15 citations · 18 across the 6 of their papers we have counts for
7 papers
On the Fragility of Tasks under Composition: Implicit Budgeting in Language Models
Chenpan Li, Thies Brockmoeller, Stefanie Jegelka +2
Modern language models are increasingly asked to handle several tasks within a single prompt. Doing so requires solving each task correctly while integrating the resulting answers…
Self-Supervised Combinatorial Optimization with Constraints via Frank-Wolfe
Akbar Rafiey, Yifei Xu, Nikolaos Karalias
Self-supervised learning for combinatorial optimization has emerged as a promising paradigm for solving discrete optimization problems with neural networks, but a central challenge…
Geometric Algorithms for Neural Combinatorial Optimization with Constraints
Nikolaos Karalias, Akbar Rafiey, Yifei Xu +4
Self-Supervised Learning (SSL) for Combinatorial Optimization (CO) is an emerging paradigm for solving combinatorial problems using neural networks. In this paper, we address a cen…
Are Graph Neural Networks Optimal Approximation Algorithms?
Morris Yau, Nikolaos Karalias, Eric Lu +2
In this work we design graph neural network architectures that capture optimal approximation algorithms for a large class of combinatorial optimization problems, using powerful alg…
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Nikolaos Karalias, Joshua Robinson, Andreas Loukas +1
Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (1) not naturally a…
Partition and Code: learning how to compress graphs
Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias +1
Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainabl…
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
Nikolaos Karalias, Andreas Loukas
Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances. This work proposes an unsupervised learning fra…