Robust ML Under Uncertainty.

This paper develops a distributionally robust estimation method that minimizes worst-case conditional value-at-risk (CVaR) of estimation error when the true distribution is uncertain but lies within a Wasserstein ball. The method can be computed via tractable semidefinite programming and outperforms existing approaches on electricity price forecasting.

Asset Harvester converts sparse, real-world object observations from autonomous vehicle driving logs into complete 3D assets suitable for simulation. The system combines large-scale data curation, geometry-aware preprocessing, and a novel sparse-view-conditioned model (SparseViewDiT) to generate simulation-ready 3D objects from limited viewing angles.

This paper introduces DESPITE, a benchmark of over 12,000 tasks to evaluate safety in LLM-based robotic planning, revealing that even models with near-perfect planning ability produce dangerous plans 28% of the time. The study shows planning ability scales with model size while safety awareness remains relatively flat, creating a critical gap for deploying LLMs in robotics.

This paper introduces a GDP-based auditing framework that provides the first tight privacy audits for state-of-the-art differentially private synthetic data generators MST and AIM. The audits show a small gap between theoretical guarantees and empirical measurements in strong-privacy regimes.