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ORCIDNIPS 2014: Montreal, Quebec, Canada
- Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada. 2014 - Krikamol Muandet, Bharath K. Sriperumbudur, Bernhard Schölkopf:
Kernel Mean Estimation via Spectral Filtering. 1-9 - Yichuan Zhang, Charles Sutton:
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models. 10-18 - Mu Li, David G. Andersen, Alexander J. Smola, Kai Yu:
Communication Efficient Distributed Machine Learning with the Parameter Server. 19-27 - Halid Ziya Yerebakan, Bartek Rajwa, Murat Dundar:
The Infinite Mixture of Infinite Gaussian Mixtures. 28-36 - Anqi Liu, Brian D. Ziebart:
Robust Classification Under Sample Selection Bias. 37-45 - Yuanjun Xiong, Wei Liu, Deli Zhao, Xiaoou Tang:
Zeta Hull Pursuits: Learning Nonconvex Data Hulls. 46-54 - Katerina Fragkiadaki, Marta Salas, Pablo Andrés Arbeláez, Jitendra Malik:
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction. 55-63 - Ferran Diego Andilla, Fred A. Hamprecht:
Sparse Space-Time Deconvolution for Calcium Image Analysis. 64-72 - Takayuki Osogami, Makoto Otsuka:
Restricted Boltzmann machines modeling human choice. 73-81 - Pedro F. Felzenszwalb, John G. Oberlin:
Multiscale Fields of Patterns. 82-90 - Yichao Lu, Dean P. Foster:
large scale canonical correlation analysis with iterative least squares. 91-99 - Stefan Wager, William Fithian, Sida Wang, Percy Liang:
Altitude Training: Strong Bounds for Single-Layer Dropout. 100-108 - M. Pawan Kumar:
Rounding-based Moves for Metric Labeling. 109-117 - Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, Michael I. Jordan:
Parallel Double Greedy Submodular Maximization. 118-126 - Han Liu, Lie Wang, Tuo Zhao:
Multivariate Regression with Calibration. 127-135 - Jason D. Lee, Jonathan E. Taylor:
Exact Post Model Selection Inference for Marginal Screening. 136-144 - Yingzhen Yang, Feng Liang, Shuicheng Yan, Zhangyang Wang, Thomas S. Huang:
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification. 145-153 - S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn:
Just-In-Time Learning for Fast and Flexible Inference. 154-162 - Ohad Shamir:
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation. 163-171 - Danfeng Qin, Xuanli Chen, Matthieu Guillaumin, Luc Van Gool:
Quantized Kernel Learning for Feature Matching. 172-180 - Huahua Wang, Arindam Banerjee, Zhi-Quan Luo:
Parallel Direction Method of Multipliers. 181-189 - Giorgio Patrini, Richard Nock, Tibério S. Caetano, Paul Rivera:
(Almost) No Label No Cry. 190-198 - Yonatan Gur, Assaf Zeevi, Omar Besbes:
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards. 199-207 - Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han:
Object Localization based on Structural SVM using Privileged Information. 208-216 - Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang:
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations. 217-225 - Daniel Zoran, Dilip Krishnan, José Bento, Bill Freeman:
Shape and Illumination from Shading using the Generic Viewpoint Assumption. 226-234 - Jason Chang, John W. Fisher III:
Parallel Sampling of HDPs using Sub-Cluster Splits. 235-243 - Josip Djolonga, Andreas Krause:
From MAP to Marginals: Variational Inference in Bayesian Submodular Models. 244-252 - Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan:
Robust Logistic Regression and Classification. 253-261 - Tianbao Yang, Rong Jin:
Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities. 262-270 - Sung Ju Hwang, Leonid Sigal:
A Unified Semantic Embedding: Relating Taxonomies and Attributes. 271-279 - Elias Bareinboim, Judea Pearl:
Transportability from Multiple Environments with Limited Experiments: Completeness Results. 280-288 - Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner:
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning. 289-297 - Ricardo Silva, Robin J. Evans:
Causal Inference through a Witness Protection Program. 298-306 - Margareta Ackerman, Sanjoy Dasgupta:
Incremental Clustering: The Case for Extra Clusters. 307-315 - Firdaus Janoos, Huseyin Denli, Niranjan A. Subrahmanya:
Multi-scale Graphical Models for Spatio-Temporal Processes. 316-324 - Tapani Raiko, Li Yao, KyungHyun Cho, Yoshua Bengio:
Iterative Neural Autoregressive Distribution Estimator NADE-k. 325-333 - Yash Deshpande, Andrea Montanari:
Sparse PCA via Covariance Thresholding. 334-342 - Evan W. Archer, Urs Köster, Jonathan W. Pillow, Jakob H. Macke:
Low-dimensional models of neural population activity in sensory cortical circuits. 343-351 - Yuanyuan Mi, Luozheng Li, Dahui Wang, Si Wu:
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System. 352-360 - Harsh H. Pareek, Pradeep Ravikumar:
A Representation Theory for Ranking Functions. 361-369 - Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch:
Near-optimal sample compression for nearest neighbors. 370-378 - Shouyuan Chen, Tian Lin, Irwin King, Michael R. Lyu, Wei Chen:
Combinatorial Pure Exploration of Multi-Armed Bandits. 379-387 - Minh Ha Quang, Marco San-Biagio, Vittorio Murino:
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces. 388-396 - Debarghya Ghoshdastidar, Ambedkar Dukkipati:
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model. 397-405 - Alaa Saade, Florent Krzakala
, Lenka Zdeborová:
Spectral Clustering of graphs with the Bethe Hessian. 406-414 - Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann:
Fast and Robust Least Squares Estimation in Corrupted Linear Models. 415-423 - Woonhyun Nam, Piotr Dollár, Joon Hee Han:
Local Decorrelation For Improved Pedestrian Detection. 424-432 - Robert A. Vandermeulen, Clayton D. Scott:
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space. 433-441 - Chicheng Zhang, Kamalika Chaudhuri:
Beyond Disagreement-Based Agnostic Active Learning. 442-450 - Arthur Guez, Nicolas Heess, David Silver, Peter Dayan:
Bayes-Adaptive Simulation-based Search with Value Function Approximation. 451-459 - Sahar Akram, Jonathan Z. Simon, Shihab A. Shamma, Behtash Babadi:
A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment. 460-468 - Sivan Sabato, Rémi Munos:
Active Regression by Stratification. 469-477 - Joseph G. Makin, Philip N. Sabes:
Sensory Integration and Density Estimation. 478-486 - Bolei Zhou, Àgata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva:
Learning Deep Features for Scene Recognition using Places Database. 487-495 - Ryan D. Turner, Steven Bottone, Bhargav Avasarala:
A Complete Variational Tracker. 496-504 - Yuanyuan Mi, C. C. Alan Fung, K. Y. Michael Wong, Si Wu:
Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks. 505-513 - Hemant Tyagi, Bernd Gärtner, Andreas Krause:
Efficient Sampling for Learning Sparse Additive Models in High Dimensions. 514-522 - Xiaolong Wang, Liliang Zhang, Liang Lin, Zhujin Liang, Wangmeng Zuo:
Deep Joint Task Learning for Generic Object Extraction. 523-531 - Changyou Chen, Jun Zhu, Xinhua Zhang:
Robust Bayesian Max-Margin Clustering. 532-540 - Deepti Pachauri, Risi Kondor, Gautam Sargur, Vikas Singh:
Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision. 541-549 - Tor Lattimore, Rémi Munos:
Bounded Regret for Finite-Armed Structured Bandits. 550-558 - Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher III, Daniela Rus:
Coresets for k-Segmentation of Streaming Data. 559-567 - Karen Simonyan, Andrew Zisserman:
Two-Stream Convolutional Networks for Action Recognition in Videos. 568-576 - Greg Ver Steeg, Aram Galstyan:
Discovering Structure in High-Dimensional Data Through Correlation Explanation. 577-585 - Christof Seiler, Simon Rubinstein-Salzedo, Susan P. Holmes:
Positive Curvature and Hamiltonian Monte Carlo. 586-594 - Sewoong Oh, Devavrat Shah:
Learning Mixed Multinomial Logit Model from Ordinal Data. 595-603 - Ian Osband, Benjamin Van Roy:
Near-optimal Reinforcement Learning in Factored MDPs. 604-612 - Tomás Kocák, Gergely Neu, Michal Valko, Rémi Munos:
Efficient learning by implicit exploration in bandit problems with side observations. 613-621 - Kareem Amin, Afshin Rostamizadeh, Umar Syed:
Repeated Contextual Auctions with Strategic Buyers. 622-630 - Christopher Meek, Marina Meila:
Recursive Inversion Models for Permutations. 631-639 - Robert Nishihara, Stefanie Jegelka, Michael I. Jordan:
On the Convergence Rate of Decomposable Submodular Function Minimization. 640-648 - Happy Mittal, Prasoon Goyal, Vibhav Gogate, Parag Singla:
New Rules for Domain Independent Lifted MAP Inference. 649-657 - James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang:
PAC-Bayesian AUC classification and scoring. 658-666 - Sang-Yun Oh, Onkar Dalal, Kshitij Khare, Bala Rajaratnam:
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection. 667-675 - Oluwasanmi Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack:
On Prior Distributions and Approximate Inference for Structured Variables. 676-684 - Prateek Jain, Ambuj Tewari, Purushottam Kar:
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation. 685-693 - Purushottam Kar, Harikrishna Narasimhan, Prateek Jain:
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions. 694-702 - Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Analysis of Learning from Positive and Unlabeled Data. 703-711 - Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju:
Dimensionality Reduction with Subspace Structure Preservation. 712-720 - Quoc Tran-Dinh, Volkan Cevher:
Constrained convex minimization via model-based excessive gap. 721-729 - Florian Stimberg, Andreas Ruttor, Manfred Opper:
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data. 730-738 - Michael Schober, David Duvenaud, Philipp Hennig:
Probabilistic ODE Solvers with Runge-Kutta Means. 739-747 - Jan Drugowitsch, Rubén Moreno-Bote, Alexandre Pouget:
Optimal decision-making with time-varying evidence reliability. 748-756 - Dongqu Chen:
Learning Shuffle Ideals Under Restricted Distributions. 757-765 - Alexey Dosovitskiy, Jost Tobias Springenberg, Martin A. Riedmiller, Thomas Brox:
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. 766-774 - David Adametz, Volker Roth:
Distance-Based Network Recovery under Feature Correlation. 775-783 - Elad Hazan, Kfir Y. Levy:
Bandit Convex Optimization: Towards Tight Bounds. 784-792 - Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng:
Projective dictionary pair learning for pattern classification. 793-801 - Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari:
Provable Submodular Minimization using Wolfe's Algorithm. 802-809 - Amir Sani, Gergely Neu, Alessandro Lazaric:
Exploiting easy data in online optimization. 810-818 - Daniele Calandriello, Alessandro Lazaric, Marcello Restelli:
Sparse Multi-Task Reinforcement Learning. 819-827 - Marta Soare, Alessandro Lazaric, Rémi Munos:
Best-Arm Identification in Linear Bandits. 828-836 - Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto:
Mind the Nuisance: Gaussian Process Classification using Privileged Noise. 837-845 - Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis:
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. 846-854 - Roi Livni, Shai Shalev-Shwartz, Ohad Shamir:
On the Computational Efficiency of Training Neural Networks. 855-863 - Xavier Boix, Gemma Roig, Salomon Diether, Luc Van Gool:
Self-Adaptable Templates for Feature Coding. 864-872 - Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John Shawe-Taylor:
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks. 873-881 - XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein:
Stochastic Network Design in Bidirected Trees. 882-890 - George O. Mohler:
Learning convolution filters for inverse covariance estimation of neural network connectivity. 891-899 - Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic:
SerialRank: Spectral Ranking using Seriation. 900-908 - Adrian Weller, Tony Jebara:
Clamping Variables and Approximate Inference. 909-917 - José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani:
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. 918-926 - Eran Treister, Javier Turek:
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation. 927-935 - Shenlong Wang, Alexander G. Schwing, Raquel Urtasun:
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials. 936-944 - Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich:
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices. 945-953 - Kenneth W. Latimer, E. J. Chichilnisky, Fred Rieke, Jonathan W. Pillow:
Inferring synaptic conductances from spike trains with a biophysically inspired point process model. 954-962 - Daniel Soudry, Itay Hubara, Ron Meir:
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights. 963-971 - Franziska Meier, Philipp Hennig, Stefan Schaal:
Incremental Local Gaussian Regression. 972-980 - Isabel Valera, Zoubin Ghahramani:
General Table Completion using a Bayesian Nonparametric Model. 981-989 - Hengshuai Yao, Csaba Szepesvári, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar:
Universal Option Models. 990-998 - Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch:
Approximating Hierarchical MV-sets for Hierarchical Clustering. 999-1007 - Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon:
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings. 1008-1016 - Deanna Needell, Rachel A. Ward, Nathan Srebro:
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm. 1017-1025 - Luigi Acerbi, Wei Ji Ma, Sethu Vijayakumar:
A Framework for Testing Identifiability of Bayesian Models of Perception. 1026-1034 - Balázs Szörényi, Gunnar Kedenburg, Rémi Munos:
Optimistic Planning in Markov Decision Processes Using a Generative Model. 1035-1043 - Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Gaussian Process Volatility Model. 1044-1052 - Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye:
A Safe Screening Rule for Sparse Logistic Regression. 1053-1061 - Guy Bresler, David Gamarnik, Devavrat Shah:
Hardness of parameter estimation in graphical models. 1062-1070 - Sergey Levine, Pieter Abbeel:
Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics. 1071-1079 - Nisheeth Srivastava, Ed Vul, Paul R. Schrater:
Magnitude-sensitive preference formation. 1080-1088 - Alexandra Carpentier, Michal Valko:
Extreme bandits. 1089-1097 - Qiang Liu, Alexander Ihler:
Distributed Estimation, Information Loss and Exponential Families. 1098-1106 - Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain:
Non-convex Robust PCA. 1107-1115 - Francesco Orabona:
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning. 1116-1124 - Jun Zhu, Junhua Mao, Alan L. Yuille:
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm. 1125-1133 - Jian Zhang, Alexander G. Schwing, Raquel Urtasun:
Message Passing Inference for Large Scale Graphical Models with High Order Potentials. 1134-1142 - Lingqiao Liu, Chunhua Shen, Lei Wang, Anton van den Hengel, Chao Wang:
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors. 1143-1151 - Kumar Avinava Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing:
Dependent nonparametric trees for dynamic hierarchical clustering. 1152-1160 - Mohammad Tanvir Irfan, Luis E. Ortiz:
Causal Strategic Inference in Networked Microfinance Economies. 1161-1169 - Haim Cohen, Koby Crammer:
Learning Multiple Tasks in Parallel with a Shared Annotator. 1170-1178 - Maximilian Nickel, Xueyan Jiang, Volker Tresp:
Reducing the Rank in Relational Factorization Models by Including Observable Patterns. 1179-1187 - Shiau Hong Lim, Yudong Chen, Huan Xu:
Clustering from Labels and Time-Varying Graphs. 1188-1196 - Nishant A. Mehta, Robert C. Williamson:
From Stochastic Mixability to Fast Rates. 1197-1205 - Guangcan Liu, Ping Li:
Recovery of Coherent Data via Low-Rank Dictionary Pursuit. 1206-1214 - Karin C. Knudson, Jacob L. Yates, Alexander Huk, Jonathan W. Pillow:
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit. 1215-1223 - Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi:
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP. 1224-1232 - Cem Tekin, Mihaela van der Schaar:
Discovering, Learning and Exploiting Relevance. 1233-1241 - Tianyi Zhou, Jeff A. Bilmes, Carlos Guestrin:
Divide-and-Conquer Learning by Anchoring a Conical Hull. 1242-1250 - Daniel M. Steinberg, Edwin V. Bonilla:
Extended and Unscented Gaussian Processes. 1251-1259 - Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan:
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing. 1260-1268 - Emily L. Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus:
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. 1269-1277 - Wojciech Zaremba, Karol Kurach, Rob Fergus:
Learning to Discover Efficient Mathematical Identities. 1278-1286 - Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song:
The Large Margin Mechanism for Differentially Private Maximization. 1287-1295 - Joon Hee Choi, S. Vishwanathan:
DFacTo: Distributed Factorization of Tensors. 1296-1304 - Mehmet Gönen, Adam A. Margolin:
Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology. 1305-1313 - Mehryar Mohri, Scott Yang:
Conditional Swap Regret and Conditional Correlated Equilibrium. 1314-1322 - Chao Chen, Han Liu, Dimitris N. Metaxas, Tianqi Zhao:
Mode Estimation for High Dimensional Discrete Tree Graphical Models. 1323-1331 - Weizhu Chen, Zhenghao Wang, Jingren Zhou:
Large-scale L-BFGS using MapReduce. 1332-1340 - Jingjing Zheng, Zhuolin Jiang, Rama Chellappa, P. Jonathon Phillips:
Submodular Attribute Selection for Action Recognition in Video. 1341-1349 - Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra:
Efficient Structured Matrix Rank Minimization. 1350-1358 - Lionel Ott, Linsey Xiaolin Pang, Fabio Tozeto Ramos, Sanjay Chawla:
On Integrated Clustering and Outlier Detection. 1359-1367 - Haipeng Luo, Robert E. Schapire:
A Drifting-Games Analysis for Online Learning and Applications to Boosting. 1368-1376 - Xianghang Liu, Justin Domke:
Projecting Markov Random Field Parameters for Fast Mixing. 1377-1385 - Robert V. Lindsey, Mohammad Khajah, Michael C. Mozer:
Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning. 1386-1394 - Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya, Ashkan Jafarpour:
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures. 1395-1403 - Trung V. Nguyen, Edwin V. Bonilla:
Automated Variational Inference for Gaussian Process Models. 1404-1412 - Sebastian Tschiatschek, Rishabh K. Iyer, Haochen Wei, Jeff A. Bilmes:
Learning Mixtures of Submodular Functions for Image Collection Summarization. 1413-1421 - Quanquan Gu, Huan Gui, Jiawei Han:
Robust Tensor Decomposition with Gross Corruption. 1422-1430 - Prateek Jain, Sewoong Oh:
Provable Tensor Factorization with Missing Data. 1431-1439 - Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, Jong-Shi Pang:
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization. 1440-1448 - Sebastian Stober, Daniel J. Cameron, Jessica A. Grahn:
Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings. 1449-1457 - Zhe Liu, John D. Lafferty:
Blossom Tree Graphical Models. 1458-1465 - Ian Osband, Benjamin Van Roy:
Model-based Reinforcement Learning and the Eluder Dimension. 1466-1474 - Bruce E. Hajek, Sewoong Oh, Jiaming Xu:
Minimax-optimal Inference from Partial Rankings. 1475-1483 - Hsiao-Yu Fish Tung, Alexander J. Smola:
Spectral Methods for Indian Buffet Process Inference. 1484-1492 - Harikrishna Narasimhan, Rohit Vaish, Shivani Agarwal:
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures. 1493-1501 - Nan Li, Rong Jin, Zhi-Hua Zhou:
Top Rank Optimization in Linear Time. 1502-1510 - Yining Wang, Jun Zhu:
Spectral Methods for Supervised Topic Models. 1511-1519 - Karthika Mohan, Judea Pearl:
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data. 1520-1528 - Quanquan Gu, Zhaoran Wang, Han Liu:
Sparse PCA with Oracle Property. 1529-1537 - Taylor Berg-Kirkpatrick, Jacob Andreas, Dan Klein:
Unsupervised Transcription of Piano Music. 1538-1546 - Mohammad E. Khan:
Decoupled Variational Gaussian Inference. 1547-1555 - Arindam Banerjee, Sheng Chen, Farideh Fazayeli, Vidyashankar Sivakumar:
Estimation with Norm Regularization. 1556-1564 - Khaled S. Refaat, Arthur Choi, Adnan Darwiche:
Decomposing Parameter Estimation Problems. 1565-1573 - Atsushi Nitanda:
Stochastic Proximal Gradient Descent with Acceleration Techniques. 1574-1582 - Daniel Russo, Benjamin Van Roy:
Learning to Optimize via Information-Directed Sampling. 1583-1591 - Daniel Bartz, Klaus-Robert Müller:
Covariance shrinkage for autocorrelated data. 1592-1600 - Jonathan Long, Ning Zhang, Trevor Darrell:
Do Convnets Learn Correspondence? 1601-1609 - Ofer Dekel, Elad Hazan, Tomer Koren:
The Blinded Bandit: Learning with Adaptive Feedback. 1610-1618 - Changbo Zhu, Huan Xu, Chenlei Leng, Shuicheng Yan:
Convex Optimization Procedure for Clustering: Theoretical Revisit. 1619-1627 - Anqi Wu, Mijung Park, Oluwasanmi Koyejo, Jonathan W. Pillow:
Sparse Bayesian structure learning with dependent relevance determination priors. 1628-1636 - Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell:
Weakly-supervised Discovery of Visual Pattern Configurations. 1637-1645 - Aaron Defazio, Francis R. Bach, Simon Lacoste-Julien:
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. 1646-1654 - Deguang Kong, Ryohei Fujimaki, Ji Liu, Feiping Nie, Chris H. Q. Ding:
Exclusive Feature Learning on Arbitrary Structures via \ell_{1, 2}-norm. 1655-1663 - John J. Bruer, Joel A. Tropp, Volkan Cevher, Stephen Becker:
Time-Data Tradeoffs by Aggressive Smoothing. 1664-1672 - Cong Xie, Ling Yan, Wu-Jun Li, Zhihua Zhang:
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis. 1673-1681 - Mateusz Malinowski, Mario Fritz:
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input. 1682-1690 - Hastagiri P. Vanchinathan, Gábor Bartók, Andreas Krause:
Efficient Partial Monitoring with Prior Information. 1691-1699 - Yariv Dror Mizrahi, Misha Denil, Nando de Freitas:
Distributed Parameter Estimation in Probabilistic Graphical Models. 1700-1708 - Xu Chen, Xiuyuan Cheng, Stéphane Mallat:
Unsupervised Deep Haar Scattering on Graphs. 1709-1717 - Jie Shen, Huan Xu, Ping Li:
Online Optimization for Max-Norm Regularization. 1718-1726 - Jean Lafond, Olga Klopp, Eric Moulines, Joseph Salmon:
Probabilistic low-rank matrix completion on finite alphabets. 1727-1735 - Xianjie Chen, Alan L. Yuille:
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations. 1736-1744 - Michael Riis Andersen, Ole Winther, Lars Kai Hansen:
Bayesian Inference for Structured Spike and Slab Priors. 1745-1753 - Ricardo Henao, Xin Yuan, Lawrence Carin:
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling. 1754-1762 - Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng:
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion. 1763-1771 - Nicholas Ruozzi, Tony Jebara:
Making Pairwise Binary Graphical Models Attractive. 1772-1780 - David P. Woodruff:
Low Rank Approximation Lower Bounds in Row-Update Streams. 1781-1789 - Li Xu, Jimmy S. J. Ren, Ce Liu, Jiaya Jia:
Deep Convolutional Neural Network for Image Deconvolution. 1790-1798 - Jonathan Tompson, Arjun Jain, Yann LeCun, Christoph Bregler:
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation. 1799-1807 - Yichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov:
Learning Generative Models with Visual Attention. 1808-1816 - Rémi Lajugie, Damien Garreau, Francis R. Bach, Sylvain Arlot:
Metric Learning for Temporal Sequence Alignment. 1817-1825 - Avrim Blum, Nika Haghtalab, Ariel D. Procaccia:
Learning Optimal Commitment to Overcome Insecurity. 1826-1834 - Odalric-Ambrym Maillard, Timothy A. Mann, Shie Mannor:
How hard is my MDP?" The distribution-norm to the rescue". 1835-1843 - Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun:
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms. 1844-1852 - A. P. Sarath Chandar, Stanislas Lauly, Hugo Larochelle, Mitesh M. Khapra, Balaraman Ravindran, Vikas C. Raykar, Amrita Saha:
An Autoencoder Approach to Learning Bilingual Word Representations. 1853-1861 - Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön:
Sequential Monte Carlo for Graphical Models. 1862-1870 - Mehryar Mohri, Andres Muñoz Medina:
Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers. 1871-1879 - Agnieszka Grabska-Barwinska, Jonathan W. Pillow:
Optimal prior-dependent neural population codes under shared input noise. 1880-1888 - Andrej Karpathy, Armand Joulin, Li Fei-Fei:
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping. 1889-1897 - Xuezhi Wang, Jeff G. Schneider:
Flexible Transfer Learning under Support and Model Shift. 1898-1906 - Yunpeng Pan, Evangelos A. Theodorou:
Probabilistic Differential Dynamic Programming. 1907-1915 - Aron Yu, Kristen Grauman:
Predicting Useful Neighborhoods for Lazy Local Learning. 1916-1924 - Vincent Michalski, Roland Memisevic, Kishore Reddy Konda:
Modeling Deep Temporal Dependencies with Recurrent "Grammar Cells". 1925-1933 - Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee:
Generalized Dantzig Selector: Application to the k-support norm. 1934-1942 - Yanping Huang, Rajesh P. N. Rao:
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks. 1943-1951 - Been Kim, Cynthia Rudin, Julie A. Shah:
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. 1952-1960 - Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada:
Latent Support Measure Machines for Bag-of-Words Data Classification. 1961-1969 - Jingwei Liang, Jalal Fadili, Gabriel Peyré:
Local Linear Convergence of Forward-Backward under Partial Smoothness. 1970-1978 - Marek Petrik, Dharmashankar Subramanian:
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning. 1979-1987 - Yi Sun, Yuheng Chen, Xiaogang Wang, Xiaoou Tang:
Deep Learning Face Representation by Joint Identification-Verification. 1988-1996 - Trapit Bansal, Chiranjib Bhattacharyya, Ravindran Kannan:
A provable SVD-based algorithm for learning topics in dominant admixture corpus. 1997-2005 - Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Stephen Becker, Peder A. Olsen:
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models. 2006-2014 - Matthias Zöhrer, Franz Pernkopf:
General Stochastic Networks for Classification. 2015-2023 - Cristina Savin, Sophie Denève:
Spatio-temporal Representations of Uncertainty in Spiking Neural Networks. 2024-2032 - Qian Wang, Jiaxing Zhang, Sen Song, Zheng Zhang:
Attentional Neural Network: Feature Selection Using Cognitive Feedback. 2033-2041 - Baotian Hu, Zhengdong Lu, Hang Li, Qingcai Chen:
Convolutional Neural Network Architectures for Matching Natural Language Sentences. 2042-2050 - Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky:
Scalable Non-linear Learning with Adaptive Polynomial Expansions. 2051-2059 - David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin:
On the relations of LFPs & Neural Spike Trains. 2060-2068 - Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei Sha:
Diverse Sequential Subset Selection for Supervised Video Summarization. 2069-2077 - Lu Jiang, Deyu Meng, Shoou-I Yu, Zhen-Zhong Lan, Shiguang Shan, Alexander G. Hauptmann:
Self-Paced Learning with Diversity. 2078-2086 - Bo Li, Yevgeniy Vorobeychik:
Feature Cross-Substitution in Adversarial Classification. 2087-2095 - Ozan Irsoy, Claire Cardie:
Deep Recursive Neural Networks for Compositionality in Language. 2096-2104 - Bruno Conejo, Nikos Komodakis, Sébastien Leprince, Jean-Philippe Avouac:
Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers. 2105-2113 - Neil Houlsby, David M. Blei:
A Filtering Approach to Stochastic Variational Inference. 2114-2122 - Shameem Puthiya Parambath, Nicolas Usunier, Yves Grandvalet:
Optimizing F-Measures by Cost-Sensitive Classification. 2123-2131 - Jie Wang, Jieping Ye:
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets. 2132-2140 - Kihyuk Sohn, Wenling Shang, Honglak Lee:
Improved Multimodal Deep Learning with Variation of Information. 2141-2149 - Charles Kervrann:
PEWA: Patch-based Exponentially Weighted Aggregation for image denoising. 2150-2158 - Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for Graphical Models. 2159-2167 - Cong Han Lim, Stephen J. Wright:
Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems. 2168-2176 - Omer Levy, Yoav Goldberg:
Neural Word Embedding as Implicit Matrix Factorization. 2177-2185 - Mohammad J. Saberian, Nuno Vasconcelos:
Multi-Resolution Cascades for Multiclass Object Detection. 2186-2194 - Xiangyu Wang, Peichao Peng, David B. Dunson:
Median Selection Subset Aggregation for Parallel Inference. 2195-2203 - Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu:
Recurrent Models of Visual Attention. 2204-2212 - Thang D. Bui, Richard E. Turner:
Tree-structured Gaussian Process Approximations. 2213-2221 - Maria-Florina Balcan, Christopher Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, Le Song:
Active Learning and Best-Response Dynamics. 2222-2230 - Dylan Festa, Guillaume Hennequin, Máté Lengyel:
Analog Memories in a Balanced Rate-Based Network of E-I Neurons. 2231-2239 - Guillaume Hennequin, Laurence Aitchison, Máté Lengyel:
Fast Sampling-Based Inference in Balanced Neuronal Networks. 2240-2248 - Y. Cem Sübakan, Johannes Traa, Paris Smaragdis:
Spectral Learning of Mixture of Hidden Markov Models. 2249-2257 - Haim Avron, Huy L. Nguyen, David P. Woodruff:
Subspace Embeddings for the Polynomial Kernel. 2258-2266 - Tofigh Naghibi Mohamadpoor, Beat Pfister:
A Boosting Framework on Grounds of Online Learning. 2267-2275 - Sina Tootoonian, Máté Lengyel:
A Dual Algorithm for Olfactory Computation in the Locust Brain. 2276-2284 - Siqi Nie, Denis Deratani Mauá, Cassio P. de Campos, Qiang Ji:
Advances in Learning Bayesian Networks of Bounded Treewidth. 2285-2293 - Wouter M. Koolen, Tim van Erven, Peter Grünwald:
Learning the Learning Rate for Prediction with Expert Advice. 2294-2302 - Rashish Tandon, Karthikeyan Shanmugam, Pradeep Ravikumar, Alexandros G. Dimakis:
On the Information Theoretic Limits of Learning Ising Models. 2303-2311 - Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar:
Efficient Optimization for Average Precision SVM. 2312-2320 - Anshumali Shrivastava, Ping Li:
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS). 2321-2329 - Scott W. Linderman, Christopher H. Stock, Ryan P. Adams:
A framework for studying synaptic plasticity with neural spike train data. 2330-2338 - Huining Hu, Zhentao Li, Adrian R. Vetta:
Randomized Experimental Design for Causal Graph Discovery. 2339-2347 - Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov:
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations. 2348-2356 - Kustaa Kangas, Mikko Koivisto, Teppo Mikael Niinimäki:
Learning Chordal Markov Networks by Dynamic Programming. 2357-2365 - David Eigen, Christian Puhrsch, Rob Fergus:
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network. 2366-2374 - Kai Zhong, Ian En-Hsu Yen, Inderjit S. Dhillon, Pradeep Ravikumar:
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators. 2375-2383 - Bahadir Ozdemir, Larry S. Davis:
A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process. 2384-2392 - Peter J. Sadowski, Daniel Whiteson, Pierre Baldi:
Searching for Higgs Boson Decay Modes with Deep Learning. 2393-2401 - Xu Sun:
Structure Regularization for Structured Prediction. 2402-2410 - Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun:
On Multiplicative Multitask Feature Learning. 2411-2419 - Kevin R. Moon, Alfred O. Hero III:
Multivariate f-divergence Estimation With Confidence. 2420-2428 - Zhen Cui, Hong Chang, Shiguang Shan, Xilin Chen:
Generalized Unsupervised Manifold Alignment. 2429-2437 - Stephan Mandt, David M. Blei:
Smoothed Gradients for Stochastic Variational Inference. 2438-2446 - Abhishek Sharma, Oncel Tuzel, Ming-Yu Liu:
Recursive Context Propagation Network for Semantic Scene Labeling. 2447-2455 - Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep Ravikumar, Inderjit S. Dhillon:
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space. 2456-2464 - Kaustubh R. Patil, Xiaojin Zhu, Lukasz Kopec, Bradley C. Love:
Optimal Teaching for Limited-Capacity Human Learners. 2465-2473 - Mehrdad Farajtabar, Nan Du, Manuel Gomez-Rodriguez, Isabel Valera, Hongyuan Zha, Le Song:
Shaping Social Activity by Incentivizing Users. 2474-2482 - Kyle R. Ulrich, David E. Carlson, Wenzhao Lian, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin:
Analysis of Brain States from Multi-Region LFP Time-Series. 2483-2491 - Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman:
Reputation-based Worker Filtering in Crowdsourcing. 2492-2500 - Vitaly Kuznetsov, Mehryar Mohri, Umar Syed:
Multi-Class Deep Boosting. 2501-2509 - Weijie Su, Stephen P. Boyd, Emmanuel J. Candès:
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights. 2510-2518 - Bilal Piot, Matthieu Geist, Olivier Pietquin:
Difference of Convex Functions Programming for Reinforcement Learning. 2519-2527 - Kimberly L. Stachenfeld, Matthew M. Botvinick, Samuel Gershman:
Design Principles of the Hippocampal Cognitive Map. 2528-2536 - Robert Gens, Pedro M. Domingos:
Deep Symmetry Networks. 2537-2545 - William Vega-Brown, Marek Doniec, Nicholas Roy:
Nonparametric Bayesian inference on multivariate exponential families. 2546-2554 - Sanjoy Dasgupta, Samory Kpotufe:
Optimal rates for k-NN density and mode estimation. 2555-2563 - Matthew Lawlor, Steven W. Zucker:
Feedforward Learning of Mixture Models. 2564-2572 - Albert Xin Jiang, Leandro Soriano Marcolino, Ariel D. Procaccia, Tuomas Sandholm, Nisarg Shah, Milind Tambe:
Diverse Randomized Agents Vote to Win. 2573-2581 - Hyokun Yun, Parameswaran Raman, S. V. N. Vishwanathan:
Ranking via Robust Binary Classification. 2582-2590 - MohammadHossein Bateni, Aditya Bhaskara, Silvio Lattanzi, Vahab S. Mirrokni:
Distributed Balanced Clustering via Mapping Coresets. 2591-2599 - Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Craig Pocock, Stephen J. Green, Guy L. Steele Jr.:
Augur: Data-Parallel Probabilistic Modeling. 2600-2608 - Pranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan:
Learning Mixtures of Ranking Models. 2609-2617 - Yu Xin, Tommi S. Jaakkola:
Controlling privacy in recommender systems. 2618-2626 - Julien Mairal, Piotr Koniusz, Zaïd Harchaoui, Cordelia Schmid:
Convolutional Kernel Networks. 2627-2635 - Chongjie Zhang, Julie A. Shah:
Fairness in Multi-Agent Sequential Decision-Making. 2636-2644 - Adarsh Prasad, Stefanie Jegelka, Dhruv Batra:
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets. 2645-2653 - Jimmy Ba, Rich Caruana:
Do Deep Nets Really Need to be Deep? 2654-2662 - Shaobo Han, Lin Du, Esther Salazar, Lawrence Carin:
Dynamic Rank Factor Model for Text Streams. 2663-2671 - Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio:
Generative Adversarial Nets. 2672-2680 - De Wen Soh, Sekhar Tatikonda:
Testing Unfaithful Gaussian Graphical Models. 2681-2689 - Jasper De Bock, Cassio P. de Campos, Alessandro Antonucci:
Global Sensitivity Analysis for MAP Inference in Graphical Models. 2690-2698 - Charles Y. Zheng, Franco Pestilli, Ariel Rokem:
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain. 2699-2707 - Jing Qian, Venkatesh Saligrama:
Efficient Minimax Signal Detection on Graphs. 2708-2716 - Yash Deshpande, Andrea Montanari, Emile Richard:
Cone-Constrained Principal Component Analysis. 2717-2725 - Ankit Garg, Tengyu Ma, Huy L. Nguyen:
On Communication Cost of Distributed Statistical Estimation and Dimensionality. 2726-2734 - Hau Chan, Luis E. Ortiz:
Computing Nash Equilibria in Generalized Interdependent Security Games. 2735-2743 - Oluwasanmi Koyejo, Nagarajan Natarajan, Pradeep Ravikumar, Inderjit S. Dhillon:
Consistent Binary Classification with Generalized Performance Metrics. 2744-2752 - Dohyung Park, Constantine Caramanis, Sujay Sanghavi:
Greedy Subspace Clustering. 2753-2761 - William E. Bishop, Byron M. Yu:
Deterministic Symmetric Positive Semidefinite Matrix Completion. 2762-2770 - Hanie Sedghi, Anima Anandkumar, Edmond A. Jonckheere:
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition. 2771-2779 - Gergely Neu, Michal Valko:
Online combinatorial optimization with stochastic decision sets and adversarial losses. 2780-2788 - Tom Gunter, Michael A. Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts:
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature. 2789-2797 - Si Si, Donghyuk Shin, Inderjit S. Dhillon, Beresford N. Parlett:
Multi-Scale Spectral Decomposition of Massive Graphs. 2798-2806 - Michal Derezinski, Manfred K. Warmuth:
The limits of squared Euclidean distance regularization. 2807-2815 - Huahua Wang, Arindam Banerjee:
Bregman Alternating Direction Method of Multipliers. 2816-2824 - Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka:
Multitask learning meets tensor factorization: task imputation via convex optimization. 2825-2833 - Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing:
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning. 2834-2842 - Alyson K. Fletcher, Sundeep Rangan:
Scalable Inference for Neuronal Connectivity from Calcium Imaging. 2843-2851 - Guy Bresler, David Gamarnik, Devavrat Shah:
Structure learning of antiferromagnetic Ising models. 2852-2860 - Moritz Hardt, Eric Price:
The Noisy Power Method: A Meta Algorithm with Applications. 2861-2869 - Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Thomas L. Griffiths:
Algorithm selection by rational metareasoning as a model of human strategy selection. 2870-2878 - Peter Kairouz, Sewoong Oh, Pramod Viswanath:
Extremal Mechanisms for Local Differential Privacy. 2879-2887 - Romain Paulus, Richard Socher, Christopher D. Manning:
Global Belief Recursive Neural Networks. 2888-2896 - Emile Richard, Andrea Montanari:
A statistical model for tensor PCA. 2897-2905 - Shreya Saxena, Munther A. Dahleh:
Real-Time Decoding of an Integrate and Fire Encoder. 2906-2914 - H. Brendan McMahan, Matthew J. Streeter:
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning. 2915-2923 - Guido Montúfar, Razvan Pascanu, KyungHyun Cho, Yoshua Bengio:
On the Number of Linear Regions of Deep Neural Networks. 2924-2932 - Yann N. Dauphin, Razvan Pascanu, Çaglar Gülçehre, KyungHyun Cho, Surya Ganguli, Yoshua Bengio:
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. 2933-2941 - João D. Semedo, Amin Zandvakili, Adam Kohn, Christian K. Machens, Byron M. Yu:
Extracting Latent Structure From Multiple Interacting Neural Populations. 2942-2950 - Qichao Que, Mikhail Belkin, Yusu Wang:
Learning with Fredholm Kernels. 2951-2959 - Michalis K. Titsias, Christopher Yau:
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models. 2960-2968 - Yuri Grinberg, Doina Precup, Michel Gendreau:
Optimizing Energy Production Using Policy Search and Predictive State Representations. 2969-2977 - Deepak Venugopal, Vibhav Gogate:
Scaling-up Importance Sampling for Markov Logic Networks. 2978-2986 - Alex K. Susemihl, Ron Meir, Manfred Opper:
Optimal Neural Codes for Control and Estimation. 2987-2995 - Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi:
Graph Clustering With Missing Data: Convex Algorithms and Analysis. 2996-3004 - Haichao Zhang, Jianchao Yang:
Scale Adaptive Blind Deblurring. 3005-3013 - Ashique Rupam Mahmood, Hado van Hasselt, Richard S. Sutton:
Weighted importance sampling for off-policy learning with linear function approximation. 3014-3022 - Shariq A. Mobin, James A. Arnemann, Fritz Sommer:
Information-based learning by agents in unbounded state spaces. 3023-3031 - Shashank Singh, Barnabás Póczos:
Exponential Concentration of a Density Functional Estimator. 3032-3040 - Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina Balcan, Le Song:
Scalable Kernel Methods via Doubly Stochastic Gradients. 3041-3049 - João F. Henriques, Pedro Martins, Rui F. Caseiro, Jorge Batista:
Fast Training of Pose Detectors in the Fourier Domain. 3050-3058 - Qihang Lin, Zhaosong Lu, Lin Xiao:
An Accelerated Proximal Coordinate Gradient Method. 3059-3067 - Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. 3068-3076 - Roy Frostig, Sida Wang, Percy Liang, Christopher D. Manning:
Simple MAP Inference via Low-Rank Relaxations. 3077-3085 - Chris J. Maddison, Daniel Tarlow, Tom Minka:
A* Sampling. 3086-3094 - Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke:
A Bayesian model for identifying hierarchically organised states in neural population activity. 3095-3103 - Ilya Sutskever, Oriol Vinyals, Quoc V. Le:
Sequence to Sequence Learning with Neural Networks. 3104-3112 - Yingyu Liang, Maria-Florina Balcan, Vandana Kanchanapally, David P. Woodruff:
Improved Distributed Principal Component Analysis. 3113-3121 - Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G. Dimakis, Adam R. Klivans:
Sparse Polynomial Learning and Graph Sketching. 3122-3130 - Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein:
Tight Continuous Relaxation of the Balanced k-Cut Problem. 3131-3139 - Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Mondrian Forests: Efficient Online Random Forests. 3140-3148 - Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, Benjamin Taskar:
Expectation-Maximization for Learning Determinantal Point Processes. 3149-3157 - David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon:
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs. 3158-3166 - Se-Young Yun, Marc Lelarge, Alexandre Proutière:
Streaming, Memory Limited Algorithms for Community Detection. 3167-3175 - Prem Gopalan, Laurent Charlin, David M. Blei:
Content-based recommendations with Poisson factorization. 3176-3184 - Hossein Azari Soufiani, David C. Parkes, Lirong Xia:
A Statistical Decision-Theoretic Framework for Social Choice. 3185-3193 - Jianbo Yang, Xuejun Liao, Minhua Chen, Lawrence Carin:
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices. 3194-3202 - Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D. Skeel, Hartmut Neven:
Bayesian Sampling Using Stochastic Gradient Thermostats. 3203-3211 - Calvin McCarter, Seyoung Kim:
On Sparse Gaussian Chain Graph Models. 3212-3220 - Renato Negrinho, Andre Martins:
Orbit Regularization. 3221-3229 - Wouter M. Koolen, Alan Malek, Peter L. Bartlett:
Efficient Minimax Strategies for Square Loss Games. 3230-3238 - Miles Lopes:
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs. 3239-3247 - Alex Kantchelian, Michael Carl Tschantz, Ling Huang, Peter L. Bartlett, Anthony D. Joseph, J. Doug Tygar:
Large-Margin Convex Polytope Machine. 3248-3256 - Yarin Gal, Mark van der Wilk, Carl E. Rasmussen:
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. 3257-3265 - Vivek Srikumar, Christopher D. Manning:
Learning Distributed Representations for Structured Output Prediction. 3266-3274 - Özlem Aslan, Xinhua Zhang, Dale Schuurmans:
Convex Deep Learning via Normalized Kernels. 3275-3283 - Emile Richard, Guillaume Obozinski, Jean-Philippe Vert:
Tight convex relaxations for sparse matrix factorization. 3284-3292 - He He, Hal Daumé III, Jason Eisner:
Learning to Search in Branch and Bound Algorithms. 3293-3301 - Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate:
An Integer Polynomial Programming Based Framework for Lifted MAP Inference. 3302-3310 - Waleed Ammar, Chris Dyer, Noah A. Smith:
Conditional Random Field Autoencoders for Unsupervised Structured Prediction. 3311-3319 - Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson:
How transferable are features in deep neural networks? 3320-3328 - Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu:
Accelerated Mini-batch Randomized Block Coordinate Descent Method. 3329-3337 - Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang:
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning. 3338-3346 - Guy Bresler, George H. Chen, Devavrat Shah:
A Latent Source Model for Online Collaborative Filtering. 3347-3355 - Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang:
Distributed Bayesian Posterior Sampling via Moment Sharing. 3356-3364 - Philip Bachman, Ouais Alsharif, Doina Precup:
Learning with Pseudo-Ensembles. 3365-3373 - Nan Du, Yingyu Liang, Maria-Florina Balcan, Le Song:
Learning Time-Varying Coverage Functions. 3374-3382 - Zhaoran Wang, Huanran Lu, Han Liu:
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time. 3383-3391 - Shubhendu Trivedi, David A. McAllester, Greg Shakhnarovich:
Discriminative Metric Learning by Neighborhood Gerrymandering. 3392-3400 - Qing Qu, Ju Sun, John Wright:
Finding a sparse vector in a subspace: Linear sparsity using alternating directions. 3401-3409 - Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh:
Asynchronous Anytime Sequential Monte Carlo. 3410-3418 - Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang:
Discrete Graph Hashing. 3419-3427 - Stefan Wager, Nick Chamandy, Omkar Muralidharan, Amir Najmi:
Feedback Detection for Live Predictors. 3428-3436 - Kamalika Chaudhuri, Sanjoy Dasgupta:
Rates of Convergence for Nearest Neighbor Classification. 3437-3445 - Daniel Berend, Aryeh Kontorovich:
Consistency of weighted majority votes. 3446-3454 - Mingyuan Zhou:
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling. 3455-3463 - Dinesh Jayaraman, Kristen Grauman:
Zero-shot recognition with unreliable attributes. 3464-3472 - Po-Ling Loh, Andre Wibisono:
Concavity of reweighted Kikuchi approximation. 3473-3481 - Arun Rajkumar, Shivani Agarwal:
Online Decision-Making in General Combinatorial Spaces. 3482-3490 - Mohammad Taha Bahadori, Qi Rose Yu, Yan Liu:
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning. 3491-3499 - Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski:
Clustered factor analysis of multineuronal spike data. 3500-3508 - Yinlam Chow, Mohammad Ghavamzadeh:
Algorithms for CVaR Optimization in MDPs. 3509-3517 - Aäron van den Oord, Benjamin Schrauwen:
Factoring Variations in Natural Images with Deep Gaussian Mixture Models. 3518-3526 - Hidekazu Oiwa, Ryohei Fujimaki:
Partition-wise Linear Models. 3527-3535 - Judy Hoffman, Sergio Guadarrama, Eric Tzeng, Ronghang Hu, Jeff Donahue, Ross B. Girshick, Trevor Darrell, Kate Saenko:
LSDA: Large Scale Detection through Adaptation. 3536-3544 - Marijn F. Stollenga, Jonathan Masci, Faustino J. Gomez, Jürgen Schmidhuber:
Deep Networks with Internal Selective Attention through Feedback Connections. 3545-3553 - Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q. Ngo, XuanLong Nguyen, Christopher Ré, Venu Govindaraju:
Parallel Feature Selection Inspired by Group Testing. 3554-3562 - Cédric Févotte, Matthieu Kowalski:
Low-Rank Time-Frequency Synthesis. 3563-3571 - Luca Pasa, Alessandro Sperduti:
Pre-training of Recurrent Neural Networks via Linear Autoencoders. 3572-3580 - Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling:
Semi-supervised Learning with Deep Generative Models. 3581-3589 - Mingjun Zhong, Nigel H. Goddard, Charles Sutton:
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation. 3590-3598 - Nicholas J. Foti, Jason Xu, Dillon Laird, Emily B. Fox:
Stochastic variational inference for hidden Markov models. 3599-3607 - Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton:
A Wild Bootstrap for Degenerate Kernel Tests. 3608-3616 - Luke J. O'Connor, Soheil Feizi:
Biclustering Usinig Message Passing. 3617-3625 - Andrew Gordon Wilson, Elad Gilboa, John P. Cunningham, Arye Nehorai:
Fast Kernel Learning for Multidimensional Pattern Extrapolation. 3626-3634 - Rakesh Shivanna, Chiranjib Bhattacharyya:
Learning on graphs using Orthonormal Representation is Statistically Consistent. 3635-3643 - Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos:
Spectral k-Support Norm Regularization. 3644-3652 - Pietro Vertechi, Wieland Brendel, Christian K. Machens:
Unsupervised learning of an efficient short-term memory network. 3653-3661 - Yuancheng Zhu, John D. Lafferty:
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls. 3662-3670 - Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik, Jonathan D. Chang:
Learning a Concept Hierarchy from Multi-labeled Documents. 3671-3679 - Roger Frigola, Yutian Chen, Carl E. Rasmussen:
Variational Gaussian Process State-Space Models. 3680-3688 - Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Fast Prediction for Large-Scale Kernel Machines. 3689-3697
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