2 citations · 4 across the 5 of their papers we have counts for
10 papers
Neural Predictive Monitoring under Partial Observability
Francesca Cairoli, Luca Bortolussi, Nicola Paoletti
We consider the problem of predictive monitoring (PM), i.e., predicting at runtime future violations of a system from the current state. We work under the most realistic settings w…
Certification of Iterative Predictions in Bayesian Neural Networks
Matthew Wicker, Luca Laurenti, Andrea Patane +3
We consider the problem of computing reach-avoid probabilities for iterative predictions made with Bayesian neural network (BNN) models. Specifically, we leverage bound propagation…
On Guaranteed Optimal Robust Explanations for NLP Models
Emanuele La Malfa, Agnieszka Zbrzezny, Rhiannon Michelmore +2
We build on abduction-based explanations for ma-chine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP).…
MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas
Hongkai Chen, Nicola Paoletti, Scott A. Smolka +1
Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, wh…
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
Pallavi Bagga, Nicola Paoletti, Bedour Alrayes +1
We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an act…
Neural Simplex Architecture
Dung T. Phan, Radu Grosu, Nils Jansen +3
We present the Neural Simplex Architecture (NSA), a new approach to runtime assurance that provides safety guarantees for neural controllers (obtained e.g. using reinforcement lear…