9 citations · 40 across the 14 of their papers we have counts for
6 papers · 1 filter
Energy-based Surprise Minimization for Multi-Agent Value Factorization
Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis +1
Multi-Agent Reinforcement Learning (MARL) has demonstrated significant success in training decentralised policies in a centralised manner by making use of value factorization metho…
Implicit Feedback Deep Collaborative Filtering Product Recommendation System
Karthik Raja Kalaiselvi Bhaskar, Deepa Kundur, Yuri Lawryshyn
In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the…
Maximum Mutation Reinforcement Learning for Scalable Control
Karush Suri, Xiao Qi Shi, Konstantinos N. Plataniotis +1
Advances in Reinforcement Learning (RL) have demonstrated data efficiency and optimal control over large state spaces at the cost of scalable performance. Genetic methods, on the o…
Market Graph Clustering Via QUBO and Digital Annealing
Seo Hong, Pierre Miasnikof, Roy Kwon +1
Our goal is to find representative nodes of a market graph that best replicate the returns of a broader market graph (index), a common task in the financial industry. We model our…
Graph Distances and Clustering
Pierre Miasnikof, Alexander Y. Shestopaloff, Leonidas Pitsoulis +1
With a view on graph clustering, we present a definition of vertex-to-vertex distance which is based on shared connectivity. We argue that vertices sharing more connections are clo…
Graph Clustering Via QUBO and Digital Annealing
Pierre Miasnikof, Seo Hong, Yuri Lawryshyn
This article empirically examines the computational cost of solving a known hard problem, graph clustering, using novel purpose-built computer hardware. We express the graph cluste…