DDPS | “Vertical Reasoning Enhanced Learning, Generation and Scientific Discovery”

DDPS Talk date: May 10, 2024
Speaker: Yexiang Xue (Purdue University, https://www.cs.purdue.edu/homes/yexia...
Description: Automated reasoning and machine learning are two fundamental pillars of artificial intelligence. Despite much recent progress, building autonomous agents fully integrating reasoning and learning is still beyond reach. This talk presents three cases where integrated vertical reasoning significantly enhances learning. In our first case, we introduce Spatial Reasoning INtegrated Generator (SPRING), which embeds a spatial reasoning module inside a deep generative network for image generation, to ensure constraint satisfaction, offer interpretability, and facilitate zero-shot transfer learning. In the second case, we embed vertical reasoning to expedite symbolic regression, and to learn Partial Differential Equations (PDEs) for materials science applications. In the third case, we demonstrate vertical reasoning via streamlining XOR constraints enables solvers with constant approximation guarantees for Satisfiable Modulo Counting (SMC), an important problem class integrating symbolic and statistical AI.
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 techniques, 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. 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.
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