I will come back to some of the videos and slides of this workshop on Semidefinite Optimization, Approximation and Applications organized at the Simons Institute at Berkeley (is Simons in the audience ? ). But in the meantime here are the links to pages that generally features both the slides and the videos (and sometimes none of the two).

- Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints Benjamin Recht, UC Berkeley
- Adventures in Linear Algebra++ and Unsupervised Learning Sanjeev Arora, Princeton University
- Learning Overcomplete Latent Variable Models through Tensor Power Method Rong Ge, Microsoft Research
- Robust PCA via Non-convex Methods: Provable Bounds Animashree Anandkumar, UC Irvine
- Convex Relaxations for Recovering Simultaneously Structured Objects Maryam Fazel, University of Washington
- The Edge of Tractability: Unique Games vs Sum of Squares David Steurer, Cornell University
- Recovering Hidden Sparsity via Sum of Squares Jonathan Kelner, Massachusetts Institute of Technology
- A Polynomial Time Algorithm for Lossy Population Recovery Ankur Moitra, Massachusetts Institute of Technology
- Serialrank: Spectral Ranking using Seriation Alexandre d'Aspremont, CNRS - École Normale Superieure Paris
- Relative Entropy Relaxations for Signomial Optimization Venkat Chandrasekaran, California Institute of Technology
- Robust Sketching for Large-Scale Multi-Instance Conic Optimization Laurent El Ghaoui, UC Berkeley
- Spectrahedra and their Shadows Bernd Sturmfels, UC Berkeley
- Spectral Bounds and SDP Hierarchies for Geometric Packing Problems Frank Vallentin, University of Cologne
- Positive Semidefinite Rank Rekha Thomas, University of Washington
- An SDP-based Algorithmic Cheeger Inequality for Vertex Expansion Santosh Vempala, Georgia Institute of Technology
- Tensor Decompositions: Uniqueness and Smoothed Analysis Moses Charikar, Princeton University
- Convex Optimization and Quantum Information Aram Harrow, Massachusetts Institute of Technology
- Noncommutative Polynomial Optimization under Dimension Constraints Miguel Navascués Cobo, Universitat Autònoma de Barcelona
- Faster SDP Hierarchy Solvers for Local Rounding Algorithms Ali Sinop, Institute for Advanced Study, Princeton
- Convergence of SDP Hierarchies Using Kernel Based Methods: Moment Matrices Stephanie Wehner, National University of Singapore
- Controlling Eigenvalues (at least in theory) Adam Marcus, Yale University
- Efficient First-Order Methods for Linear (and Semidefinite and Hyperbolic) Programming James Renegar, Cornell University
- Primal-Dual Symmetric Interior-Point Methods: From SDP to Hyperbolic Cone Programming and Beyond Levent Tunçel, University of Waterloo
- Effectivity Issues and Results for Hilbert's 17th Problem Marie-Françoise Roy, Universite de Rennes

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