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

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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