NIPS2015 Papers and posters

  After the blogs, here are the papers and posters listed on the NIPS site, some have already been mentioned here and some caught my attention, here they are:

• Space-Time Local Embeddings Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet
• A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements Qinqing Zheng, John Lafferty
• Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning Jiajun Wu, Ilker Yildirim, Joseph J. Lim, Bill Freeman, Josh Tenenbaum
• On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors Andrea Montanari, Daniel Reichman, Ofer Zeitouni
• A fast, universal algorithm to learn parametric nonlinear embeddings Miguel A. Carreira-Perpinan, Max Vladymyrov
• Orthogonal NMF through Subspace Exploration Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G. Dimakis
• Deeply Learning the Messages in Message Passing Inference Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel
• Accelerated Proximal Gradient Methods for Nonconvex Programming Huan Li, Zhouchen Lin
• Approximating Sparse PCA from Incomplete Data ABHISEK KUNDU, Petros Drineas, Malik Magdon-Ismail
• Column Selection via Adaptive Sampling Saurabh Paul, Malik Magdon-Ismail, Petros Drineas
• HONOR: Hybrid Optimization for NOn-convex Regularized problems Pinghua Gong, Jieping Ye
• Tensorizing Neural Networks Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, Dmitry P. Vetrov
• Parallelizing MCMC with Random Partition Trees Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson
• On the Global Linear Convergence of Frank-Wolfe Optimization Variants Simon Lacoste-Julien, Martin Jaggi
• Efficient Compressive Phase Retrieval with Constrained Sensing Vectors Sohail Bahmani, Justin Romberg
• Compressive spectral embedding: sidestepping the SVD Dinesh Ramasamy, Upamanyu Madhow
• A Nonconvex Optimization Framework for Low Rank Matrix Estimation Tuo Zhao, Zhaoran Wang, Han Liu
• Automatic Variational Inference in Stan Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei
• Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy Marylou Gabrie, Eric W. Tramel, Florent Krzakala
• Robust Regression via Hard Thresholding Kush Bhatia, Prateek Jain, Purushottam Kar
• Sparse Local Embeddings for Extreme Multi-label Classification Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain
• Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems Yuxin Chen, Emmanuel Candes
• Subspace Clustering with Irrelevant Features via Robust Dantzig Selector Chao Qu, Huan Xu
• Sparse PCA via Bipartite Matchings Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis
• Fast Randomized Kernel Ridge Regression with Statistical Guarantees Ahmed Alaoui, Michael W. Mahoney
• Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling Zheng Qu, Peter Richtarik, Tong Zhang
• Matrix Manifold Optimization for Gaussian Mixtures Reshad Hosseini, Suvrit Sra
• Fast and Guaranteed Tensor Decomposition via Sketching Yining Wang, Hsiao-Yu Tung, Alex J. Smola, Anima Anandkumar
• Differentially private subspace clustering Yining Wang, Yu-Xiang Wang, Aarti Singh
• Non-convex Statistical Optimization for Sparse Tensor Graphical Model Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng
• Optimal Rates for Random Fourier Features Bharath Sriperumbudur, Zoltan Szabo
• Practical and Optimal LSH for Angular Distance Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt
• Learning to Linearize Under Uncertainty Ross Goroshin, Michael F. Mathieu, Yann LeCun
• Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation Alaa Saade, Florent Krzakala, Lenka Zdeborová
• Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition Cameron Musco, Christopher Musco
• Optimal Linear Estimation under Unknown Nonlinear Transform Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu
• Sum-of-Squares Lower Bounds for Sparse PCA Tengyu Ma, Avi Wigderson
• Learning with Incremental Iterative Regularization Lorenzo Rosasco, Silvia Villa
• Halting in Random Walk Kernels Mahito Sugiyama, Karsten Borgwardt
• MCMC for Variationally Sparse Gaussian Processes James Hensman, Alexander G. Matthews, Maurizio Filippone, Zoubin Ghahramani
• Less is More: Nyström Computational Regularization Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco
• Analysis of Robust PCA via Local Incoherence Huishuai Zhang, Yi Zhou, Yingbin Liang
• Spherical Random Features for Polynomial Kernels Jeffrey Pennington, Felix Yu, Sanjiv Kumar
• Matrix Completion Under Monotonic Single Index Models Ravi Sastry Ganti, Laura Balzano, Rebecca Willett
• Robust PCA with compressed data Wooseok Ha, Rina Foygel Barber
• b-bit Marginal Regression Martin Slawski, Ping Li
• Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs Vidyashankar Sivakumar, Arindam Banerjee, Pradeep K. Ravikumar
• Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients Bo Xie, Yingyu Liang, Le Song
• Generalization in Adaptive Data Analysis and Holdout Reuse Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, Aaron Roth
• Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
• Training Very Deep Networks Rupesh K. Srivastava, Klaus Greff, Juergen Schmidhuber
• End-To-End Memory Networks Sainbayar Sukhbaatar, arthur szlam, Jason Weston, Rob Fergus
• Spectral Representations for Convolutional Neural Networks Oren Rippel, Jasper Snoek, Ryan P. Adams
• Recognizing retinal ganglion cells in the dark Emile Richard, Georges A. Goetz, E. J. Chichilnisky
• Hidden Technical Debt in Machine Learning Systems D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison
• Deep Convolutional Inverse Graphics Network Tejas D. Kulkarni, William F. Whitney, Pushmeet Kohli, Josh Tenenbaum
• Sparse and Low-Rank Tensor Decomposition Parikshit Shah, Nikhil Rao, Gongguo Tang
• Super-Resolution Off the Grid Qingqing Huang, Sham M. Kakade
• Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms Christopher M. De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré, Christopher Ré
• Associative Memory via a Sparse Recovery Model Arya Mazumdar, Ankit Singh Rawat
• Robust Spectral Inference for Joint Stochastic Matrix Factorization Moontae Lee, David Bindel, David Mimno
• Fast, Provable Algorithms for Isotonic Regression in all L_p-norms Rasmus Kyng, Anup Rao, Sushant Sachdeva
• Grammar as a Foreign Language Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton
• Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices Martin Slawski, Ping Li, Matthias Hein
• Winner-Take-All Autoencoders Alireza Makhzani, Brendan J. Frey
• Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's Vitaly Feldman, Will Perkins, Santosh Vempala
• Accelerated Mirror Descent in Continuous and Discrete Time Walid Krichene, Alexandre Bayen, Peter L. Bartlett
• The Human Kernel Andrew G. Wilson, Christoph Dann, Chris Lucas, Eric P. Xing
• Structured Estimation with Atomic Norms: General Bounds and Applications Sheng Chen, Arindam Banerjee
• Preconditioned Spectral Descent for Deep Learning David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher
• Nearly Optimal Private LASSO Kunal Talwar, Abhradeep Thakurta, Li Zhang
• Convergence Analysis of Prediction Markets via Randomized Subspace Descent Rafael Frongillo, Mark D. Reid
• Structured Transforms for Small-Footprint Deep Learning Vikas Sindhwani, Tara Sainath, Sanjiv Kumar
• BinaryConnect: Training Deep Neural Networks with binary weights during propagations Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
• Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm Qinqing Zheng, Ryota Tomioka
• Fast and Memory Optimal Low-Rank Matrix Approximation Se-Young Yun, marc lelarge, Alexandre Proutiere
• The Self-Normalized Estimator for Counterfactual Learning Adith Swaminathan, Thorsten Joachims
• Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Gael Varoquaux
• Gaussian Process Random Fields David Moore, Stuart J. Russell
• M-Statistic for Kernel Change-Point Detection Shuang Li, Yao Xie, Hanjun Dai, Le Song
• Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization Niao He, Zaid Harchaoui
• LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements CHRISTOS THRAMPOULIDIS, Ehsan Abbasi, Babak Hassibi
• Matrix Completion with Noisy Side Information Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon

Credits: ESA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

Date: 11 December 2015
Satellite: Rosetta
Depicts: Comet 67P/Churyumov-Gerasimenko
Copyright: See below

Single-frame OSIRIS narrow-angle camera (NAC) image taken on 10 December 2015, when Rosetta was 103.2 km from the nucleus of Comet 67P/Churyumov-Gerasimenko. The scale is 1.87 m/pixel.

Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.