1 citations · 1 across the 4 of their papers we have counts for
4 papers
Learnable Koopman-Enhanced Transformer-Based Time Series Forecasting with Spectral Control
Ali Forootani, Raffaele Iervolino
This paper proposes a unified family of learnable Koopman operator parameterizations that integrate linear dynamical systems theory with modern deep learning forecasting architectu…
Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset
Ali Forootani, Raffaele Iervolino
Federated Learning (FL) enables collaborative model training across decentralized devices while preserving data privacy. However, traditional FL suffers from communication overhead…
Off-Policy Temporal Difference Learning for Perturbed Markov Decision Processes: Theoretical Insights and Extensive Simulations
Ali Forootani, Raffaele Iervolino, Massimo Tipaldi +1
Dynamic Programming suffers from the curse of dimensionality due to large state and action spaces, a challenge further compounded by uncertainties in the environment. To mitigate t…
Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning
Ali Forootani, Raffaele Iervolino
Federated Learning (FL) has emerged as a powerful paradigm for decentralized machine learning, enabling collaborative model training across diverse clients without sharing raw data…