I am a computer science researcher and graph enthusiast.
My work focuses on the foundations and applications of machine learning to structured problems. I aim to find ways to exploit (graph, constraint, group) information, with the ultimate goal of designing algorithms that can learn from fewer data. I am also fascinated by the theoretical analysis of neural networks and in using them to solve hard combinatorial and bio-engineering problems (especially protein design).
Selected recent works
A Loukas, M. Poiitis, S. Jegelka. What training reveals about neural network complexity. To appear at NeurIPS 2021. (preprint, bibtex, video)
G. Bouritsas, A Loukas, N. Karalias, M. Bronstein. Partition and code: learning how to compress graphs. To appear at NeurIPS 2021. (preprint, bibtex)
Y. Dong, J.B. Cordonnier, A. Loukas. Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth. Oral at ICML 2021. (preprint, bibtex, blogpost)
N. Karalias, A. Loukas. Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. Oral at NeurIPS 2020. (paper, bibtex, video starts at 36:54, blogpost, slides, talk at Yale)