Full Professor, New York University
3 papers at NeurIPS 2025
A new framework for analyzing and proving length generalization bounds.
This paper proposes a novel learning-augmented deterministic and truthful mechanism for the strategic unrelated machine scheduling problem using fewer predictions to achieve the best possible consistency and robustness.
We provide improved upper and lower bounds for contextual pricing with adversarial corruptions.
