• Click on the titles of individual talks for abstract, slides and archived video.

All events take place in the Calvin Lab auditorium.

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Coffee and Check-In
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Opening Remarks
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Continuous Methods for Discrete Optimization: From Convex Relaxations, to Iterative Schemes, and Back
Aleksander Mądry, MIT
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Break
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Width-independent Iterative Algorithms for Packing and Covering Programs
Lorenzo Orecchia, Boston University
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Slime Molds and Sparse Recovery
Nisheeth Vishnoi, École Polytechnique Fédérale de Lausanne
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Lunch
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Michael Cohen and ell_p Regression
Yin-Tat Lee, University of Washington
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Break
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Chordal Graphs and Sparse Semidefinite Optimization
Lieven Vandenberghe, UCLA
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On the Optimization Landscape of Matrix and Tensor Decomposition Problems
Tengyu Ma, Princeton University
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Reception
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Coffee and Check-In
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The State of Contemporary Computing Substrates for Optimization Methods
Ben Recht, UC Berkeley
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Break
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Let's Make Block Coordinate Descent Go Fast
Mark Schmidt, University of British Columbia
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Stochastic, Second-order Black-box Optimization for Step Functions
Katya Scheinberg, Lehigh University
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Lunch
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Algorithmic Tools for Smooth Nonconvex Optimization
Steve Wright, University of Wisconsin-Madison
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Break
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Zero-order and Dynamic Sampling Methods for Nonlinear Optimization
Jorge Nocedal, Northwestern University
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Dealing with Linear Constraints via Random Permutation
Ruoyu Sun, University of Illinois at Urbana-Champaign
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Coffee and Check-In
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Sketching as a Tool for Fast Algorithm Design
Alex Andoni, Columbia University
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Break
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Sublinear Time Low-rank Approximation of Positive Semidefinite Matrices
David Woodruff, IBM Almaden
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Hashing-based-estimators for Kernel Density in High Dimensions
Moses Charikar, Stanford University
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Lunch
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Randomized Iterative Methods and Complexity for Markov Decision Process
Mengdi Wang, Princeton University
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Break
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Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage
Madeleine Udell, Cornell University
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Open Problems Session
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Coffee and Check-In
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Trends in Large-scale Nonconvex Optimization
Suvrit Sra, MIT
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Break
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Faster Algorithms and New Iterative Methods for Computing the Stationary Distribution
Aaron Sidford, Stanford University
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Nonconvex Optimization for High-dimensional Learning: From ReLUs to Submodular Maximization
Mahdi Soltanolkotabi, University of Southern California
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Lunch
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Monte Carlo Approximation Certificates for K-means Clustering
Soledad Villar, New York University
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Approximate Gaussian Elimination for Laplacians
Rasmus Kyng, Yale University
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Michael Cohen's Proper Version of Nesterov's Accelerated Method
Jelena Diakonikolas, Boston University
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A Lyapunov Analysis for Accelerated Gauss-Seidel
Stephen Tu, UC Berkeley
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Optimization Problems over Submodular Polytopes
Swati Gupta, MIT
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An Iterative Quasi-polynomial Time Algorithm for Solving Parity Games
Marcin Jurdzinski, University of Warwick
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Break
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Highly Efficient Algorithms for Lasso-type Problems Based on Second-order Sparsity
Kim-Chuan Toh, National University of Singapore
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The Douglas--Rachford Splitting Algorithm for Inconsistent Minimization Problems
Walaa Moursi, University of British Columbia
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On the Convergence and Complexity of Nonconvex ADMM
Shiqian Ma, UC Davis
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Faster Constrained Optimization via Approximate Projections
Ludwig Schmidt, MIT
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Coffee and Check-In
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Will Vanishing Gradients Ever Vanish from Deep Learning?
Moritz Hardt, UC Berkeley
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Break
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From Minimum Cut to Submodular Minimization: Leveraging the Decomposable Structure
Alina Ene, Boston University
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Natasha 2: Faster Non-convex Optimization Than SGD
Zeyuan Allen-Zhu, Microsoft Research