1 citations · 1 across the 4 of their papers we have counts for
6 papers
DeepKoopFormer: A Koopman Enhanced Transformer Based Architecture for Time Series Forecasting
Ali Forootani, Mohammad Khosravi, Masoud Barati
Time series forecasting plays a vital role across scientific, industrial, and environmental domains, especially when dealing with high-dimensional and nonlinear systems. While Tran…
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…
Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks
Ali Forootani, Mohammad Khosravi
Time series forecasting plays a critical role in domains such as energy, finance, and healthcare, where accurate predictions inform decision-making under uncertainty. Although Tran…
A Survey on Mathematical Reasoning and Optimization with Large Language Models
Ali Forootani
Mathematical reasoning and optimization are fundamental to artificial intelligence and computational problem-solving. Recent advancements in Large Language Models (LLMs) have signi…
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…