COLT 2016 accepted papers

On his blog, Sebastien put together a list of some of the accepted papers at COLT 2016. Go read his blog entry, I'll wait......Here is a subset that will be or has been featured here:

 

• Ronen Eldan and Ohad Shamir. The Power of Depth for Feedforward Neural Network
• Nadav Cohen, Or Sharir and Amnon Shashua. On the Expressive Power of Deep Learning: A Tensor Analysis
• Matus Telgarsky. Benefits of depth in neural networks
• Jason Lee, Max Simchowitz, Benjamin Recht and Michael Jordan. Gradient Descent only Converges to Minimizers
• Srinadh Bhojanapalli, Anastasios Kyrillidis and Sujay Sanghavi. Dropping Convexity for Faster Semi-definite Optimization
• Dean Foster, Satyen Kale and Howard Karloff. Online Sparse Linear Regression
• Siu On Chan, Dimitris Papailliopoulos and Aviad Rubinstein. On the Approximability of Sparse PCA
•  Boaz Barak and Ankur Moitra. Noisy Tensor Completion via the Sum-of-Squares Hierarchy
• Elad Hazan, Tomer Koren, Roi Livni and Yishay Mansour. Online Learning with Low Rank Experts
• Jess Banks, Cristopher Moore, Joe Neeman and Praneeth Netrapalli. Information-theoretic thresholds for community detection in sparse networks
• Hongyi Zhang and Suvrit Sra. First-order Methods for Geodesically Convex Optimization
• Christos Papadimitriou, Samantha Petti and Santosh Vempala. Cortical Computation via Iterative Constructions
• Ahmed El Alaoui, Xiang Cheng, Aaditya Ramdas, Martin Wainwright and Michael Jordan. Asymptotic behavior of $\ell_q$-based Laplacian regularization in semi-supervised learning
• Francis Bach and Vianney Perchet. Highly-Smooth Zero-th Order Online Optimization
• Bruce Hajek, Yihong Wu and Jiaming Xu. Semidefinite Programs for Exact Recovery of a Hidden Community
• Afonso S. Bandeira, Nicolas Boumal and Vladislav Voroninski. On the low-rank approach for semidefinite programs arising in synchronization and community detection

 

 

 

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