• 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
Video
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Break
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Width-independent Iterative Algorithms for Packing and Covering Programs

Lorenzo Orecchia, Boston University
Video
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Slime Molds and Sparse Recovery

Nisheeth Vishnoi, École Polytechnique Fédérale de Lausanne
Video
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Lunch
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Michael Cohen and ell_p Regression

Yin-Tat Lee, University of Washington
Video
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Break
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Chordal Graphs and Sparse Semidefinite Optimization

Lieven Vandenberghe, UCLA
Video
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On the Optimization Landscape of Matrix and Tensor Decomposition Problems

Tengyu Ma, Princeton University
Video
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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
Video
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Break
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Let's Make Block Coordinate Descent Go Fast

Mark Schmidt, University of British Columbia
Video
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Stochastic, Second-order Black-box Optimization for Step Functions

Katya Scheinberg, Lehigh University
Video
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Lunch
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Algorithmic Tools for Smooth Nonconvex Optimization

Steve Wright, University of Wisconsin-Madison
Video
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Break
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Zero-order and Dynamic Sampling Methods for Nonlinear Optimization

Jorge Nocedal, Northwestern University
Video
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Dealing with Linear Constraints via Random Permutation

Ruoyu Sun, University of Illinois at Urbana-Champaign
Video
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Coffee and Check-In
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Sketching as a Tool for Fast Algorithm Design

Alex Andoni, Columbia University
Video
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Break
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Sublinear Time Low-rank Approximation of Positive Semidefinite Matrices

David Woodruff, IBM Almaden
Video
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Hashing-based-estimators for Kernel Density in High Dimensions

Moses Charikar, Stanford University
Video
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Lunch
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Randomized Iterative Methods and Complexity for Markov Decision Process

Mengdi Wang, Princeton University
Video
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Break
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Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage

Madeleine Udell, Cornell University
Video
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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
Video
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Break
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Faster Algorithms and New Iterative Methods for Computing the Stationary Distribution

Aaron Sidford, Stanford University
Video
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Nonconvex Optimization for High-dimensional Learning: From ReLUs to Submodular Maximization

Mahdi Soltanolkotabi, University of Southern California
Video
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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
Video
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Break
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From Minimum Cut to Submodular Minimization: Leveraging the Decomposable Structure

Alina Ene, Boston University
Video
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Natasha 2: Faster Non-convex Optimization Than SGD

Zeyuan Allen-Zhu, Microsoft Research
Video