Book

Advances in Neural Information Processing Systems 27 (NIPS 2014)

Edited by: Z. Ghahramani and M. Welling and C. Cortes and N. Lawrence and K.Q. Weinberger

Two-Stream Convolutional Networks for Action Recognition in Videos Karen Simonyan, Andrew Zisserman
Exploiting easy data in online optimization Amir Sani, Gergely Neu, Alessandro Lazaric
Optimal prior-dependent neural population codes under shared input noise Agnieszka Grabska-Barwinska, Jonathan W. Pillow
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
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification Yingzhen Yang, Feng Liang, Shuicheng Yan, Zhangyang Wang, Thomas S. Huang
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights Daniel Soudry, Itay Hubara, Ron Meir
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices Jianbo Yang, Xuejun Liao, Minhua Chen, Lawrence Carin
Recovery of Coherent Data via Low-Rank Dictionary Pursuit Guangcan Liu, Ping Li
Learning to Discover Efficient Mathematical Identities Wojciech Zaremba, Karol Kurach, Rob Fergus
Global Sensitivity Analysis for MAP Inference in Graphical Models Jasper De Bock, Cassio P. de Campos, Alessandro Antonucci
Recurrent Models of Visual Attention Volodymyr Mnih, Nicolas Heess, Alex Graves, koray kavukcuoglu
LSDA: Large Scale Detection through Adaptation Judy Hoffman, Sergio Guadarrama, Eric S. Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, Kate Saenko
Analog Memories in a Balanced Rate-Based Network of E-I Neurons Dylan Festa, Guillaume Hennequin, Mate Lengyel
Efficient Partial Monitoring with Prior Information Hastagiri P. Vanchinathan, Gábor Bartók, Andreas Krause
Near-optimal Reinforcement Learning in Factored MDPs Ian Osband, Benjamin Van Roy
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space Robert A. Vandermeulen, Clayton Scott
Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities Tianbao Yang, Rong Jin
Diverse Sequential Subset Selection for Supervised Video Summarization Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei Sha
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning Francesco Orabona
On the Number of Linear Regions of Deep Neural Networks Guido F. Montufar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio
Model-based Reinforcement Learning and the Eluder Dimension Ian Osband, Benjamin Van Roy
A Wild Bootstrap for Degenerate Kernel Tests Kacper P. Chwialkowski, Dino Sejdinovic, Arthur Gretton
Extracting Latent Structure From Multiple Interacting Neural Populations Joao Semedo, Amin Zandvakili, Adam Kohn, Christian K. Machens, Byron M. Yu
Variational Gaussian Process State-Space Models Roger Frigola, Yutian Chen, Carl Edward Rasmussen
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang
Global Belief Recursive Neural Networks Romain Paulus, Richard Socher, Christopher D. Manning
Parallel Sampling of HDPs using Sub-Cluster Splits Jason Chang, John W. Fisher III
Recursive Inversion Models for Permutations Christopher Meek, Marina Meila
Active Learning and Best-Response Dynamics Maria-Florina F. Balcan, Christopher Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, Le Song
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S. Shawe-Taylor
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
PEWA: Patch-based Exponentially Weighted Aggregation for image denoising Charles Kervrann
A Safe Screening Rule for Sparse Logistic Regression Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye
Weakly-supervised Discovery of Visual Pattern Configurations Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell
Deep Networks with Internal Selective Attention through Feedback Connections Marijn F. Stollenga, Jonathan Masci, Faustino Gomez, Jürgen Schmidhuber
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology Remi Lemonnier, Kevin Scaman, Nicolas Vayatis
A Bayesian model for identifying hierarchically organised states in neural population activity Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
Deep Convolutional Neural Network for Image Deconvolution Li Xu, Jimmy SJ Ren, Ce Liu, Jiaya Jia
Attentional Neural Network: Feature Selection Using Cognitive Feedback Qian Wang, Jiaxing Zhang, Sen Song, Zheng Zhang
The Blinded Bandit: Learning with Adaptive Feedback Ofer Dekel, Elad Hazan, Tomer Koren
Zero-shot recognition with unreliable attributes Dinesh Jayaraman, Kristen Grauman
Communication Efficient Distributed Machine Learning with the Parameter Server Mu Li, David G. Andersen, Alexander J. Smola, Kai Yu
Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems Cong Han Lim, Stephen Wright
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain Charles Y. Zheng, Franco Pestilli, Ariel Rokem
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation Prateek Jain, Ambuj Tewari, Purushottam Kar
Bayesian Sampling Using Stochastic Gradient Thermostats Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D. Skeel, Hartmut Neven
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors Lingqiao Liu, Chunhua Shen, Lei Wang, Anton van den Hengel, Chao Wang
Efficient learning by implicit exploration in bandit problems with side observations Tomáš Kocák, Gergely Neu, Michal Valko, Remi Munos
An Integer Polynomial Programming Based Framework for Lifted MAP Inference Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav G. Gogate
Hardness of parameter estimation in graphical models Guy Bresler, David Gamarnik, Devavrat Shah
On the Information Theoretic Limits of Learning Ising Models Rashish Tandon, Karthikeyan Shanmugam, Pradeep K. Ravikumar, Alexandros G. Dimakis
Projecting Markov Random Field Parameters for Fast Mixing Xianghang Liu, Justin Domke
A Boosting Framework on Grounds of Online Learning Tofigh Naghibi Mohamadpoor, Beat Pfister
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun
Efficient Minimax Signal Detection on Graphs Jing Qian, Venkatesh Saligrama
Magnitude-sensitive preference formation` Nisheeth Srivastava, Ed Vul, Paul R. Schrater
Accelerated Mini-batch Randomized Block Coordinate Descent Method Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu
Multiscale Fields of Patterns Pedro Felzenszwalb, John G. Oberlin
How hard is my MDP?" The distribution-norm to the rescue" Odalric-Ambrym Maillard, Timothy A. Mann, Shie Mannor
Spectral Learning of Mixture of Hidden Markov Models Cem Subakan, Johannes Traa, Paris Smaragdis
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation Emily L. Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus
Tree-structured Gaussian Process Approximations Thang D. Bui, Richard E. Turner
Optimal decision-making with time-varying evidence reliability Jan Drugowitsch, Ruben Moreno-Bote, Alexandre Pouget
An Autoencoder Approach to Learning Bilingual Word Representations Sarath Chandar A P, Stanislas Lauly, Hugo Larochelle, Mitesh Khapra, Balaraman Ravindran, Vikas C. Raykar, Amrita Saha
Testing Unfaithful Gaussian Graphical Models De Wen Soh, Sekhar C. Tatikonda
Deep Recursive Neural Networks for Compositionality in Language Ozan Irsoy, Claire Cardie
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation Mingjun Zhong, Nigel Goddard, Charles Sutton
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data Florian Stimberg, Andreas Ruttor, Manfred Opper
Low-Rank Time-Frequency Synthesis Cédric Févotte, Matthieu Kowalski
Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks Yuanyuan Mi, C. C. Alan Fung, K. Y. Michael Wong, Si Wu
Learning to Optimize via Information-Directed Sampling Daniel Russo, Benjamin Van Roy
Distributed Estimation, Information Loss and Exponential Families Qiang Liu, Alexander T. Ihler
A* Sampling Chris J. Maddison, Daniel Tarlow, Tom Minka
Consistent Binary Classification with Generalized Performance Metrics Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) Anshumali Shrivastava, Ping Li
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data Karthika Mohan, Judea Pearl
Restricted Boltzmann machines modeling human choice Takayuki Osogami, Makoto Otsuka
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures Harikrishna Narasimhan, Rohit Vaish, Shivani Agarwal
Clustering from Labels and Time-Varying Graphs Shiau Hong Lim, Yudong Chen, Huan Xu
Unsupervised learning of an efficient short-term memory network Pietro Vertechi, Wieland Brendel, Christian K. Machens
Projective dictionary pair learning for pattern classification Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng
Analysis of Learning from Positive and Unlabeled Data Marthinus C. du Plessis, Gang Niu, Masashi Sugiyama
Learning with Fredholm Kernels Qichao Que, Mikhail Belkin, Yusu Wang
How transferable are features in deep neural networks? Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson
Concavity of reweighted Kikuchi approximation Po-Ling Loh, Andre Wibisono
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim, Cynthia Rudin, Julie A. Shah
Advances in Learning Bayesian Networks of Bounded Treewidth Siqi Nie, Denis D. Maua, Cassio P. de Campos, Qiang Ji
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm Jun Zhu, Junhua Mao, Alan L. Yuille
On the Computational Efficiency of Training Neural Networks Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
Scaling-up Importance Sampling for Markov Logic Networks Deepak Venugopal, Vibhav G. Gogate
Unsupervised Transcription of Piano Music Taylor Berg-Kirkpatrick, Jacob Andreas, Dan Klein
Multi-Scale Spectral Decomposition of Massive Graphs Si Si, Donghyuk Shin, Inderjit S. Dhillon, Beresford N. Parlett
Dimensionality Reduction with Subspace Structure Preservation Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju
Ranking via Robust Binary Classification Hyokun Yun, Parameswaran Raman, S. Vishwanathan
Learning the Learning Rate for Prediction with Expert Advice Wouter M. Koolen, Tim van Erven, Peter Grünwald
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling Mingyuan Zhou
Learning Deep Features for Scene Recognition using Places Database Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva
Efficient Sampling for Learning Sparse Additive Models in High Dimensions Hemant Tyagi, Bernd Gärtner, Andreas Krause
A framework for studying synaptic plasticity with neural spike train data Scott Linderman, Christopher H. Stock, Ryan P. Adams
Real-Time Decoding of an Integrate and Fire Encoder Shreya Saxena, Munther Dahleh
Parallel Direction Method of Multipliers Huahua Wang, Arindam Banerjee, Zhi-Quan Luo
Spectral Methods for Supervised Topic Models Yining Wang, Jun Zhu
Exclusive Feature Learning on Arbitrary Structures via $\ell_{1,2}$-norm Deguang Kong, Ryohei Fujimaki, Ji Liu, Feiping Nie, Chris Ding
Non-convex Robust PCA Praneeth Netrapalli, Niranjan U N, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain
Expectation-Maximization for Learning Determinantal Point Processes Jennifer A. Gillenwater, Alex Kulesza, Emily Fox, Ben Taskar
Estimation with Norm Regularization Arindam Banerjee, Sheng Chen, Farideh Fazayeli, Vidyashankar Sivakumar
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
Nonparametric Bayesian inference on multivariate exponential families William R. Vega-Brown, Marek Doniec, Nicholas G. Roy
On Communication Cost of Distributed Statistical Estimation and Dimensionality Ankit Garg, Tengyu Ma, Huy Nguyen
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation Eran Treister, Javier S. Turek
Local Decorrelation For Improved Pedestrian Detection Woonhyun Nam, Piotr Dollar, Joon Hee Han
Graph Clustering With Missing Data: Convex Algorithms and Analysis Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi
Spatio-temporal Representations of Uncertainty in Spiking Neural Networks Cristina Savin, Sophie Denève
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models Michalis Titsias RC AUEB, Christopher Yau
The Infinite Mixture of Infinite Gaussian Mixtures Halid Z. Yerebakan, Bartek Rajwa, Murat Dundar
Partition-wise Linear Models Hidekazu Oiwa, Ryohei Fujimaki
Convex Deep Learning via Normalized Kernels Özlem Aslan, Xinhua Zhang, Dale Schuurmans
Discovering Structure in High-Dimensional Data Through Correlation Explanation Greg Ver Steeg, Aram Galstyan
Difference of Convex Functions Programming for Reinforcement Learning Bilal Piot, Matthieu Geist, Olivier Pietquin
Local Linear Convergence of Forward--Backward under Partial Smoothness Jingwei Liang, Jalal Fadili, Gabriel Peyré
Improved Distributed Principal Component Analysis Yingyu Liang, Maria-Florina F. Balcan, Vandana Kanchanapally, David Woodruff
Reputation-based Worker Filtering in Crowdsourcing Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman
large scale canonical correlation analysis with iterative least squares Yichao Lu, Dean P. Foster
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi
Iterative Neural Autoregressive Distribution Estimator NADE-k Tapani Raiko, Yao Li, Kyunghyun Cho, Yoshua Bengio
Reducing the Rank in Relational Factorization Models by Including Observable Patterns Maximilian Nickel, Xueyan Jiang, Volker Tresp
Deterministic Symmetric Positive Semidefinite Matrix Completion William E. Bishop, Byron M. Yu
Distributed Parameter Estimation in Probabilistic Graphical Models Yariv D. Mizrahi, Misha Denil, Nando de Freitas
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets Jie Wang, Jieping Ye
The Large Margin Mechanism for Differentially Private Maximization Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
Causal Inference through a Witness Protection Program Ricardo Silva, Robin Evans
Self-Adaptable Templates for Feature Coding Xavier Boix, Gemma Roig, Salomon Diether, Luc V. Gool
A Framework for Testing Identifiability of Bayesian Models of Perception Luigi Acerbi, Wei Ji Ma, Sethu Vijayakumar
Low Rank Approximation Lower Bounds in Row-Update Streams David Woodruff
Probabilistic ODE Solvers with Runge-Kutta Means Michael Schober, David K. Duvenaud, Philipp Hennig
Learning a Concept Hierarchy from Multi-labeled Documents Viet-An Nguyen, Jordan L. Ying, Philip Resnik, Jonathan Chang
Dependent nonparametric trees for dynamic hierarchical clustering Kumar Avinava Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing
A Statistical Decision-Theoretic Framework for Social Choice Hossein Azari Soufiani, David C. Parkes, Lirong Xia
Generative Adversarial Nets Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning Brendan McMahan, Matthew Streeter
Sequential Monte Carlo for Graphical Models Christian Andersson Naesseth, Fredrik Lindsten, Thomas B. Schön
Learning Time-Varying Coverage Functions Nan Du, Yingyu Liang, Maria-Florina F. Balcan, Le Song
Learning Shuffle Ideals Under Restricted Distributions Dongqu Chen
Spectral Clustering of graphs with the Bethe Hessian Alaa Saade, Florent Krzakala, Lenka Zdeborová
Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers Mehryar Mohri, Andres Munoz
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation Ohad Shamir
Repeated Contextual Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed
Learning with Pseudo-Ensembles Philip Bachman, Ouais Alsharif, Doina Precup
Top Rank Optimization in Linear Time Nan Li, Rong Jin, Zhi-Hua Zhou
Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics Sergey Levine, Pieter Abbeel
Optimizing F-Measures by Cost-Sensitive Classification Shameem Puthiya Parambath, Nicolas Usunier, Yves Grandvalet
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis Cong Xie, Ling Yan, Wu-Jun Li, Zhihua Zhang
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, Jong-Shi Pang
Active Regression by Stratification Sivan Sabato, Remi Munos
Distance-Based Network Recovery under Feature Correlation David Adametz, Volker Roth
Rounding-based Moves for Metric Labeling M. Pawan Kumar
Transportability from Multiple Environments with Limited Experiments: Completeness Results Elias Bareinboim, Judea Pearl
Fast and Robust Least Squares Estimation in Corrupted Linear Models Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann
Incremental Local Gaussian Regression Franziska Meier, Philipp Hennig, Stefan Schaal
Controlling privacy in recommender systems Yu Xin, Tommi Jaakkola
Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology Mehmet Gönen, Adam A. Margolin
Object Localization based on Structural SVM using Privileged Information Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han
Robust Logistic Regression and Classification Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
Flexible Transfer Learning under Support and Model Shift Xuezhi Wang, Jeff Schneider
Computing Nash Equilibria in Generalized Interdependent Security Games Hau Chan, Luis E. Ortiz
Multitask learning meets tensor factorization: task imputation via convex optimization Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka
Mind the Nuisance: Gaussian Process Classification using Privileged Noise Daniel Hernández-lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto
On Integrated Clustering and Outlier Detection Lionel Ott, Linsey Pang, Fabio T. Ramos, Sanjay Chawla
Latent Support Measure Machines for Bag-of-Words Data Classification Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada
Sparse Polynomial Learning and Graph Sketching Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G. Dimakis, Adam Klivans
The Noisy Power Method: A Meta Algorithm with Applications Moritz Hardt, Eric Price
Robust Tensor Decomposition with Gross Corruption Quanquan Gu, Huan Gui, Jiawei Han
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning Marek Petrik, Dharmashankar Subramanian
On Prior Distributions and Approximate Inference for Structured Variables Oluwasanmi O. Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell Poldrack
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs Miles Lopes
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time Zhaoran Wang, Huanran Lu, Han Liu
Learning to Search in Branch and Bound Algorithms He He, Hal Daume III, Jason M. Eisner
Bayesian Inference for Structured Spike and Slab Priors Michael R. Andersen, Ole Winther, Lars K. Hansen
Just-In-Time Learning for Fast and Flexible Inference S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn
Fast Kernel Learning for Multidimensional Pattern Extrapolation Andrew G. Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham
Recursive Context Propagation Network for Semantic Scene Labeling Abhishek Sharma, Oncel Tuzel, Ming-Yu Liu
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan
Fairness in Multi-Agent Sequential Decision-Making Chongjie Zhang, Julie A. Shah
Multi-Class Deep Boosting Vitaly Kuznetsov, Mehryar Mohri, Umar Syed
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network David Eigen, Christian Puhrsch, Rob Fergus
Provable Submodular Minimization using Wolfe's Algorithm Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions Purushottam Kar, Harikrishna Narasimhan, Prateek Jain
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing
Shape and Illumination from Shading using the Generic Viewpoint Assumption Daniel Zoran, Dilip Krishnan, José Bento, Bill Freeman
Asynchronous Anytime Sequential Monte Carlo Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh
Sparse Space-Time Deconvolution for Calcium Image Analysis Ferran Diego Andilla, Fred A. Hamprecht
From Stochastic Mixability to Fast Rates Nishant A. Mehta, Robert C. Williamson
Algorithm selection by rational metareasoning as a model of human strategy selection Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Tom Griffiths
PAC-Bayesian AUC classification and scoring James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang
Probabilistic Differential Dynamic Programming Yunpeng Pan, Evangelos Theodorou
Improved Multimodal Deep Learning with Variation of Information Kihyuk Sohn, Wenling Shang, Honglak Lee
On Sparse Gaussian Chain Graph Models Calvin McCarter, Seyoung Kim
Convolutional Kernel Networks Julien Mairal, Piotr Koniusz, Zaid Harchaoui, Cordelia Schmid
Learning Chordal Markov Networks by Dynamic Programming Kustaa Kangas, Mikko Koivisto, Teppo Niinimäki
From MAP to Marginals: Variational Inference in Bayesian Submodular Models Josip Djolonga, Andreas Krause
Algorithms for CVaR Optimization in MDPs Yinlam Chow, Mohammad Ghavamzadeh
Structure Regularization for Structured Prediction Xu Sun
Bayes-Adaptive Simulation-based Search with Value Function Approximation Arthur Guez, Nicolas Heess, David Silver, Peter Dayan
Optimal Teaching for Limited-Capacity Human Learners Kaustubh R. Patil, Jerry Zhu, Łukasz Kopeć, Bradley C. Love
Spectral Methods for Indian Buffet Process Inference Hsiao-Yu Tung, Alexander J. Smola
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping Andrej Karpathy, Armand Joulin, Li F. Fei-Fei
Greedy Subspace Clustering Dohyung Park, Constantine Caramanis, Sujay Sanghavi
Feature Cross-Substitution in Adversarial Classification Bo Li, Yevgeniy Vorobeychik
Distributed Balanced Clustering via Mapping Coresets Mohammadhossein Bateni, Aditya Bhaskara, Silvio Lattanzi, Vahab Mirrokni
Stochastic variational inference for hidden Markov models Nick Foti, Jason Xu, Dillon Laird, Emily Fox
Tight convex relaxations for sparse matrix factorization Emile Richard, Guillaume R. Obozinski, Jean-Philippe Vert
Extremal Mechanisms for Local Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection Sang Oh, Onkar Dalal, Kshitij Khare, Bala Rajaratnam
Unsupervised Deep Haar Scattering on Graphs Xu Chen, Xiuyuan Cheng, Stephane Mallat
Communication-Efficient Distributed Dual Coordinate Ascent Martin Jaggi, Virginia Smith, Martin Takac, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
Learning convolution filters for inverse covariance estimation of neural network connectivity George Mohler
General Stochastic Networks for Classification Matthias Zöhrer, Franz Pernkopf
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations Xianjie Chen, Alan L. Yuille
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
Near-optimal sample compression for nearest neighbors Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
Factoring Variations in Natural Images with Deep Gaussian Mixture Models Aaron van den Oord, Benjamin Schrauwen
Automated Variational Inference for Gaussian Process Models Trung V. Nguyen, Edwin V. Bonilla
Extreme bandits Alexandra Carpentier, Michal Valko
Learning Mixed Multinomial Logit Model from Ordinal Data Sewoong Oh, Devavrat Shah
Elementary Estimators for Graphical Models Eunho Yang, Aurelie C. Lozano, Pradeep K. Ravikumar
Efficient Minimax Strategies for Square Loss Games Wouter M. Koolen, Alan Malek, Peter L. Bartlett
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets Adarsh Prasad, Stefanie Jegelka, Dhruv Batra
Robust Bayesian Max-Margin Clustering Changyou Chen, Jun Zhu, Xinhua Zhang
Approximating Hierarchical MV-sets for Hierarchical Clustering Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch
Diverse Randomized Agents Vote to Win Albert Jiang, Leandro Soriano Marcolino, Ariel D. Procaccia, Tuomas Sandholm, Nisarg Shah, Milind Tambe
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model Debarghya Ghoshdastidar, Ambedkar Dukkipati
An Accelerated Proximal Coordinate Gradient Method Qihang Lin, Zhaosong Lu, Lin Xiao
Scalable Non-linear Learning with Adaptive Polynomial Expansions Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus J. Telgarsky
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards Omar Besbes, Yonatan Gur, Assaf Zeevi
Efficient Structured Matrix Rank Minimization Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime Carbonell, Suvrit Sra
Beyond Disagreement-Based Agnostic Active Learning Chicheng Zhang, Kamalika Chaudhuri
Optimal Neural Codes for Control and Estimation Alex K. Susemihl, Ron Meir, Manfred Opper
New Rules for Domain Independent Lifted MAP Inference Happy Mittal, Prasoon Goyal, Vibhav G. Gogate, Parag Singla
Sparse Multi-Task Reinforcement Learning Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
The limits of squared Euclidean distance regularization Michal Derezinski, Manfred K. K. Warmuth
Finding a sparse vector in a subspace: Linear sparsity using alternating directions Qing Qu, Ju Sun, John Wright
Scalable Kernel Methods via Doubly Stochastic Gradients Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina F. Balcan, Le Song
Learning Mixtures of Ranking Models Pranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan
Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings Sebastian Stober, Daniel J. Cameron, Jessica A. Grahn
Content-based recommendations with Poisson factorization Prem K. Gopalan, Laurent Charlin, David Blei
Optimizing Energy Production Using Policy Search and Predictive State Representations Yuri Grinberg, Doina Precup, Michel Gendreau
Time--Data Tradeoffs by Aggressive Smoothing John J. Bruer, Joel A. Tropp, Volkan Cevher, Stephen Becker
Shaping Social Activity by Incentivizing Users Mehrdad Farajtabar, Nan Du, Manuel Gomez Rodriguez, Isabel Valera, Hongyuan Zha, Le Song
Universal Option Models hengshuai yao, Csaba Szepesvari, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar
Discovering, Learning and Exploiting Relevance Cem Tekin, Mihaela van der Schaar
Stochastic Network Design in Bidirected Trees xiaojian wu, Daniel R. Sheldon, Shlomo Zilberstein
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models Yarin Gal, Mark van der Wilk, Carl Edward Rasmussen
On the Convergence Rate of Decomposable Submodular Function Minimization Robert Nishihara, Stefanie Jegelka, Michael I. Jordan
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials Shenlong Wang, Alex Schwing, Raquel Urtasun
Metric Learning for Temporal Sequence Alignment Damien Garreau, Rémi Lajugie, Sylvain Arlot, Francis Bach
Extended and Unscented Gaussian Processes Daniel M. Steinberg, Edwin V. Bonilla
A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment Sahar Akram, Jonathan Z. Simon, Shihab A. Shamma, Behtash Babadi
Sensory Integration and Density Estimation Joseph G. Makin, Philip N. Sabes
Causal Strategic Inference in Networked Microfinance Economies Mohammad T. Irfan, Luis E. Ortiz
Exact Post Model Selection Inference for Marginal Screening Jason D. Lee, Jonathan E. Taylor
Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc V. Le
Information-based learning by agents in unbounded state spaces Shariq A. Mobin, James A. Arnemann, Fritz Sommer
Multi-Resolution Cascades for Multiclass Object Detection Mohammad Saberian, Nuno Vasconcelos
Generalized Dantzig Selector: Application to the k-support norm Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee
Conditional Swap Regret and Conditional Correlated Equilibrium Mehryar Mohri, Scott Yang
Gaussian Process Volatility Model Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani
Fast Sampling-Based Inference in Balanced Neuronal Networks Guillaume Hennequin, Laurence Aitchison, Mate Lengyel
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models Yichuan Zhang, Charles Sutton
Predicting Useful Neighborhoods for Lazy Local Learning Aron Yu, Kristen Grauman
(Almost) No Label No Cry Giorgio Patrini, Richard Nock, Paul Rivera, Tiberio Caetano
Learning Mixtures of Submodular Functions for Image Collection Summarization Sebastian Tschiatschek, Rishabh K. Iyer, Haochen Wei, Jeff A. Bilmes
Distributed Bayesian Posterior Sampling via Moment Sharing Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators Kai Zhong, Ian En-Hsu Yen, Inderjit S. Dhillon, Pradeep K. Ravikumar
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning Mohammad Taha Bahadori, Qi (Rose) Yu, Yan Liu
Multi-scale Graphical Models for Spatio-Temporal Processes firdaus janoos, Huseyin Denli, Niranjan Subrahmanya
Bregman Alternating Direction Method of Multipliers Huahua Wang, Arindam Banerjee
Bounded Regret for Finite-Armed Structured Bandits Tor Lattimore, Remi Munos
Exponential Concentration of a Density Functional Estimator Shashank Singh, Barnabas Poczos
Decomposing Parameter Estimation Problems Khaled S. Refaat, Arthur Choi, Adnan Darwiche
Convex Optimization Procedure for Clustering: Theoretical Revisit Changbo Zhu, Huan Xu, Chenlei Leng, Shuicheng Yan
Submodular Attribute Selection for Action Recognition in Video Jingjing Zheng, Zhuolin Jiang, Rama Chellappa, Jonathon P. Phillips
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls Yuancheng Zhu, John Lafferty
Large-Margin Convex Polytope Machine Alex Kantchelian, Michael C. Tschantz, Ling Huang, Peter L. Bartlett, Anthony D. Joseph, J. D. Tygar
A provable SVD-based algorithm for learning topics in dominant admixture corpus Trapit Bansal, Chiranjib Bhattacharyya, Ravindran Kannan
Divide-and-Conquer Learning by Anchoring a Conical Hull Tianyi Zhou, Jeff A. Bilmes, Carlos Guestrin
Discriminative Metric Learning by Neighborhood Gerrymandering Shubhendu Trivedi, David Mcallester, Greg Shakhnarovich
Learning on graphs using Orthonormal Representation is Statistically Consistent Rakesh Shivanna, Chiranjib Bhattacharyya
Generalized Unsupervised Manifold Alignment Zhen Cui, Hong Chang, Shiguang Shan, Xilin Chen
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction Katerina Fragkiadaki, Marta Salas, Pablo Arbelaez, Jitendra Malik
A statistical model for tensor PCA Emile Richard, Andrea Montanari
Making Pairwise Binary Graphical Models Attractive Nicholas Ruozzi, Tony Jebara
Subspace Embeddings for the Polynomial Kernel Haim Avron, Huy Nguyen, David Woodruff
On the relations of LFPs & Neural Spike Trains David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin
Online Optimization for Max-Norm Regularization Jie Shen, Huan Xu, Ping Li
Cone-Constrained Principal Component Analysis Yash Deshpande, Andrea Montanari, Emile Richard
Fast Training of Pose Detectors in the Fourier Domain João F. Henriques, Pedro Martins, Rui F. Caseiro, Jorge Batista
Optimistic Planning in Markov Decision Processes Using a Generative Model Balázs Szörényi, Gunnar Kedenburg, Remi Munos
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling Ricardo Henao, Xin Yuan, Lawrence Carin
Feedback Detection for Live Predictors Stefan Wager, Nick Chamandy, Omkar Muralidharan, Amir Najmi
Do Convnets Learn Correspondence? Jonathan L. Long, Ning Zhang, Trevor Darrell
Convolutional Neural Network Architectures for Matching Natural Language Sentences Baotian Hu, Zhengdong Lu, Hang Li, Qingcai Chen
Conditional Random Field Autoencoders for Unsupervised Structured Prediction Waleed Ammar, Chris Dyer, Noah A. Smith
Optimal rates for k-NN density and mode estimation Sanjoy Dasgupta, Samory Kpotufe
Coresets for k-Segmentation of Streaming Data Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher III, Daniela Rus
Message Passing Inference for Large Scale Graphical Models with High Order Potentials Jian Zhang, Alex Schwing, Raquel Urtasun
Weighted importance sampling for off-policy learning with linear function approximation A. Rupam Mahmood, Hado P. van Hasselt, Richard S. Sutton
Incremental Clustering: The Case for Extra Clusters Margareta Ackerman, Sanjoy Dasgupta
Positive Curvature and Hamiltonian Monte Carlo Christof Seiler, Simon Rubinstein-Salzedo, Susan Holmes
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit Karin C. Knudson, Jacob Yates, Alexander Huk, Jonathan W. Pillow
Zeta Hull Pursuits: Learning Nonconvex Data Hulls Yuanjun Xiong, Wei Liu, Deli Zhao, Xiaoou Tang
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya, Ashkan Jafarpour
Provable Tensor Factorization with Missing Data Prateek Jain, Sewoong Oh
Learning Generative Models with Visual Attention Charlie Tang, Nitish Srivastava, Russ R. Salakhutdinov
Bandit Convex Optimization: Towards Tight Bounds Elad Hazan, Kfir Levy
A Latent Source Model for Online Collaborative Filtering Guy Bresler, George H. Chen, Devavrat Shah
Self-Paced Learning with Diversity Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, Alexander Hauptmann
Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers Bruno Conejo, Nikos Komodakis, Sebastien Leprince, Jean Philippe Avouac
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs David I. Inouye, Pradeep K. Ravikumar, Inderjit S. Dhillon
Kernel Mean Estimation via Spectral Filtering Krikamol Muandet, Bharath Sriperumbudur, Bernhard Schölkopf
A Dual Algorithm for Olfactory Computation in the Locust Brain Sina Tootoonian, Mate Lengyel
Online combinatorial optimization with stochastic decision sets and adversarial losses Gergely Neu, Michal Valko
Learning Multiple Tasks in Parallel with a Shared Annotator Haim Cohen, Koby Crammer
A Complete Variational Tracker Ryan D. Turner, Steven Bottone, Bhargav Avasarala
Low-dimensional models of neural population activity in sensory cortical circuits Evan W. Archer, Urs Koster, Jonathan W. Pillow, Jakob H. Macke
Spectral k-Support Norm Regularization Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
Learning Optimal Commitment to Overcome Insecurity Avrim Blum, Nika Haghtalab, Ariel D. Procaccia
A Drifting-Games Analysis for Online Learning and Applications to Boosting Haipeng Luo, Robert E. Schapire
Dynamic Rank Factor Model for Text Streams Shaobo Han, Lin Du, Esther Salazar, Lawrence Carin
Consistency of weighted majority votes Daniel Berend, Aryeh Kontorovich
Modeling Deep Temporal Dependencies with Recurrent Grammar Cells"" Vincent Michalski, Roland Memisevic, Kishore Konda
Augur: Data-Parallel Probabilistic Modeling Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam C. Pocock, Stephen Green, Guy L. Steele
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
Mondrian Forests: Efficient Online Random Forests Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process Bahadir Ozdemir, Larry S. Davis
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input Mateusz Malinowski, Mario Fritz
Semi-supervised Learning with Deep Generative Models Durk P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling
Biclustering Using Message Passing Luke O'Connor, Soheil Feizi
Stochastic Proximal Gradient Descent with Acceleration Techniques Atsushi Nitanda
DFacTo: Distributed Factorization of Tensors Joon Hee Choi, S. Vishwanathan
Robust Classification Under Sample Selection Bias Anqi Liu, Brian Ziebart
Deep Joint Task Learning for Generic Object Extraction Xiaolong Wang, Liliang Zhang, Liang Lin, Zhujin Liang, Wangmeng Zuo
Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning Robert V. Lindsey, Mohammad Khajah, Michael C. Mozer
General Table Completion using a Bayesian Nonparametric Model Isabel Valera, Zoubin Ghahramani
A Representation Theory for Ranking Functions Harsh H. Pareek, Pradeep K. Ravikumar
Multivariate Regression with Calibration Han Liu, Lie Wang, Tuo Zhao
Blossom Tree Graphical Models Zhe Liu, John Lafferty
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich
Rates of Convergence for Nearest Neighbor Classification Kamalika Chaudhuri, Sanjoy Dasgupta
Inferring synaptic conductances from spike trains with a biophysically inspired point process model Kenneth W. Latimer, E.J. Chichilnisky, Fred Rieke, Jonathan W. Pillow
Scalable Inference for Neuronal Connectivity from Calcium Imaging Alyson K. Fletcher, Sundeep Rangan
Minimax-optimal Inference from Partial Rankings Bruce Hajek, Sewoong Oh, Jiaming Xu
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks Yanping Huang, Rajesh PN Rao
Simple MAP Inference via Low-Rank Relaxations Roy Frostig, Sida Wang, Percy S. Liang, Christopher D. Manning
Fast Prediction for Large-Scale Kernel Machines Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon
Median Selection Subset Aggregation for Parallel Inference Xiangyu Wang, Peichao Peng, David B. Dunson
Design Principles of the Hippocampal Cognitive Map Kimberly L. Stachenfeld, Matthew Botvinick, Samuel J. Gershman
Smoothed Gradients for Stochastic Variational Inference Stephan Mandt, David Blei
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations Ryan Kiros, Richard Zemel, Russ R. Salakhutdinov
Feedforward Learning of Mixture Models Matthew Lawlor, Steven W. Zucker
Searching for Higgs Boson Decay Modes with Deep Learning Peter J. Sadowski, Daniel Whiteson, Pierre Baldi
Decoupled Variational Gaussian Inference Mohammad Emtiyaz E. Khan
On Multiplicative Multitask Feature Learning Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun
Learning Distributed Representations for Structured Output Prediction Vivek Srikumar, Christopher D. Manning
Large-scale L-BFGS using MapReduce Weizhu Chen, Zhenghao Wang, Jingren Zhou
Sparse PCA via Covariance Thresholding Yash Deshpande, Andrea Montanari
Randomized Experimental Design for Causal Graph Discovery Huining Hu, Zhentao Li, Adrian R. Vetta
Combinatorial Pure Exploration of Multi-Armed Bandits Shouyuan Chen, Tian Lin, Irwin King, Michael R. Lyu, Wei Chen
Deep Learning Face Representation by Joint Identification-Verification Yi Sun, Yuheng Chen, Xiaogang Wang, Xiaoou Tang
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation Jonathan J. Tompson, Arjun Jain, Yann LeCun, Christoph Bregler
Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision Deepti Pachauri, Risi Kondor, Gautam Sargur, Vikas Singh
Structure learning of antiferromagnetic Ising models Guy Bresler, David Gamarnik, Devavrat Shah
Clustered factor analysis of multineuronal spike data Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
Mode Estimation for High Dimensional Discrete Tree Graphical Models Chao Chen, Han Liu, Dimitris Metaxas, Tianqi Zhao
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature Tom Gunter, Michael A. Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts
Do Deep Nets Really Need to be Deep? Jimmy Ba, Rich Caruana
A Unified Semantic Embedding: Relating Taxonomies and Attributes Sung Ju Hwang, Leonid Sigal
Parallel Double Greedy Submodular Maximization Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, Michael I. Jordan
Efficient Optimization for Average Precision SVM Pritish Mohapatra, C.V. Jawahar, M. Pawan Kumar
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio, Francis Bach, Simon Lacoste-Julien
A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights Weijie Su, Stephen Boyd, Emmanuel Candes
Sparse PCA with Oracle Property Quanquan Gu, Zhaoran Wang, Han Liu
SerialRank: Spectral Ranking using Seriation Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
Pre-training of Recurrent Neural Networks via Linear Autoencoders Luca Pasa, Alessandro Sperduti
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm Deanna Needell, Rachel Ward, Nati Srebro
Best-Arm Identification in Linear Bandits Marta Soare, Alessandro Lazaric, Remi Munos
Multivariate f-divergence Estimation With Confidence Kevin Moon, Alfred Hero
Online Decision-Making in General Combinatorial Spaces Arun Rajkumar, Shivani Agarwal
Altitude Training: Strong Bounds for Single-Layer Dropout Stefan Wager, William Fithian, Sida Wang, Percy S. Liang
Probabilistic low-rank matrix completion on finite alphabets Jean Lafond, Olga Klopp, Eric Moulines, Joseph Salmon
Tight Continuous Relaxation of the Balanced k-Cut Problem Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
Discrete Graph Hashing Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang
Orbit Regularization Renato Negrinho, Andre Martins
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System Yuanyuan Mi, Luozheng Li, Dahui Wang, Si Wu
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces Minh Ha Quang, Marco San Biagio, Vittorio Murino
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
Sparse Bayesian structure learning with “dependent relevance determination” priors Anqi Wu, Mijung Park, Oluwasanmi O. Koyejo, Jonathan W. Pillow
Deep Symmetry Networks Robert Gens, Pedro M. Domingos
Covariance shrinkage for autocorrelated data Daniel Bartz, Klaus-Robert Müller
Scale Adaptive Blind Deblurring Haichao Zhang, Jianchao Yang
Parallel Feature Selection Inspired by Group Testing Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q. Ngo, XuanLong Nguyen, Christopher Ré, Venu Govindaraju
Streaming, Memory Limited Algorithms for Community Detection Se-Young Yun, marc lelarge, Alexandre Proutiere
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
Clamping Variables and Approximate Inference Adrian Weller, Tony Jebara
Neural Word Embedding as Implicit Matrix Factorization Omer Levy, Yoav Goldberg
Constrained convex minimization via model-based excessive gap Quoc Tran-Dinh, Volkan Cevher
A Filtering Approach to Stochastic Variational Inference Neil Houlsby, David Blei