Assistant Professor, New York University
2 papers at NeurIPS 2025
We combine Monte Carlo and regression-based methods to get a flexible estimator which achieves state-of-the-art performance.
We propose a one-line modification of the ubiquitous beam search method for graph-based near neighbor search. Our new method yields strong theoretical approximation guarantees and better practical performance.
