From the 2 of 15 linked papers with an AI index.
16 citations · 16 across the 6 of their papers we have counts for
9 papers · 1 filter
Joint State-Parameter Inference Enhances Estimation Performance in Model-Based Digital Therapeutics for Type 1 Diabetes
Milad Banitalebi Dehkordi, Vihangkumar V. Naik, Manas Mejari +2
The paper introduces a recursive filtering method that jointly estimates glucose levels and model parameters in real time for type‑1 diabetes, using a Rao‑Blackwellized Stein Varia…
Learning reduced-order latent linear models for Kalman filtering of nonlinear systems
Manas Mejari, Milad Banitalebi Dehkordi, Dario Piga
The paper introduces an end-to-end learning framework that jointly trains an autoencoder and a reduced-order linear model to perform Kalman filtering directly in a low-dimensional…
From system models to class models: An in-context learning paradigm
Marco Forgione, Filippo Pura, Dario Piga
Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same cla…
Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation
Milad Banitalebi Dehkordi, Manas Mejari, Dario Piga
We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses Rao-Blackwellization techniqu…
Nonlinear System Identification Nano-drone Benchmark
Riccardo Busetto, Elia Cereda, Marco Forgione +3
We introduce a benchmark for system identification based on 75k real-world samples from the Crazyflie 2.1 Brushless nano-quadrotor, a sub-50g aerial vehicle widely adopted in robot…
Learning Low-Dimensional Embeddings for Black-Box Optimization
Riccardo Busetto, Manas Mejari, Marco Forgione +2
When gradient-based methods are impractical, black-box optimization (BBO) provides a valuable alternative. However, BBO often struggles with high-dimensional problems and limited t…