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**

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.

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.

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