Saturday Morning Videos: ICML 2015 in Lille

All the videos of all the talks at ICML were just released this week. Enjoy !

http://videolectures.net/icml2015_lille/

Here are a few I had not noticed before. On average they last about 15 to 20 minutes:

• Theory of Dual-sparse Regularized Randomized Reduction Tianbao Yang
• Guaranteed Tensor Decomposition: A Moment Approach Gongguo Tang  
• PU Learning for Matrix Completion Cho-Jui Hsieh  
• Low-Rank Matrix Recovery from Row-and-Column Affine Measurements Avishai Wagner  
• Intersecting Faces: Non-negative Matrix Factorization With New Guarantees James Y. Zou  
•  CUR Algorithm for Partially Observed Matrices Miao Xu  
• An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection Tianbao Yang 
• Swept Approximate Message Passing for Sparse Estimation Eric W. Tramel  
• Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing Rongda Zhu  
• Sparse Subspace Clustering with Missing Entries René Vidal   
• Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems Qiang Zhou  
• Complete Dictionary Recovery Using Nonconvex Optimization John Wright  
• Low Rank Approximation using Error Correcting Coding Matrices Shashanka Ubaru  
• A Unified Framework for Outlier-Robust PCA-like Algorithms Wenzhuo Yang 
• Deep Unsupervised Learning using Nonequilibrium Thermodynamics Jascha Sohl-Dickstein
• Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions
  Alina Raluca Ene
• The Power of Randomization: Distributed Submodular Maximization on Massive Datasets Justin Ward
• Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs Yarin Gal
• Sparse Variational Inference for Generalized GP ModelsRishit Sheth
• A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data Yining Wang  
• Stay on path: PCA along graph paths Megasthenis Asteris  
• Geometric Conditions for Subspace-Sparse Recovery René Vidal 
• A Convex Optimization Framework for Bi-Clustering Huan Xu  
• Multi-Task Learning for Subspace Segmentation Yu Wang  
• Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization Jinbo Bi
• Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification Bo Xin

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