18 citations · 24 across the 14 of their papers we have counts for
8 papers · 1 filter
Constrained Deep Reinforcement Learning for Fronthaul Compression Optimization
Axel Grönland, Alessio Russo, Yassir Jedra +2
In the Centralized-Radio Access Network (C-RAN) architecture, functions can be placed in the central or distributed locations. This architecture can offer higher capacity and cost…
Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systems
Alessio Russo
In recent years, there has been a growing interest in the effects of data poisoning attacks on data-driven control methods. Poisoning attacks are well-known to the Machine Learning…
Self-Tuning Tube-based Model Predictive Control
Damianos Tranos, Alessio Russo, Alexandre Proutiere
We present Self-Tuning Tube-based Model Predictive Control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the leas…
Tube-Based Zonotopic Data-Driven Predictive Control
Alessio Russo, Alexandre Proutiere
We present a novel tube-based data-driven predictive control method for linear systems affected by a bounded addictive disturbance. Our method leverages recent results in the reach…
Balancing detectability and performance of attacks on the control channel of Markov Decision Processes
Alessio Russo, Alexandre Proutiere
We investigate the problem of designing optimal stealthy poisoning attacks on the control channel of Markov decision processes (MDPs). This research is motivated by the recent inte…
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study
Alessio Russo, Marco Molinari, Alexandre Proutiere
This work investigates the feasibility of using input-output data-driven control techniques for building control and their susceptibility to data-poisoning techniques. The analysis…
Poisoning Attacks against Data-Driven Control Methods
Alessio Russo, Alexandre Proutiere
This paper investigates poisoning attacks against data-driven control methods. This work is motivated by recent trends showing that, in supervised learning, slightly modifying the…
Minimizing Information Leakage of Abrupt Changes in Stochastic Systems
Alessio Russo, Alexandre Proutiere
This work investigates the problem of analyzing privacy of abrupt changes for general Markov processes. These processes may be affected by changes, or exogenous signals, that need…