Associate Professor, University of Michigan - Ann Arbor
1 paper at NeurIPS 2025
We introduce random search neural networks (RSNNs), a more efficient and expressive alternative to random walk neural networks (RWNNs) that achieves strong performance on sparse graphs with significantly fewer samples.
