3 papers
cs.LG2026
Which Algorithms Can Graph Neural Networks Learn?
Solveig Wittig, Antonis Vasileiou, Robert R. Nerem +4
In recent years, there has been growing interest in understanding neural architectures' ability to learn to execute discrete algorithms, a line of work often referred to as neural…
cs.DS2025
Differentiable Extensions with Rounding Guarantees for Combinatorial Optimization over Permutations
Robert R. Nerem, Zhishang Luo, Akbar Rafiey +1
Continuously extending combinatorial optimization objectives is a powerful technique commonly applied to the optimization of set functions. However, few such methods exist for exte…
cs.LG2025
Graph neural networks extrapolate out-of-distribution for shortest paths
Robert R. Nerem, Samantha Chen, Sanjoy Dasgupta +1
Neural networks (NNs), despite their success and wide adoption, still struggle to extrapolate out-of-distribution (OOD), i.e., to inputs that are not well-represented by their trai…