The NIPS 2014 proceedings are out, what paper did you fancy ?

## Advances in Neural Information Processing Systems 27 (NIPS 2014)

The papers below appear in Advances in Neural Information Processing Systems 27 edited by Z. Ghahramani and M. Welling and C. Cortes and N.D. Lawrence and K.Q. Weinberger.They are proceedings from the conference Neural Information Processing Systems 2014.

- Kernel Mean Estimation via Spectral Filtering Krikamol Muandet, Bharath Sriperumbudur, Bernhard Schölkopf
- Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models Yichuan Zhang, Charles Sutton
- Communication Efficient Distributed Machine Learning with the Parameter Server Mu Li, David G. Andersen, Alex J. Smola, Kai Yu
- The Infinite Mixture of Infinite Gaussian Mixtures Halid Z. Yerebakan, Bartek Rajwa, Murat Dundar
- Robust Classification Under Sample Selection Bias Anqi Liu, Brian Ziebart
- Zeta Hull Pursuits: Learning Nonconvex Data Hulls Yuanjun Xiong, Wei Liu, Deli Zhao, Xiaoou Tang
- Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction Katerina Fragkiadaki, Marta Salas, Pablo Arbelaez, Jitendra Malik
- Sparse Space-Time Deconvolution for Calcium Image Analysis Ferran Diego Andilla, Fred A. Hamprecht
- Restricted Boltzmann machines modeling human choice Takayuki Osogami, Makoto Otsuka
- Multiscale Fields of Patterns Pedro Felzenszwalb, John G. Oberlin
- large scale canonical correlation analysis with iterative least squares Yichao Lu, Dean P. Foster
- Altitude Training: Strong Bounds for Single-Layer Dropout Stefan Wager, William Fithian, Sida Wang, Percy S. Liang
- Rounding-based Moves for Metric Labeling M. Pawan Kumar
- Parallel Double Greedy Submodular Maximization Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, Michael I. Jordan
- Multivariate Regression with Calibration Han Liu, Lie Wang, Tuo Zhao
- Exact Post Model Selection Inference for Marginal Screening Jason D. Lee, Jonathan E. Taylor
- On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification Yingzhen Yang, Feng Liang, Shuicheng Yan, Zhangyang Wang, Thomas S. Huang
- Just-In-Time Learning for Fast and Flexible Inference S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn
- Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation Ohad Shamir
- Quantized Kernel Learning for Feature Matching Danfeng Qin, Xuanli Chen, Matthieu Guillaumin, Luc V. Gool
- Parallel Direction Method of Multipliers Huahua Wang, Arindam Banerjee, Zhi-Quan Luo
- (Almost) No Label No Cry Giorgio Patrini, Richard Nock, Tiberio Caetano, Paul Rivera
- Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards Yonatan Gur, Assaf Zeevi, Omar Besbes
- Object Localization based on Structural SVM using Privileged Information Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han
- Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang
- Shape and Illumination from Shading using the Generic Viewpoint Assumption Daniel Zoran, Dilip Krishnan, Jose Bento, Bill Freeman
- Parallel Sampling of HDPs using Sub-Cluster Splits Jason Chang, John W. Fisher III
- From MAP to Marginals: Variational Inference in Bayesian Submodular Models Josip Djolonga, Andreas Krause
- Robust Logistic Regression and Classification Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
- Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities Tianbao Yang, Rong Jin
- A Unified Semantic Embedding: Relating Taxonomies and Attributes Sung Ju Hwang, Leonid Sigal
- Transportability from Multiple Environments with Limited Experiments: Completeness Results Elias Bareinboim, Judea Pearl
- Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
- Causal Inference through a Witness Protection Program Ricardo Silva, Robin Evans
- Incremental Clustering: The Case for Extra Clusters Margareta Ackerman, Sanjoy Dasgupta
- Multi-scale Graphical Models for Spatio-Temporal Processes firdaus janoos, Huseyin Denli, Niranjan Subrahmanya
- Iterative Neural Autoregressive Distribution Estimator NADE-k Tapani Raiko, Yao Li, Kyunghyun Cho, Yoshua Bengio
- Sparse PCA via Covariance Thresholding Yash Deshpande, Andrea Montanari
- Low-dimensional models of neural population activity in sensory cortical circuits Evan W. Archer, Urs Koster, Jonathan W. Pillow, Jakob H. Macke
- A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System Yuanyuan Mi, Luozheng Li, Dahui Wang, Si Wu
- A Representation Theory for Ranking Functions Harsh H. Pareek, Pradeep K. Ravikumar
- Near-optimal sample compression for nearest neighbors Lee-Ad Gottlieb, Aryeh Kontorovitch, Pinhas Nisnevitch
- Combinatorial Pure Exploration of Multi-Armed Bandits Shouyuan Chen, Tian Lin, Irwin King, Michael R. Lyu, Wei Chen
- Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces Minh Ha Quang, Marco San Biagio, Vittorio Murino
- Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model Debarghya Ghoshdastidar, Ambedkar Dukkipati
- Spectral Clustering of graphs with the Bethe Hessian Alaa Saade, Florent Krzakala, Lenka Zdeborova
- Fast and Robust Least Squares Estimation in Corrupted Linear Models Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann
- Local Decorrelation For Improved Pedestrian Detection Woonhyun Nam, Piotr Dollar, Joon Hee Han
- Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space Robert A. Vandermeulen, Clayton Scott
- Beyond Disagreement-Based Agnostic Active Learning Chicheng Zhang, Kamalika Chaudhuri
- Bayes-Adaptive Simulation-based Search with Value Function Approximation Arthur Guez, Nicolas Heess, David Silver, Peter Dayan
- 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
- Active Regression by Stratification Sivan Sabato, Remi Munos
- Sensory Integration and Density Estimation Joseph G. Makin, Philip N. Sabes
- Learning Deep Features for Scene Recognition using Places Database Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva
- A Complete Variational Tracker Ryan D. Turner, Steven Bottone, Bhargav Avasarala
- Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks Yuanyuan Mi, C. C. Alan Fung, K. Y. Michael Wong, Si Wu
- Efficient Sampling for Learning Sparse Additive Models in High Dimensions Hemant Tyagi, Bernd Gärtner, Andreas Krause
- Deep Joint Task Learning for Generic Object Extraction Xiaolong Wang, Liliang Zhang, Liang Lin, Zhujin Liang, Wangmeng Zuo
- Robust Bayesian Max-Margin Clustering Changyou Chen, Jun Zhu, Xinhua Zhang
- Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision Deepti Pachauri, Risi Kondor, Gautam Sargur, Vikas Singh
- Bounded Regret for Finite-Armed Structured Bandits Tor Lattimore, Remi Munos
- Coresets for k-Segmentation of Streaming Data Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher III, Daniela Rus
- Two-Stream Convolutional Networks for Action Recognition in Videos Karen Simonyan, Andrew Zisserman
- Discovering Structure in High-Dimensional Data Through Correlation Explanation Greg Ver Steeg, Aram Galstyan
- Positive Curvature and Hamiltonian Monte Carlo Christof Seiler, Simon Rubinstein-Salzedo, Susan Holmes
- Learning Mixed Multinomial Logit Model from Ordinal Data Sewoong Oh, Devavrat Shah
- Near-optimal Reinforcement Learning in Factored MDPs Ian Osband, Benjamin Van Roy
- Efficient learning by implicit exploration in bandit problems with side observations Tomáš Kocák, Gergely Neu, Michal Valko, Remi Munos
- Repeated Contextual Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed
- Recursive Inversion Models for Permutations Christopher Meek, Marina Meila
- On the Convergence Rate of Decomposable Submodular Function Minimization Robert Nishihara, Stefanie Jegelka, Michael I. Jordan
- New Rules for Domain Independent Lifted MAP Inference Happy Mittal, Prasoon Goyal, Vibhav G. Gogate, Parag Singla
- PAC-Bayesian AUC classification and scoring James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang
- Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection Sang Oh, Onkar Dalal, Kshitij Khare, Bala Rajaratnam
- On Prior Distributions and Approximate Inference for Structured Variables Oluwasanmi O. Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell Poldrack
- On Iterative Hard Thresholding Methods for High-dimensional M-Estimation Prateek Jain, Ambuj Tewari, Purushottam Kar
- Online and Stochastic Gradient Methods for Non-decomposable Loss Functions Purushottam Kar, Harikrishna Narasimhan, Prateek Jain
- Analysis of Learning from Positive and Unlabeled Data Marthinus C. du Plessis, Gang Niu, Masashi Sugiyama
- Dimensionality Reduction with Subspace Structure Preservation Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju
- Constrained convex minimization via model-based excessive gap Quoc Tran-Dinh, Volkan Cevher
- Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data Florian Stimberg, Andreas Ruttor, Manfred Opper
- Probabilistic ODE Solvers with Runge-Kutta Means Michael Schober, David K. Duvenaud, Philipp Hennig
- Optimal decision-making with time-varying evidence reliability Jan Drugowitsch, Ruben Moreno-Bote, Alexandre Pouget
- Learning Shuffle Ideals Under Restricted Distributions Dongqu Chen
- Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox
- Distance-Based Network Recovery under Feature Correlation David Adametz, Volker Roth
- Bandit Convex Optimization: Towards Tight Bounds Elad Hazan, Kfir Levy
- Projective dictionary pair learning for pattern classification Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng
- Provable Submodular Minimization using Wolfe's Algorithm Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari
- Exploiting easy data in online optimization Amir Sani, Gergely Neu, Alessandro Lazaric
- Sparse Multi-Task Reinforcement Learning Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
- Best-Arm Identification in Linear Bandits Marta Soare, Alessandro Lazaric, Remi Munos
- Mind the Nuisance: Gaussian Process Classification using Privileged Noise Daniel Hernández-lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto
- Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology Remi Lemonnier, Kevin Scaman, Nicolas Vayatis
- On the Computational Efficiency of Training Neural Networks Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
- Self-Adaptable Templates for Feature Coding Xavier Boix, Gemma Roig, Salomon Diether, Luc V. Gool
- 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
- Stochastic Network Design in Bidirected Trees xiaojian wu, Daniel R. Sheldon, Shlomo Zilberstein
- Learning convolution filters for inverse covariance estimation of neural network connectivity George Mohler
- SerialRank: Spectral Ranking using Seriation Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
- Clamping Variables and Approximate Inference Adrian Weller, Tony Jebara
- Predictive Entropy Search for Efficient Global Optimization of Black-box Functions José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
- A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation Eran Treister, Javier S. Turek
- Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials Shenlong Wang, Alex Schwing, Raquel Urtasun
- Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich
- Inferring synaptic conductances from spike trains with a biophysically inspired point process model Kenneth W. Latimer, E. J. Chichilnisky, Fred Rieke, Jonathan W. Pillow
- Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights Daniel Soudry, Itay Hubara, Ron Meir
- Incremental Local Gaussian Regression Franziska Meier, Philipp Hennig, Stefan Schaal
- General Table Completion using a Bayesian Nonparametric Model Isabel Valera, Zoubin Ghahramani
- Universal Option Models hengshuai yao, Csaba Szepesvari, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar
- Approximating Hierarchical MV-sets for Hierarchical Clustering Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch
- Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings En-Hsu Yen, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
- Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm Deanna Needell, Rachel Ward, Nati Srebro
- A Framework for Testing Identifiability of Bayesian Models of Perception Luigi Acerbi, Wei Ji Ma, Sethu Vijayakumar
- Optimistic Planning in Markov Decision Processes Using a Generative Model Balázs Szörényi, Gunnar Kedenburg, Remi Munos
- Gaussian Process Volatility Model Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani
- A Safe Screening Rule for Sparse Logistic Regression Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye
- Hardness of parameter estimation in graphical models Guy Bresler, David Gamarnik, Devavrat Shah
- Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics Sergey Levine, Pieter Abbeel
- Magnitude-sensitive preference formation` Nisheeth Srivastava, Ed Vul, Paul R. Schrater
- Extreme bandits Alexandra Carpentier, Michal Valko
- Distributed Estimation, Information Loss and Exponential Families Qiang Liu, Alex T. Ihler
- Non-convex Robust PCA Praneeth Netrapalli, Niranjan U N, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain
- Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning Francesco Orabona
- Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm Jun Zhu, Junhua Mao, Alan L. Yuille
- Message Passing Inference for Large Scale Graphical Models with High Order Potentials Jian Zhang, Alex Schwing, Raquel Urtasun
- Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors Lingqiao Liu, Chunhua Shen, Lei Wang, Anton van den Hengel, Chao Wang
- Dependent nonparametric trees for dynamic hierarchical clustering Kumar Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing
- Causal Strategic Inference in Networked Microfinance Economies Mohammad T. Irfan, Luis E. Ortiz
- Learning Multiple Tasks in Parallel with a Shared Annotator Haim Cohen, Koby Crammer
- Reducing the Rank in Relational Factorization Models by Including Observable Patterns Maximilian Nickel, Xueyan Jiang, Volker Tresp
- Clustering from Labels and Time-Varying Graphs Shiau Hong Lim, Yudong Chen, Huan Xu
- From Stochastic Mixability to Fast Rates Nishant A. Mehta, Robert C. Williamson
- Recovery of Coherent Data via Low-Rank Dictionary Pursuit Guangcan Liu, Ping Li
- Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit Karin C. Knudson, Jacob Yates, Alexander Huk, Jonathan W. Pillow
- Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi
- Discovering, Learning and Exploiting Relevance Cem Tekin, Mihaela Van Der Schaar
- Divide-and-Conquer Learning by Anchoring a Conical Hull Tianyi Zhou, Jeff A. Bilmes, Carlos Guestrin
- Extended and Unscented Gaussian Processes Daniel M. Steinberg, Edwin V. Bonilla
- Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan
- Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation Emily L. Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus
- Learning to Discover Efficient Mathematical Identities Wojciech Zaremba, Karol Kurach, Rob Fergus
- The Large Margin Mechanism for Differentially Private Maximization Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
- DFacTo: Distributed Factorization of Tensors Joon Hee Choi, S. Vishwanathan
- Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology Mehmet Gönen, Adam A. Margolin
- Conditional Swap Regret and Conditional Correlated Equilibrium Mehryar Mohri, Scott Yang
- Mode Estimation for High Dimensional Discrete Tree Graphical Models Chao Chen, Han Liu, Dimitris Metaxas, Tianqi Zhao
- Large-scale L-BFGS using MapReduce Weizhu Chen, Zhenghao Wang, Jingren Zhou
- Submodular Attribute Selection for Action Recognition in Video Jingjing Zheng, Zhuolin Jiang, Rama Chellappa, Jonathon P. Phillips
- Efficient Structured Matrix Rank Minimization Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime Carbonell, Suvrit Sra
- On Integrated Clustering and Outlier Detection Lionel Ott, Linsey Pang, Fabio T. Ramos, Sanjay Chawla
- A Drifting-Games Analysis for Online Learning and Applications to Boosting Haipeng Luo, Robert E. Schapire
- Projecting Markov Random Field Parameters for Fast Mixing Xianghang Liu, Justin Domke
- Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning Robert V. Lindsey, Mohammad Khajah, Michael C. Mozer
- Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya, Ashkan Jafarpour
- Automated Variational Inference for Gaussian Process Models Trung V. Nguyen, Edwin V. Bonilla
- Learning Mixtures of Submodular Functions for Image Collection Summarization Sebastian Tschiatschek, Rishabh K. Iyer, Haochen Wei, Jeff A. Bilmes
- Robust Tensor Decomposition with Gross Corruption Quanquan Gu, Huan Gui, Jiawei Han
- Provable Tensor Factorization with Missing Data Prateek Jain, Sewoong Oh
- Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, Jong-Shi Pang
- Using Convolutional Neural Networks to Recognize Rhythm ￼Stimuli from Electroencephalography Recordings Sebastian Stober, Daniel J. Cameron, Jessica A. Grahn
- Blossom Tree Graphical Models Zhe Liu, John Lafferty
- Model-based Reinforcement Learning and the Eluder Dimension Ian Osband, Benjamin Van Roy
- Minimax-optimal Inference from Partial Rankings Bruce Hajek, Sewoong Oh, Jiaming Xu
- Spectral Methods for Indian Buffet Process Inference Hsiao-Yu Tung, Alex J. Smola
- On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures Harikrishna Narasimhan, Rohit Vaish, Shivani Agarwal
- Top Rank Optimization in Linear Time Nan Li, Rong Jin, Zhi-Hua Zhou
- Spectral Methods for Supervised Topic Models Yining Wang, Jun Zhu
- Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data Karthika Mohan, Judea Pearl
- Sparse PCA with Oracle Property Quanquan Gu, Zhaoran Wang, Han Liu
- Unsupervised Transcription of Piano Music Taylor Berg-Kirkpatrick, Jacob Andreas, Dan Klein
- Decoupled Variational Gaussian Inference Mohammad E. Khan
- Estimation with Norm Regularization Arindam Banerjee, Sheng Chen, Farideh Fazayeli, Vidyashankar Sivakumar
- Decomposing Parameter Estimation Problems Khaled S. Refaat, Arthur Choi, Adnan Darwiche
- Stochastic Proximal Gradient Descent with Acceleration Techniques Atsushi Nitanda
- Learning to Optimize via Information-Directed Sampling Dan Russo, Benjamin Van Roy
- Covariance shrinkage for autocorrelated data Daniel Bartz, Klaus-Robert Müller
- Do Convnets Learn Correspondence? Jonathan L. Long, Ning Zhang, Trevor Darrell
- The Blinded Bandit: Learning with Adaptive Feedback Ofer Dekel, Elad Hazan, Tomer Koren
- Convex Optimization Procedure for Clustering: Theoretical Revisit Changbo Zhu, Huan Xu, Chenlei Leng, Shuicheng Yan
- Sparse Bayesian structure learning with “dependent relevance determination” priors Anqi Wu, Mijung Park, Oluwasanmi O. Koyejo, Jonathan W. Pillow
- Weakly-supervised Discovery of Visual Pattern Configurations Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell
- SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio, Francis Bach, Simon Lacoste-Julien
- Exclusive Feature Learning on Arbitrary Structures via
`\ell_{1,2}`-norm Deguang Kong, Ryohei Fujimaki, Ji Liu, Feiping Nie, Chris Ding - Time--Data Tradeoffs by Aggressive Smoothing John J. Bruer, Joel A. Tropp, Volkan Cevher, Stephen Becker
- Distributed Power-law Graph Computing: Theoretical and Empirical Analysis Cong Xie, Ling Yan, Wu-Jun Li, Zhihua Zhang
- A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input Mateusz Malinowski, Mario Fritz
- Efficient Partial Monitoring with Prior Information Hastagiri P. Vanchinathan, Gábor Bartók, Andreas Krause
- Distributed Parameter Estimation in Probabilistic Graphical Models Yariv D. Mizrahi, Misha Denil, Nando de Freitas
- Unsupervised Deep Haar Scattering on Graphs Xu Chen, Xiuyuan Cheng, Stephane Mallat
- Online Optimization for Max-Norm Regularization Jie Shen, Huan Xu, Ping Li
- Probabilistic low-rank matrix completion on finite alphabets Jean Lafond, Olga Klopp, Eric Moulines, Joseph Salmon
- Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations Xianjie Chen, Alan L. Yuille
- Bayesian Inference for Structured Spike and Slab Priors Michael R. Andersen, Ole Winther, Lars K. Hansen
- Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling Ricardo Henao, Xin Yuan, Lawrence Carin
- Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng
- Making Pairwise Binary Graphical Models Attractive Nicholas Ruozzi, Tony Jebara
- Low Rank Approximation Lower Bounds in Row-Update Streams David Woodruff
- Deep Convolutional Neural Network for Image Deconvolution Li Xu, Jimmy S. Ren, Ce Liu, Jiaya Jia
- Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation Jonathan J. Tompson, Arjun Jain, Yann LeCun, Christoph Bregler
- Learning Generative Models with Visual Attention Yichuan Tang, Nitish Srivastava, Ruslan R. Salakhutdinov
- Metric Learning for Temporal Sequence Alignment Rémi Lajugie, Damien Garreau, Francis Bach, Sylvain Arlot
- Learning Optimal Commitment to Overcome Insecurity Avrim Blum, Nika Haghtalab, Ariel D. Procaccia
- How hard is my MDP?" The distribution-norm to the rescue" Odalric-Ambrym Maillard, Timothy A. Mann, Shie Mannor
- Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun
- 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
- Sequential Monte Carlo for Graphical Models Christian Andersson Naesseth, Fredrik Lindsten, Thomas B. Schön
- Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers Mehryar Mohri, Andres Munoz
- Optimal prior-dependent neural population codes under shared input noise Agnieszka Grabska-Barwinska, Jonathan W. Pillow
- Deep Fragment Embeddings for Bidirectional Image Sentence Mapping Andrej Karpathy, Armand Joulin, Fei Fei F. Li
- Flexible Transfer Learning under Support and Model Shift Xuezhi Wang, Jeff Schneider
- Probabilistic Differential Dynamic Programming Yunpeng Pan, Evangelos Theodorou
- Predicting Useful Neighborhoods for Lazy Local Learning Aron Yu, Kristen Grauman
- Modeling Deep Temporal Dependencies with Recurrent Grammar Cells"" Vincent Michalski, Roland Memisevic, Kishore Konda
- Generalized Dantzig Selector: Application to the k-support norm Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee
- Neurons as Monte Carlo Samplers: Bayesian ￼Inference and Learning in Spiking Networks Yanping Huang, Rajesh P. Rao
- The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim, Cynthia Rudin, Julie A. Shah
- Latent Support Measure Machines for Bag-of-Words Data Classification Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada
- Local Linear Convergence of Forward--Backward under Partial Smoothness Jingwei Liang, Jalal Fadili, Gabriel Peyré
- RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning Marek Petrik, Dharmashankar Subramanian
- Deep Learning Face Representation by Joint Identification-Verification Yi Sun, Yuheng Chen, Xiaogang Wang, Xiaoou Tang
- A provable SVD-based algorithm for learning topics in dominant admixture corpus Trapit Bansal, Chiranjib Bhattacharyya, Ravindran Kannan
- QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep K. Ravikumar, Stephen Becker, Peder A. Olsen
- General Stochastic Networks for Classification Matthias Zöhrer, Franz Pernkopf
- Spatio-temporal Representations of Uncertainty in Spiking Neural Networks Cristina Savin, Sophie Denève
- Attentional Neural Network: Feature Selection Using Cognitive Feedback Qian Wang, Jiaxing Zhang, Sen Song, Zheng Zhang
- Convolutional Neural Network Architectures for Matching Natural Language Sentences Baotian Hu, Zhengdong Lu, Hang Li, Qingcai Chen
- Scalable Non-linear Learning with Adaptive Polynomial Expansions Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus J. Telgarsky
- On the relations of LFPs & Neural Spike Trains David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin
- Diverse Sequential Subset Selection for Supervised Video Summarization Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei Sha
- Self-Paced Learning with Diversity Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, Alexander Hauptmann
- Feature Cross-Substitution in Adversarial Classification Bo Li, Yevgeniy Vorobeychik
- Deep Recursive Neural Networks for Compositionality in Language Ozan Irsoy, Claire Cardie
- 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
- A Filtering Approach to Stochastic Variational Inference Neil Houlsby, David Blei
- Optimizing F-Measures by Cost-Sensitive Classification Shameem Puthiya Parambath, Nicolas Usunier, Yves Grandvalet
- Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets Jie Wang, Jieping Ye
- Improved Multimodal Deep Learning with Variation of Information Kihyuk Sohn, Wenling Shang, Honglak Lee
- PEWA: Patch-based Exponentially Weighted Aggregation for image denoising Charles Kervrann
- Elementary Estimators for Graphical Models Eunho Yang, Aurelie C. Lozano, Pradeep K. Ravikumar
- Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems Cong Han Lim, Stephen Wright
- Neural Word Embedding as Implicit Matrix Factorization Omer Levy, Yoav Goldberg
- Multi-Resolution Cascades for Multiclass Object Detection Mohammad Saberian, Nuno Vasconcelos
- Median Selection Subset Aggregation for Parallel Inference Xiangyu Wang, Peichao Peng, David B. Dunson
- Recurrent Models of Visual Attention Volodymyr Mnih, Nicolas Heess, Alex Graves, koray kavukcuoglu
- Tree-structured Gaussian Process Approximations Thang D. Bui, Richard E. Turner
- Active Learning and Best-Response Dynamics Maria-Florina F. Balcan, Christopher Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, Le Song
- Analog Memories in a Balanced Rate-Based Network of E-I Neurons Dylan Festa, Guillaume Hennequin, Mate Lengyel
- Fast Sampling-Based Inference in Balanced Neuronal Networks Guillaume Hennequin, Laurence Aitchison, Mate Lengyel
- Spectral Learning of Mixture of Hidden Markov Models Cem Subakan, Johannes Traa, Paris Smaragdis
- Subspace Embeddings for the Polynomial Kernel Haim Avron, Huy Nguyen, David Woodruff
- A Boosting Framework on Grounds of Online Learning Tofigh Naghibi Mohamadpoor, Beat Pfister
- A Dual Algorithm for Olfactory Computation in the Locust Brain Sina Tootoonian, Mate Lengyel
- Advances in Learning Bayesian Networks of Bounded Treewidth Siqi Nie, Denis D. Maua, Cassio P. de Campos, Qiang Ji
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## 2 comments:

Almost paper URLs are wrong, although author pages seem to be correct.

Fixed for the moment!

Thanks for the heads up.

Igor.

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