100 citations · 229 across the 26 of their papers we have counts for
28 papers
Revisiting LARS for Large Batch Training Generalization of Neural Networks
Khoi Do, Duong Nguyen, Hoa Nguyen +3
This paper explores Large Batch Training techniques using layer-wise adaptive scaling ratio (LARS) across diverse settings, uncovering insights. LARS algorithms with warm-up tend t…
Label driven Knowledge Distillation for Federated Learning with non-IID Data
Minh-Duong Nguyen, Quoc-Viet Pham, Dinh Thai Hoang +3
In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especially when applied to massive IoT networks; and (2) how to be robust against an envi…
Temporal Multiresolution Graph Neural Networks For Epidemic Prediction
Truong Son Hy, Viet Bach Nguyen, Long Tran-Thanh +1
In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph st…
Optimising Resource Management for Embedded Machine Learning
Lei Xun, Long Tran-Thanh, Bashir M Al-Hashimi +1
Machine learning inference is increasingly being executed locally on mobile and embedded platforms, due to the clear advantages in latency, privacy and connectivity. In this paper,…
Incremental Training and Group Convolution Pruning for Runtime DNN Performance Scaling on Heterogeneous Embedded Platforms
Lei Xun, Long Tran-Thanh, Bashir M Al-Hashimi +1
Inference for Deep Neural Networks is increasingly being executed locally on mobile and embedded platforms due to its advantages in latency, privacy and connectivity. Since modern…
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…
Zealotry and Influence Maximization in the Voter Model: When to Target Zealots?
Guillermo Romero Moreno, Edoardo Manino, Long Tran-Thanh +1
In this paper, we study influence maximization in the voter model in the presence of biased voters (or zealots) on complex networks. Under what conditions should an external contro…
Fuzzy c-Means Clustering for Persistence Diagrams
Thomas Davies, Jack Aspinall, Bryan Wilder +1
Persistence diagrams concisely represent the topology of a point cloud whilst having strong theoretical guarantees, but the question of how to best integrate this information into…
Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs
Saaduddin Mahmud, Md. Mosaddek Khan, Moumita Choudhury +2
Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete vari…
Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek
Dong Quan Vu, Patrick Loiseau, Alonso Silva +1
Resource allocation games such as the famous Colonel Blotto (CB) and Hide-and-Seek (HS) games are often used to model a large variety of practical problems, but only in their one-s…
Defending with Shared Resources on a Network
Minming Li, Long Tran-Thanh, Xiaowei Wu
In this paper we consider a defending problem on a network. In the model, the defender holds a total defending resource of R, which can be distributed to the nodes of the network.…
Streaming Bayesian Inference for Crowdsourced Classification
Edoardo Manino, Long Tran-Thanh, Nicholas R. Jennings
A key challenge in crowdsourcing is inferring the ground truth from noisy and unreliable data. To do so, existing approaches rely on collecting redundant information from the crowd…
AED: An Anytime Evolutionary DCOP Algorithm
Saaduddin Mahmud, Moumita Choudhury, Md. Mosaddek Khan +2
Evolutionary optimization is a generic population-based metaheuristic that can be adapted to solve a wide variety of optimization problems and has proven very effective for combina…
Social Cost Guarantees in Smart Route Guidance
Paolo Serafino, Carmine Ventre, Long Tran-Thanh +3
We model and study the problem of assigning traffic in an urban road network infrastructure. In our model, each driver submits their intended destination and is assigned a route to…
Manipulating a Learning Defender and Ways to Counteract
Jiarui Gan, Qingyu Guo, Long Tran-Thanh +2
In Stackelberg security games when information about the attacker's payoffs is uncertain, algorithms have been proposed to learn the optimal defender commitment by interacting with…
Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek
Dong Quan Vu, Patrick Loiseau, Alonso Silva +1
Resource allocation games such as the famous Colonel Blotto (CB) and Hide-and-Seek (HS) games are often used to model a large variety of practical problems, but only in their one-s…
Selfish Mining in Proof-of-Work Blockchain with Multiple Miners: An Empirical Evaluation
Tin Leelavimolsilp, Long Tran-Thanh, Sebastian Stein +1
Proof-of-Work blockchain, despite its numerous benefits, is still not an entirely secure technology due to the existence of Selfish Mining (SM) strategies that can disrupt the syst…
Imitative Follower Deception in Stackelberg Games
Jiarui Gan, Haifeng Xu, Qingyu Guo +3
Information uncertainty is one of the major challenges facing applications of game theory. In the context of Stackelberg games, various approaches have been proposed to deal with t…
On the Inducibility of Stackelberg Equilibrium for Security Games
Qingyu Guo, Jiarui Gan, Fei Fang +3
Strong Stackelberg equilibrium (SSE) is the standard solution concept of Stackelberg security games. As opposed to the weak Stackelberg equilibrium (WSE), the SSE assumes that the…
Unifying the stochastic and the adversarial Bandits with Knapsack
Anshuka Rangi, Massimo Franceschetti, Long Tran-Thanh
This paper investigates the adversarial Bandits with Knapsack (BwK) online learning problem, where a player repeatedly chooses to perform an action, pays the corresponding cost, an…
Designing the Game to Play: Optimizing Payoff Structure in Security Games
Zheyuan Ryan Shi, Ziye Tang, Long Tran-Thanh +2
Effective game-theoretic modeling of defender-attacker behavior is becoming increasingly important. In many domains, the defender functions not only as a player but also the design…
On the Preliminary Investigation of Selfish Mining Strategy with Multiple Selfish Miners
Tin Leelavimolsilp, Long Tran-Thanh, Sebastian Stein
Eyal and Sirer's selfish mining strategy has demonstrated that Bitcoin system is not secure even if 50% of total mining power is held by altruistic miners. Since then, researchers…
Efficiency of active learning for the allocation of workers on crowdsourced classification tasks
Edoardo Manino, Long Tran-Thanh, Nicholas R. Jennings
Crowdsourcing has been successfully employed in the past as an effective and cheap way to execute classification tasks and has therefore attracted the attention of the research com…