Vertical Reasoning Enhanced Learning, Generation and Scientific Discovery

Title: Vertical Reasoning Enhanced Learning, Generation and Scientific Discovery

Abstract: Automated reasoning and machine learning are two fundamental pillars of artificial intelligence. Despite much recent progress, the full integration of reasoning and learning is beyond reach. This talk presents three cases where integrated vertical reasoning significantly enhances learning. Our first case is in neural generation, where state-of-the-art models struggle to generate pleasing images while satisfying complex specifications. We introduce Spatial Reasoning INtegrated Generator (SPRING). SPRING embeds a spatial reasoning module inside the deep generative network to reason about object locations. This embedded approach guarantees constraint satisfaction, offers interpretability, and facilitates zero-shot transfer learning. Our second case is in AI-driven scientific discovery, where we embed vertical reasoning to expedite symbolic regression. Vertical reasoning builds from reduced models that involve a subset of variables (or processes) to full models inspired by the human scientific approach. Vertical discovery outperforms horizontal ones at discovering equations involving many variables and complex processes, especially in learning PDEs in computational materials science. In the third case, we demonstrate that vertical reasoning enables constant approximation guarantees in solving Satisfiable Modulo Counting (SMC). SMC involves model counting as predicates in Boolean satisfiability. It encompasses many problems that require both symbolic decision-making and statistical reasoning, e.g., stochastic optimization, hypothesis testing, solving quantal-response leader-follower games, and learning (inverse reinforcement learning) with provable guarantees. Using vertical reasoning that streamlines XOR constraints, our proposed XOR-SMC reduces highly intractable SMC problems into solving satisfiability instances, while obtaining a constant approximation guarantee.

Bio: Dr. Yexiang Xue is an assistant professor in the Department of Computer Science, Purdue University. The goal of Dr. Xue’s research is to bridge large-scale constraint-based reasoning with state-of-the-art machine learning techniques to enable intelligent agents to make optimal decisions in high-dimensional and uncertain real-world applications. More specifically, Dr Xue’s research focuses on scalable and accurate probabilistic reasoning, statistical modeling of data, and robust decision-making under uncertainty. His work is motivated by key problems across multiple scientific domains, ranging from artificial intelligence, machine learning, renewable energy, materials science, crowdsourcing, citizen science, urban computing, ecology, to behavioral econometrics. Dr. Xue obtained his PhD from Cornell in 2018. Recently, Dr. Xue has been focusing on developing cross-cutting computational methods, with an emphasis in the areas of computational sustainability and AI-driven scientific discovery