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Found 8 papers, 4 papers with code
Global optimality under amenable symmetry constraints
We also explain connections to the Hunt-Stein theorem on invariant tests.
Representing and Learning Functions Invariant Under Crystallographic Groups
The linear representation generalizes the Fourier basis to crystallographically invariant basis functions.
Data Augmentation in the Underparameterized and Overparameterized Regimes
We provide results that exactly quantify how data augmentation affects the variance and limiting distribution of estimates, and analyze several specific models in detail.
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
We solve this problem using recent ideas from graph sampling theory to (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this empirical risk that are automatically unbiased.
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach
Our main technical result is a generalization bound for compressed networks based on the compressed size.
Random Walk Models of Network Formation and Sequential Monte Carlo Methods for Graphs
We introduce a class of generative network models that insert edges by connecting the starting and terminal vertices of a random walk on the network graph.
Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures
The natural habitat of most Bayesian methods is data represented by exchangeable sequences of observations, for which de Finetti's theorem provides the theoretical foundation.
Construction of Nonparametric Bayesian Models from Parametric Bayes Equations
We consider the general problem of constructing nonparametric Bayesian models on infinite-dimensional random objects, such as functions, infinite graphs or infinite permutations.
