Tobia Boschi — accepted AI-conference papers

2025

Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation

Cécile Rousseau, Tobia Boschi, Giandomenico Cornacchia, Dhaval Salwala, Alessandra Pascale, Juan Bernabe Moreno

NeurIPS 2025poster

SDForger is a flexible and efficient framework for generating high-quality multivariate time series using LLMs. Leveraging a compact data representation, SDForger provides synthetic time series generation from a few samples and low-computation fine-tuning of any autoregressive LLM. Specifically, the…

Cited by 0 Source ↗code ↗Scholar ↗

2024

A New Computationally Efficient Algorithm to solve Feature Selection for Functional Data Classification in High-dimensional Spaces

Tobia Boschi, Francesca Bonin, Rodrigo Ordonez-Hurtado, Alessandra Pascale, Jonathan P Epperlein

ICML 2024poster

This paper introduces a novel methodology for Feature Selection for Functional Classification, FSFC, that addresses the challenge of jointly performing feature selection and classification of functional data in scenarios with categorical responses and multivariate longitudinal features. FSFC tackles…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2024

Functional Graph Convolutional Networks: A Unified Multi-task and Multi-modal Learning Framework to Facilitate Health and Social-Care Insights

Tobia Boschi, Francesca Bonin, Rodrigo Ordonez-Hurtado, Cécile Rousseau, Alessandra Pascale, John Dinsmore

IJCAI 2024poster

This paper introduces a novel Functional Graph Convolutional Network (funGCN) framework that combines Functional Data Analysis and Graph Convolutional Networks to address the complexities of multi-task and multi-modal learning in digital health and longitudinal studies. With the growing importance…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2021

A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression

Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte

NeurIPS 2021poster

Feature Selection and Functional Data Analysis are two dynamic areas of research, with important applications in the analysis of large and complex data sets. Straddling these two areas, we propose a new highly efficient algorithm to perform Group Elastic Net with application to function-on-scalar fe…

Cited by 7 Source ↗PDF ↗Scholar ↗