arXiv subjects

Alberto Bocchinfuso

Publications and source records attributed to Alberto Bocchinfuso.

3 recordsLinked to original sources

arXiv2026

An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees

Fine-tuning adapts a pretrained machine learning model to a small, sensitive dataset, but this process risks memorizing individual new data points, making the model vulnerable to adversaries who seek to extract sensitive information. In this work, we develop a randomized algorithm based on the exponential mechanism for fine-tuning while ensuring differential privacy. Our key idea is to construct a simple utility function that combines a local quadratic approximation of the pretrained model with information from the new dataset. The resulting exponential mechanism admits exact sampling from a multivariate normal distribution in closed form. We establish theoretical privacy guarantees, sensitivity bounds, and accuracy estimations for our method. We further introduce a random-projection strategy that makes the approach scalable to high-dimensional models. Numerical experiments on the MNIST benchmark and the MIMIC clinical dataset demonstrate competitive performance against existing differentially private fine-tuning techniques.

cs.LG↗

arXiv2025

Bayesian dictionary learning estimation of cell membrane permeability from surface pH data

Gas transport across cell membrane is a very important process in biochemistry which is essential for many crucial tasks, including cell respiration pH regulation in the cell. In the late 1990's, the suggestion that gasses are transported via preferred gas channels embedded into the cell membrane challenged the century old Overton's theory that gases pass through the lipid cell membrane by diffusing across the concentration gradient. Since experimental evidence alone does not provide enough evidence to favor one of the proposed mechanisms, mathematical models have been introduced to provide a context for the interpretation of laboratory measurement. Following up on previous work where the membrane permeability was estimated using particle filter, in this article we propose an algorithm based on dictionary learning for estimating cell membrane permeability. Computed examples illustrate that the novel approach, which can be applied when the properties of the membrane do not change in the course of the data collection process, is computationally much more efficient than particle filter.

math.NA↗

arXiv2023

Bayesian sparsity and class sparsity priors for dictionary learning and coding

Dictionary learning methods continue to gain popularity for the solution of challenging inverse problems. In the dictionary learning approach, the computational forward model is replaced by a large dictionary of possible outcomes, and the problem is to identify the dictionary entries that best match the data, akin to traditional query matching in search engines. Sparse coding techniques are used to guarantee that the dictionary matching identifies only few of the dictionary entries, and dictionary compression methods are used to reduce the complexity of the matching problem. In this article, we propose a work flow to facilitate the dictionary matching process. First, the full dictionary is divided into subdictionaries that are separately compressed. The error introduced by the dictionary compression is handled in the Bayesian framework as a modeling error. Furthermore, we propose a new Bayesian data-driven group sparsity coding method to help identify subdictionaries that are not relevant for the dictionary matching. After discarding irrelevant subdictionaries, the dictionary matching is addressed as a deflated problem using sparse coding. The compression and deflation steps can lead to substantial decreases of the computational complexity. The effectiveness of compensating for the dictionary compression error and using the novel group sparsity promotion to deflate the original dictionary are illustrated by applying the methodology to real world problems, the glitch detection in the LIGO experiment and hyperspectral remote sensing.

stat.ML↗