7 citations · 9 across the 13 of their papers we have counts for
6 papers · 1 filter
Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study
Viet Bach Nguyen, Truong Son Hy, Long Tran-Thanh +1
Modeling and simulations of pandemic dynamics play an essential role in understanding and addressing the spreading of highly infectious diseases such as COVID-19. In this work, we…
Achieving Better Regret against Strategic Adversaries
Le Cong Dinh, Tri-Dung Nguyen, Alain Zemkoho +1
We study online learning problems in which the learner has extra knowledge about the adversary's behaviour, i.e., in game-theoretic settings where opponents typically follow some n…
Invariant Lipschitz Bandits: A Side Observation Approach
Nam Phuong Tran, Long Tran-Thanh
Symmetry arises in many optimization and decision-making problems, and has attracted considerable attention from the optimization community: By utilizing the existence of such symm…
Multi-Player Bandits Robust to Adversarial Collisions
Shivakumar Mahesh, Anshuka Rangi, Haifeng Xu +1
Motivated by cognitive radios, stochastic Multi-Player Multi-Armed Bandits has been extensively studied in recent years. In this setting, each player pulls an arm, and receives a r…
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…
Online Markov Decision Processes with Non-oblivious Strategic Adversary
Le Cong Dinh, David Henry Mguni, Long Tran-Thanh +2
We study a novel setting in Online Markov Decision Processes (OMDPs) where the loss function is chosen by a non-oblivious strategic adversary who follows a no-external regret algor…