5 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…
Any-Dimensional Invariant Universality
Shengtai Yao, Eitan Levin, Mateo DÃaz
Several machine learning models are defined for inputs of any size, such as graphs with different numbers of nodes and point clouds containing varying numbers of points. The univer…
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…
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…
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…