30 citations · 31 across the 10 of their papers we have counts for
11 papers
Single-Letterization of Rate-Cost Tradeoffs in Static Time-Invariant Control (STIC) Systems
Eray Unsal Atay, Venkat Chandrasekaran, Victoria Kostina
In settings such as online recommendation platforms, medical decision-making with interim summaries, and stochastic control with static plants, the underlying system is often stati…
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 finite-horizon rate-cost tradeoffs for general stochastic control systems, including nonlinear systems. Under an average control cost constraint, we show that the minimum…
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…
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…
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…
Poset-Markov Channels: Capacity via Group Symmetry
Eray Unsal Atay, Eitan Levin, Venkat Chandrasekaran +1
Computing channel capacity is in general intractable because it is given by the limit of a sequence of optimization problems whose dimensionality grows to infinity. As a result, co…
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…
Controlling the False Discovery Rate in Subspace Selection
Mateo Díaz, Venkat Chandrasekaran
Controlling the false discovery rate (FDR) is a popular approach to multiple testing, variable selection, and related problems of simultaneous inference. In many contemporary appli…
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression
Anshuman Pradhan, Kyra H. Adams, Venkat Chandrasekaran +4
Modeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low-quality well data which is sparsely and noisily sampl…
Feedback Message Passing for Inference in Gaussian Graphical Models
Ying Liu, Venkat Chandrasekaran, Animashree Anandkumar +1
While loopy belief propagation (LBP) performs reasonably well for inference in some Gaussian graphical models with cycles, its performance is unsatisfactory for many others. In par…