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DRIFT: Disentangled Responsive-Invariant Flow Transport for Single-Cell Perturbation Prediction
Mustapha Bounoua, Giulio Franzese, Pietro Michiardi
Predicting cellular responses to perturbations is a central problem in cellular biology, with broad applications in systems biology and drug discovery. This task is challenging bec…
ALICE: In-context, Zero-shot, Mutual Information Estimation
Giulio Franzese, Simone Rossi, Pietro Michiardi
Estimating mutual information (MI) from samples is a central objective in a variety of scientific fields. Modern neural estimators are accurate in the large-data regime, but they f…
DIPHINE: Diffusion-based -ID Neural Estimator
Simon Pedro Galeano Munoz, Mustapha Bounoua, Giulio Franzese +2
Uncovering the true informational architecture of real-world complex systems requires disentangling how their components uniquely store, redundantly share, and synergistically inte…
Towards Diverse and Comprehensive Benchmarks for Mutual Information Estimation
Alberto Foresti, Ivan Butakov, Alexander Tolmachev +3
Mutual information (MI) estimation is a central problem in machine learning and statistics; however, existing benchmarks typically evaluate estimators on simplified, low-dimensiona…
Relative Entropy Estimation in Function Space: Theory and Applications to Trajectory Inference
Chao Wang, Luca Nepote, Giulio Franzese +1
Trajectory Inference (TI) seeks to recover latent dynamical processes from snapshot data, where only independent samples from time-indexed marginals are observed. In applications s…
Improved Sampling Schedules for Discrete Diffusion Models
Alberto Foresti, Mustapha Bounoua, Giulio Franzese +2
Discrete diffusion models have emerged as a powerful paradigm for generative modeling on sequence data; however, the information-theoretic principles governing their reverse proces…
TENDE: Transfer Entropy Neural Diffusion Estimation
Simon Pedro Galeano Munoz, Mustapha Bounoua, Giulio Franzese +2
Transfer entropy measures directed information flow in time series, and it has become a fundamental quantity in applications spanning neuroscience, finance, and complex systems ana…
Learning to Match Unpaired Data with Minimum Entropy Coupling
Mustapha Bounoua, Giulio Franzese, Pietro Michiardi
Multimodal data is a precious asset enabling a variety of downstream tasks in machine learning. However, real-world data collected across different modalities is often not paired,…
INFO-SEDD: Continuous Time Markov Chains as Scalable Information Metrics Estimators
Alberto Foresti, Giulio Franzese, Pietro Michiardi
Information-theoretic quantities play a crucial role in understanding non-linear relationships between random variables and are widely used across scientific disciplines. However,…
Latent Abstractions in Generative Diffusion Models
Giulio Franzese, Mattia Martini, Giulio Corallo +2
In this work we study how diffusion-based generative models produce high-dimensional data, such as an image, by implicitly relying on a manifestation of a low-dimensional set of la…
Information Theoretic Text-to-Image Alignment
Chao Wang, Giulio Franzese, Alessandro Finamore +2
Diffusion models for Text-to-Image (T2I) conditional generation have recently achieved tremendous success. Yet, aligning these models with user's intentions still involves a labori…
SI: Score-based O-INFORMATION Estimation
Mustapha Bounoua, Giulio Franzese, Pietro Michiardi
The analysis of scientific data and complex multivariate systems requires information quantities that capture relationships among multiple random variables. Recently, new informati…
MINDE: Mutual Information Neural Diffusion Estimation
Giulio Franzese, Mustapha Bounoua, Pietro Michiardi
In this work we present a new method for the estimation of Mutual Information (MI) between random variables. Our approach is based on an original interpretation of the Girsanov the…