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
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