7 papers
Any-Dimensional Learning by Sampling
Eitan Levin, Venkat Chandrasekaran
Many machine learning models are defined for inputs of different sizes, such as point clouds containing different numbers of points, sequences of tokens of different lengths, and g…
Rate-Cost Tradeoffs in Nonlinear Control
Eray Unsal Atay, Venkat Chandrasekaran, Victoria Kostina
We study the rate-cost tradeoff in rate-limited control of general stochastic control systems, including nonlinear systems, over a finite horizon. At each time step, an encoder obs…
Limits of Weighted Graphs via Random Quotients
Eitan Levin, Venkat Chandrasekaran
We present a new notion of limits of weighted directed graphs of growing size based on convergence of their random quotients. These limits are specified in terms of random exchange…
Operatopes, Operanoids, and Noncommutative Zonoids
Eliza O'Reilly, Venkat Chandrasekaran
We study a class of convex bodies called operatopes that are obtained by taking Minkowski sums of affine images of an operator norm ball. This notion generalizes that of zonotopes…
Lagrangian Dual Sections: A Topological Perspective on Hidden Convexity
Venkat Chandrasekaran, Timothy Duff, Jose Israel Rodriguez +1
Hidden convexity is a powerful idea in optimization: under the right transformations, nonconvex problems that are seemingly intractable can be solved efficiently using convex optim…
Any-Dimensional Polynomial Optimization via de Finetti Theorems
Eitan Levin, Venkat Chandrasekaran
Polynomial optimization problems often arise in sequences indexed by dimension, and it is of interest to compute bounds on the optimal values of all problems in the sequence. Examp…