen, Matthieu Guillaumin, Luc V. Gool QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models
Cho-Jui Hsieh, Inderjit S Dhillon, Pradeep K Ravikumar, Stephen Becker, Peder A. Olsen Ranking via Robust Binary Classification
Hyokun Yun, Parameswaran Raman, S. Vishwanathan Recurrent Models of Visual Attention
Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu Reputation-Based Worker Filtering in Crowdsourcing
Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman Robust Bayesian Max-Margin Clustering
Changyou Chen, Jun Zhu, Xinhua Zhang Robust Logistic Regression and Classification
Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
Tom Gunter, Michael A Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts Scalable Kernel Methods via Doubly Stochastic Gradients
Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina F Balcan, Le Song Scalable Non-Linear Learning with Adaptive Polynomial Expansions
Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus J Telgarsky Self-Adaptable Templates for Feature Coding
Xavier Boix, Gemma Roig, Salomon Diether, Luc V. Gool Self-Paced Learning with Diversity
Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, Alexander Hauptmann Semi-Supervised Learning with Deep Generative Models
Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling Sequential Monte Carlo for Graphical Models
Christian Andersson Naesseth, Fredrik Lindsten, Thomas B Schön SerialRank: Spectral Ranking Using Seriation
Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic Shaping Social Activity by Incentivizing Users
Mehrdad Farajtabar, Nan Du, Manuel Gomez Rodriguez, Isabel Valera, Hongyuan Zha, Le Song Simple MAP Inference via Low-Rank Relaxations
Roy Frostig, Sida Wang, Percy Liang, Christopher D. Manning Sparse Multi-Task Reinforcement Learning
Daniele Calandriello, Alessandro Lazaric, Marcello Restelli Sparse PCA with Oracle Property
Quanquan Gu, Zhaoran Wang, Han Liu Sparse Polynomial Learning and Graph Sketching
Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G Dimakis, Adam Klivans Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space
Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep K Ravikumar, Inderjit S Dhillon Spectral K-Support Norm Regularization
Andrew M McDonald, Massimiliano Pontil, Dimitris Stamos Stochastic Network Design in Bidirected Trees
Xiaojian Wu, Daniel R. Sheldon, Shlomo Zilberstein Submodular Attribute Selection for Action Recognition in Video
Jingjing Zheng, Zhuolin Jiang, Rama Chellappa, Jonathon P Phillips Tight Continuous Relaxation of the Balanced K-Cut Problem
Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein Tight Convex Relaxations for Sparse Matrix Factorization
Emile Richard, Guillaume R. Obozinski, Jean-Philippe Vert Time--Data Tradeoffs by Aggressive Smoothing
John J Bruer, Joel A Tropp, Volkan Cevher, Stephen Becker Universal Option Models
Hengshuai Yao, Csaba Szepesvari, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar Unsupervised Transcription of Piano Music
Taylor Berg-Kirkpatrick, Jacob Andreas, Dan Klein Variational Gaussian Process State-Space Models
Roger Frigola, Yutian Chen, Carl Edward Rasmussen Weakly-Supervised Discovery of Visual Pattern Configurations
Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell