7 citations · 11 across the 8 of their papers we have counts for
9 papers
Simultaneous Frequentist Calibration of Confidence Regions for Multiple Functionals in Constrained Inverse Problems
Pau Batlle, Pratik Patil, Michael Stanley +3
Many scientific analyses require simultaneous comparison of multiple functionals of an unknown signal at once, calling for multidimensional confidence regions with guaranteed simul…
Confidence intervals for functionals in constrained inverse problems via data-adaptive sampling-based calibration
Michael Stanley, Pau Batlle, Pratik Patil +2
We address functional uncertainty quantification for ill-posed inverse problems where it is possible to evaluate a possibly rank-deficient forward model, the observation noise dist…
Diffeomorphic Measure Matching with Kernels for Generative Modeling
Biraj Pandey, Bamdad Hosseini, Pau Batlle +1
This article presents a general framework for the transport of probability measures towards minimum divergence generative modeling and sampling using ordinary differential equation…
Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots
Théo Bourdais, Pau Batlle, Xianjin Yang +3
Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown f…
Optimization-based frequentist confidence intervals for functionals in constrained inverse problems: Resolving the Burrus conjecture
Pau Batlle, Pratik Patil, Michael Stanley +2
We present an optimization-based framework to construct confidence intervals for functionals in constrained inverse problems, ensuring valid one-at-a-time frequentist coverage guar…
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs
Pau Batlle, Yifan Chen, Bamdad Hosseini +2
We introduce a priori Sobolev-space error estimates for the solution of nonlinear, and possibly parametric, PDEs using Gaussian process and kernel based methods. The primary assump…
Kernel Methods are Competitive for Operator Learning
Pau Batlle, Matthieu Darcy, Bamdad Hosseini +1
We present a general kernel-based framework for learning operators between Banach spaces along with a priori error analysis and comprehensive numerical comparisons with popular neu…
Learning grounded word meaning representations on similarity graphs
Mariella Dimiccoli, Herwig Wendt, Pau Batlle
This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy. The lower le…
Adaptive Test Allocation for Outbreak Detection and Tracking in Social Contact Networks
Pau Batlle, Joan Bruna, Carlos Fernandez-Granda +1
We present a general framework for adaptive allocation of viral tests in social contact networks. We pose and solve several complementary problems. First, we consider the design of…