Yuri Saporito — accepted AI-conference papers

2025

Infinite Neural Operators: Gaussian processes on functions

Daniel Augusto de Souza, Yuchen Zhu, Harry Jake Cunningham, Yuri Saporito, Diego Mesquita, Marc Peter Deisenroth

NeurIPS 2025poster

A variety of infinitely wide neural architectures (e.g., dense NNs, CNNs, and transformers) induce Gaussian process (GP) priors over their outputs. These relationships provide both an accurate characterization of the prior predictive distribution and enable the use of GP machinery to improve the unc…

Cited by 0 Source ↗Scholar ↗

2024

Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients

Yuri Fonseca, Caio Peixoto, Yuri Saporito

NeurIPS 2024poster

Instrumental variables (IVs) provide a powerful strategy for identifying causal effects in the presence of unobservable confounders. Within the nonparametric setting (NPIV), recent methods have been based on nonlinear generalizations of Two-Stage Least Squares and on minimax formulations derived fro…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2022

Statistical Learning and Inverse Problems: A Stochastic Gradient Approach

Yuri Fonseca, Yuri Saporito

NeurIPS 2022accept

Inverse problems are paramount in Science and Engineering. In this paper, we consider the setup of Statistical Inverse Problem (SIP) and demonstrate how Stochastic Gradient Descent (SGD) algorithms can be used to solve linear SIP. We provide consistency and finite sample bounds for the excess risk.…

Cited by 4 Source ↗PDF ↗Scholar ↗