ML Seminar: Vertical Reasoning Enhanced Learning, Generation and Scientific Discovery

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Tuesday from 11 a.m. - 12 p.m. during the Spring 2024 semester.

This week's speaker, Yexiang Xue (Purdue University), will be giving a talk titled "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, building autonomous agents fully integrating reasoning and learning is still beyond reach. This talk presents three cases where integrated vertical reasoning significantly enhances learning. Our first application 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 which decides the locations of objects to be generated. Embedding symbolic reasoning into neural generation guarantees constraint satisfaction, offers interpretability, and facilitates zero-shot transfer learning. Our second application 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 human scientific approach. Demonstrated in computational materials science, vertical discovery outperforms horizontal ones at discovering equations involving many variables and complex processes. In the third application, we demonstrate 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, learning (and inverse reinforcement learning) with provable guarantees. Our proposed XOR-SMC reduces highly intractable SMC problems into solving satisfiability instances, with a constant approximation guarantee, using vertical reasoning that streamlines XOR constraints.  

Biography

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 in order 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.