DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems
Combinatorial Optimization (CO) problems are fundamentally important in numerous real-
world applications across diverse industries, characterized by entailing enormous solution …
world applications across diverse industries, characterized by entailing enormous solution …
Strategic use of payoff information in k-hop evolutionary Best-shot networked public goods game
Globalization has led to increasingly interconnected interactions among individuals. Their
payoffs are affected by the investment decision of themselves and their neighbors, which will …
payoffs are affected by the investment decision of themselves and their neighbors, which will …
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
VA Darvariu, S Hailes, M Musolesi - arXiv preprint arXiv:2404.06492, 2024 - arxiv.org
Graphs are a natural representation for systems based on relations between connected
entities. Combinatorial optimization problems, which arise when considering an objective …
entities. Combinatorial optimization problems, which arise when considering an objective …
Addressing implicit bias in adversarial imitation learning with mutual information
L Zhang, Q Liu, F Zhu, Z Huang - Neural Networks, 2023 - Elsevier
Adversarial imitation learning (AIL) is a powerful method for automated decision systems
due to training a policy efficiently by mimicking expert demonstrations. However, implicit bias …
due to training a policy efficiently by mimicking expert demonstrations. However, implicit bias …
Learning Unbiased Rewards with Mutual Information in Adversarial Imitation Learning
L Zhang, Q Liu, Z Huang, L Wu - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
A powerful method for automated decision systems is Adversarial Imitation Learning (AIL). It
is based on a generative adversarial framework that alternately optimizes a generator …
is based on a generative adversarial framework that alternately optimizes a generator …
A Perturbation-Based Policy Distillation Framework with Generative Adversarial Nets
L Zhang, Q Liu, X Zhang, Y Xu - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
We study the problem of imitation learning in automated decision systems, in which a
learner is trained to imitate an expert demonstrator. A widely used method is adversarial …
learner is trained to imitate an expert demonstrator. A widely used method is adversarial …
Learning to Optimise Networked Systems
VA Darvariu - 2023 - discovery.ucl.ac.uk
Many systems based on relations between connected entities find a natural representation
in graphs, which has led to the development of mathematical and statistical tools for …
in graphs, which has led to the development of mathematical and statistical tools for …
[PDF][PDF] Using Graph Neural Networks in Local Search for Edge-Based Relaxations of the Maximum Clique Problem
R Ettrich - 2022 - ac.tuwien.ac.at
This thesis investigates the utilization of Graph Neural Networks (GNNs) in a local
searchbased metaheuristic for edge-based relaxations of the Maximum Clique Problem …
searchbased metaheuristic for edge-based relaxations of the Maximum Clique Problem …