1 citations · 1 across the 7 of their papers we have counts for
9 papers
A Unified Framework for Training-Dataset Aggregation and Exact Machine Unlearning via Gibbs Probability Measures
Yaiza Bermudez, Samir M. Perlaza, Iñaki Esnaola
In this paper, three operations on Gibbs probability measures are studied: renormalization, normalized log-linear combination, and nesting. The resulting measures are shown to be G…
Machine Unlearning for Gibbs Supervised Learning Algorithms
Yaiza Bermudez, Samir M. Perlaza, Iñaki Esnaola
In this paper, a method for achieving exact unlearning for Gibbs supervised learning algorithms is proposed using a variational formulation inspired by empirical risk minimization…
Equivalence Between Nested Gibbs Measures and Log-Linear Combinations of Gibbs Measures
Yaiza Bermudez, Samir M. Perlaza, Iñaki Esnaola
In this paper, three operations on Gibbs probability measures are studied. The first operation, often referred to as renormalization, takes one Gibbs probability measure and genera…
Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms
Yaiza Bermudez, Samir M. Perlaza, Iñaki Esnaola
In this paper, it is shown, for the first time, that centralized performance is achievable in decentralized learning without sharing the local datasets. Specifically, when clients…
Symmetry of concentration and scaling for self-bounding functions
George Crowley, Iñaki Esnaola
We prove generalised concentration inequalities for a class of scaled self-bounding functions of independent random variables, referred to as self-bounding. The scaling…
Solar Vortices as Conduits for Magnetoacoustic Waves: Multi-Layer Coupling and Their Role in Atmospheric Heating
Suzana S. A. Silva, Ioannis Dakanalis, Luiz A. C. A. Schiavo +10
Vortices are widespread throughout the solar atmosphere and commonly interpreted as channels for Alfvén wave energy transport. However, this interpretation has mostly been inferred…
TaylorPODA: A Taylor Expansion-Based Method to Improve Post-Hoc Attributions for Opaque Models
Yuchi Tang, Iñaki Esnaola, George Panoutsos
Post-hoc model-agnostic local attribution (LA) methods have been widely adopted to explain opaque AI models by quantifying feature-wise contributions. However, many existing method…
Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
Nazia Nafis, Inaki Esnaola, Alvaro Martinez-Perez +2
Generating synthetic tabular health data is challenging, and evaluating their quality is equally, if not more, complex. This systematic review highlights the critical importance of…
Submodularity of Mutual Information for Multivariate Gaussian Sources with Additive Noise
George Crowley, Inaki Esnaola
Sensor placement approaches in networks often involve using information-theoretic measures such as entropy and mutual information. We prove that mutual information abides by submod…