- Daniel Daza and Thomas Kipf: A modular framework for unsupervised graph representation learning
- Dong Quan Vu, Patrick Loiseau, Alonso Silva, Long Tran-Thanh: Path-planning problems with side observations for resource allocation games
- Sephora Madjiheurem, Laura Toni: Representation Learning on Graphs: A reinforcement learning application
- Mariana Vargas Vieyra, Aurélien Bellet, Pascal Denis: Joint learning of the graph and the data Representation for graph-based semi-supervised learning
- Marco Bressan, Nicolò Cesa-Bianchi, Andrea Paudice, Fabio Vitale: Correlation clustering with adaptive similarity queries
- Kaige Yang, Xiaowen Dong, Laura Toni: Laplacian-regularized graph bandits: Algorithms and theoretical analysis
- Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He: Graph Structure Learning for GCNs
- Kinda Al_Sayed: An automated urban design model using generative networks and supervised machine learning
- Michaël Defferrard, Nathanaël Perraudin, Tomasz Kacprzak, Raphael Sgier: DeepSphere: Towards an equivariant graph-based spherical CNN
We look forward to welcoming you to London to participate in this stimulating workshop and offer a presentation possibility for the attendees.
We accept submission of 1-page extended abstracts that will be lightly reviewed based on relevance. Accepted contributions will be arranged in the format of poster presentations during the lunch slot on day 2. The workshop is a venue to share recent research results with no published proceedings. The workshop accepts submission of review papers if they are clearly identified as such.
This workshop is a forum intended to disseminate ideas to a broader audience and to exchange ideas and experience on the future path of the vibrant field of graph signal processing to online sequential decision strategies (and machine learning at large). We accept submission related to this topic.
Example of topics of interests are:
- sequential decision-making on and with graphs
- graph representation for online learning
- graph networks
- geometric deep learning
- active learning graphs with limited information