79 citations · 519 across the 58 of their papers we have counts for
8 papers · 1 filter
Minimal Expected Regret in Linear Quadratic Control
Yassir Jedra, Alexandre Proutiere
We consider the problem of online learning in Linear Quadratic Control systems whose state transition and state-action transition matrices and may be initially unknown. We…
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…
Online Learning of Optimally Diverse Rankings
Stefan Magureanu, Alexandre Proutiere, Marcus Isaksson +1
Search engines answer users' queries by listing relevant items (e.g. documents, songs, products, web pages, ...). These engines rely on algorithms that learn to rank items so as to…
Regret Analysis in Deterministic Reinforcement Learning
Damianos Tranos, Alexandre Proutiere
We consider Markov Decision Processes (MDPs) with deterministic transitions and study the problem of regret minimization, which is central to the analysis and design of optimal lea…
Navigating to the Best Policy in Markov Decision Processes
Aymen Al Marjani, Aurélien Garivier, Alexandre Proutiere
We investigate the classical active pure exploration problem in Markov Decision Processes, where the agent sequentially selects actions and, from the resulting system trajectory, a…
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…