PhD student, Stanford University
2 papers at NeurIPS 2025
We formalize the notion of size generalization in the context of data-driven algorithm selection and prove size generalization guarantees for three clustering algorithms and two max-cut algorithms.
We develop and analyze a scalable algorithm for multi-agent RL by sampling from the mean-field distribution of the agents to overcome the curse of dimensionality.
