120 citations · 388 across the 34 of their papers we have counts for
42 papers
Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning
Anshuka Rangi, Haifeng Xu, Long Tran-Thanh +1
To understand the security threats to reinforcement learning (RL) algorithms, this paper studies poisoning attacks to manipulate \emph{any} order-optimal learning algorithm towards…
A Random Adaptation Perspective on Distributed Averaging
Rohit Parasnis, Ashwin Verma, Massimo Franceschetti +1
We propose a random adaptation variant of time-varying distributed averaging dynamics in discrete time. We show that this leads to novel interpretations of fundamental concepts in…
A Perron-Frobenius Theorem for Strongly Aperiodic Stochastic Chains
Rohit Parasnis, Massimo Franceschetti, Behrouz Touri
We derive a generalization of the Perron-Frobenius theorem to time-varying row-stochastic matrices as follows: using Kolmogorov's concept of absolute probability sequences, which a…
Usefulness of the Age-Structured SIR Dynamics in Modelling COVID-19
Rohit Parasnis, Ryosuke Kato, Amol Sakhale +2
We examine the age-structured SIR model, a variant of the classical Susceptible-Infected-Recovered (SIR) model of epidemic propagation, in the context of COVID-19. In doing so, we…
Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification
Anshuka Rangi, Long Tran-Thanh, Haifeng Xu +1
We study bandit algorithms under data poisoning attacks in a bounded reward setting. We consider a strong attacker model in which the attacker can observe both the selected actions…
Sequential Choice Bandits with Feedback for Personalizing users' experience
Anshuka Rangi, Massimo Franceschetti, Long Tran-Thanh
In this work, we study sequential choice bandits with feedback. We propose bandit algorithms for a platform that personalizes users' experience to maximize its rewards. For each ac…