Professor, University of Pennsylvania
3 papers at NeurIPS 2025
We present an efficient judgmetn distribution estimation method for LLM ensembles.
We develop a fine-grained f-DP analysis for decentralized federated learning, improving privacy–utility trade-offs under random walk communication and extending to settings with dependent noise.
Introduce and apply Goodness-of-Fit (GoF) tests to watermark detection, and explore their effectiveness across diverse conditions.