7 citations · 15 across the 11 of their papers we have counts for
12 papers
Learning sufficient low-dimensional structures through conditional optimal transport
Kaiqiang Alan Zeng, Efstathia Bura
Sufficient dimension reduction seeks a low-dimensional covariate representation that preserves the conditional law of a response. We introduce SDR-COT, which represents that law by…
Generalized Multi-Linear Models for Sufficient Dimension Reduction on Tensor Valued Predictors
Daniel Kapla, Efstathia Bura
We consider supervised learning (regression/classification) problems with tensor-valued input. We derive multi-linear sufficient reductions for the regression or classification pro…
Exact Upper and Lower Bounds for the Output Distribution of Neural Networks with Random Inputs
Andrey Kofnov, Daniel Kapla, Ezio Bartocci +1
We derive exact upper and lower bounds for the cumulative distribution function (cdf) of the output of a neural network (NN) over its entire support subject to noisy (stochastic) i…
Exact and Approximate Moment Derivation for Probabilistic Loops With Non-Polynomial Assignments
Andrey Kofnov, Marcel Moosbrugger, Miroslav Stankovič +2
Many stochastic continuous-state dynamical systems can be modeled as probabilistic programs with nonlinear non-polynomial updates in non-nested loops. We present two methods, one a…
Moment-based Density Elicitation with Applications in Probabilistic Loops
Andrey Kofnov, Ezio Bartocci, Efstathia Bura
We propose the K-series estimation approach for the recovery of unknown univariate and multivariate distributions given knowledge of a finite number of their moments. Our method is…
Moment-based Invariants for Probabilistic Loops with Non-polynomial Assignments
Andrey Kofnov, Marcel Moosbrugger, Miroslav Stankovič +2
We present a method to automatically approximate moment-based invariants of probabilistic programs with non-polynomial updates of continuous state variables to accommodate more com…