NeurIPS 2013

360 papers

Reasoning With Neural Tensor Networks for Knowledge Base Completion

Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Ng

Designed Measurements for Vector Count Data

Liming Wang, David E Carlson, Miguel Rodrigues, David Wilcox, Robert Calderbank

Flexible sampling of discrete data correlations without the marginal distributions

Alfredo Kalaitzis, Ricardo Silva

EDML for Learning Parameters in Directed and Undirected Graphical Models

Khaled S Refaat, Arthur Choi, Adnan Darwiche

Nonparametric Multi-group Membership Model for Dynamic Networks

Myunghwan Kim, Jure Leskovec

Sinkhorn Distances: Lightspeed Computation of Optimal Transport

Marco Cuturi

Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators.

Michel Besserve, Nikos K. Logothetis, Bernhard Schölkopf

Optimistic Concurrency Control for Distributed Unsupervised Learning

Xinghao Pan, Joseph E Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan

Learning invariant representations and applications to face verification

Qianli Liao, Joel Z. Leibo, Tomaso Poggio

Multisensory Encoding, Decoding, and Identification

Aurel A. Lazar, Yevgeniy Slutskiy

Accelerating Stochastic Gradient Descent using Predictive Variance Reduction

Rie Johnson, Tong Zhang

Thompson Sampling for 1-Dimensional Exponential Family Bandits

Nathaniel Korda, Emilie Kaufmann, Rémi Munos

Sparse Inverse Covariance Estimation with Calibration

Tuo Zhao, Han Liu

The Randomized Dependence Coefficient

David Lopez-paz, Philipp Hennig, Bernhard Schölkopf

Convex Two-Layer Modeling

Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans

PAC-Bayes-Empirical-Bernstein Inequality

Ilya O Tolstikhin, Yevgeny Seldin

A Latent Source Model for Nonparametric Time Series Classification

George H Chen, Stanislav Nikolov, Devavrat Shah

Spectral methods for neural characterization using generalized quadratic models

ll Memming Park, Evan W Archer, Nicholas Priebe, Jonathan W Pillow

Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions

Ari Pakman, Liam Paninski

Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses

Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari

Moment-based Uniform Deviation Bounds for k-means and Friends

Sanjoy Dasgupta, Matus J. Telgarsky, Matus J Telgarsky

B-test: A Non-parametric, Low Variance Kernel Two-sample Test

Wojciech Zaremba, Arthur Gretton, Matthew Blaschko

Bayesian optimization explains human active search

Ali Borji, Laurent Itti

Scalable kernels for graphs with continuous attributes

Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt

Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition

Adel Javanmard, Andrea Montanari

Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints

Rishabh K Iyer, Jeff A. Bilmes

Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis

Nikhil Rao, Christopher Cox, Rob Nowak, Timothy T. Rogers

Small-Variance Asymptotics for Hidden Markov Models

Anirban Roychowdhury, Ke Jiang, Brian Kulis

Which Space Partitioning Tree to Use for Search?

Parikshit Ram, Alexander Gray

From Bandits to Experts: A Tale of Domination and Independence

Noga Alon, Nicolò Cesa-bianchi, Claudio Gentile, Yishay Mansour

Online Learning with Switching Costs and Other Adaptive Adversaries

Nicolò Cesa-bianchi, Ofer Dekel, Ohad Shamir

Machine Teaching for Bayesian Learners in the Exponential Family

Xiaojin Zhu

Discriminative Transfer Learning with Tree-based Priors

Nitish Srivastava, Ruslan Salakhutdinov

Learning Multiple Models via Regularized Weighting

Daniel Vainsencher, Shie Mannor, Huan Xu

Distributed Representations of Words and Phrases and their Compositionality

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, Jeff Dean

Online PCA for Contaminated Data

Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan

Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream

Daniel L Yamins, Ha Hong, Charles Cadieu, James J DiCarlo

Latent Maximum Margin Clustering

Guang-Tong Zhou, Tian Lan, Arash Vahdat, Greg Mori

Computing the Stationary Distribution Locally

Christina E. Lee, Asuman Ozdaglar, Devavrat Shah

Multilinear Dynamical Systems for Tensor Time Series

Mark Rogers, Lei Li, Stuart Russell

Learning Prices for Repeated Auctions with Strategic Buyers

Kareem Amin, Afshin Rostamizadeh, Umar Syed

Adaptive Market Making via Online Learning

Jacob Abernethy, Satyen Kale

Optimizing Instructional Policies

Robert Lindsey, Michael Mozer, William J Huggins, Harold Pashler

Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model

Fang Han, Han Liu

Documents as multiple overlapping windows into grids of counts

Alessandro Perina, Nebojsa Jojic, Manuele Bicego, Andrzej Truski

On Decomposing the Proximal Map

Yaoliang Yu, Yao-Liang Yu

Variance Reduction for Stochastic Gradient Optimization

Chong Wang, Xi Chen, Alexander J Smola, Eric P. Xing, Eric P Xing

Sketching Structured Matrices for Faster Nonlinear Regression

Haim Avron, Vikas Sindhwani, David Woodruff

Marginals-to-Models Reducibility

Tim Roughgarden, Michael Kearns

Direct 0-1 Loss Minimization and Margin Maximization with Boosting

Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang

Real-Time Inference for a Gamma Process Model of Neural Spiking

David E Carlson, Vinayak Rao, Joshua T Vogelstein, Lawrence Carin

Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space

Xinhua Zhang, Wee Sun Lee, Yee Whye Teh

Polar Operators for Structured Sparse Estimation

Xinhua Zhang, Dale Schuurmans, Yaoliang Yu, Yao-Liang Yu

Top-Down Regularization of Deep Belief Networks

Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-Hwee Lim

More data speeds up training time in learning halfspaces over sparse vectors

Amit Daniely, Nati Linial, Shai Shalev-shwartz

Scalable Influence Estimation in Continuous-Time Diffusion Networks

Nan Du, Le Song, Manuel Gomez Rodriguez, Hongyuan Zha

Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms

Yu Zhang

Compete to Compute

Rupesh K Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Jürgen Schmidhuber

Online Robust PCA via Stochastic Optimization

Jiashi Feng, Huan Xu, Shuicheng Yan

Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation

Vibhav Vineet, Carsten Rother, Philip Torr

Generalizing Analytic Shrinkage for Arbitrary Covariance Structures

Daniel Bartz, Klaus-Robert Müller

High-Dimensional Gaussian Process Bandits

Josip Djolonga, Andreas Krause, Volkan Cevher

A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables

Jing Xiang, Seyoung Kim

Exact and Stable Recovery of Pairwise Interaction Tensors

Shouyuan Chen, Michael R Lyu, Irwin King, Zenglin Xu

Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation

Dahua Lin

Dirty Statistical Models

Eunho Yang, Pradeep K Ravikumar

Multiscale Dictionary Learning for Estimating Conditional Distributions

Francesca Petralia, Joshua T Vogelstein, David B. Dunson, David B Dunson

Lasso Screening Rules via Dual Polytope Projection

Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye

The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited

Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram

Sensor Selection in High-Dimensional Gaussian Trees with Nuisances

Daniel S Levine, Jonathan P How

A Deep Architecture for Matching Short Texts

Zhengdong Lu, Hang Li

Matrix Completion From any Given Set of Observations

Troy Lee, Adi Shraibman

Similarity Component Analysis

Soravit Changpinyo, Kuan Liu, Fei Sha

Bellman Error Based Feature Generation using Random Projections on Sparse Spaces

Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand, Joelle Pineau, Doina Precup

Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic

James L Sharpnack, Akshay Krishnamurthy, Aarti Singh

Analyzing the Harmonic Structure in Graph-Based Learning

Xiao-ming Wu, Zhenguo Li, Shih-fu Chang

Distributed Submodular Maximization: Identifying Representative Elements in Massive Data

Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause

Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search

Aijun Bai, Feng Wu, Xiaoping Chen

The Power of Asymmetry in Binary Hashing

Behnam Neyshabur, Nati Srebro, Ruslan Salakhutdinov, Yury Makarychev, Payman Yadollahpour

Statistical Active Learning Algorithms

Maria-Florina F Balcan, Vitaly Feldman

Adaptive Anonymity via b-Matching

Krzysztof M Choromanski, Tony Jebara, Kui Tang

Variational Planning for Graph-based MDPs

Qiang Cheng, Qiang Liu, Feng Chen, Alex Ihler

Brian McWilliams, David Balduzzi, Joachim M Buhmann

Sparse Additive Text Models with Low Rank Background

Lei Shi

Fast Template Evaluation with Vector Quantization

Mohammad Amin Sadeghi, David Forsyth

Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA

Vincent Q. Vu, Juhee Cho, Jing Lei, Karl Rohe

q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions

Assaf Glazer, Michael Lindenbaum, Shaul Markovitch

Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions

Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi Jaakkola

Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising

Min Xu, Tao Qin, Tie-yan Liu

Predicting Parameters in Deep Learning

Misha Denil, Babak Shakibi, Laurent Dinh, Marc'aurelio Ranzato, Nando de Freitas

Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)

Francis Bach, Eric Moulines

Distributed k-means and k-median Clustering on General Topologies

Maria-Florina F Balcan, Steven Ehrlich, Yingyu Liang

Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs

Yann Dauphin, Yoshua Bengio

Learning Stochastic Inverses

Andreas Stuhlmüller, Jacob Taylor, Noah Goodman

Data-driven Distributionally Robust Polynomial Optimization

Martin Mevissen, Emanuele Ragnoli, Jia Yuan Yu

A memory frontier for complex synapses

Subhaneil Lahiri, Surya Ganguli

Least Informative Dimensions

Fabian Sinz, Anna Stockl, Jan Grewe, Jan Benda

Symbolic Opportunistic Policy Iteration for Factored-Action MDPs

Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli

DeViSE: A Deep Visual-Semantic Embedding Model

Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean

Robust Spatial Filtering with Beta Divergence

Wojciech Samek, Duncan Blythe, Klaus-Robert Müller, Motoaki Kawanabe