Associate Professor, The University of Warwick
2 papers at NeurIPS 2025
We develop efficient algorithms for non-uniformly sampling over directed acyclic graph structures, and use these along with results from online learning, to develop efficient algorithms for agnostically-learning Bayes nets in KL divergence.
Product distributions on n dimensions can be learned with sublinear samples if a sufficiently close distribution is provided as advice.
