Yexiang Xue – Combining Reasoning and Learning for Data Science and Decision Making: Integrating Concepts from AI, Sustainability, and Scientific Discovery
Problems at the intersection of reasoning, optimization, and learning often involve multi-stage inference and are therefore highly intractable. I will introduce a novel computational framework, based on embeddings, to tackle multi-stage inference problems. As a first example, I present a novel way to encode the reward allocation problem for a two-stage organizer-agent game-theoretic framework as a single stage optimization problem. The encoding embeds an approximation of the agents’ decision-making process into the organizer’s problem. We apply this methodology to eBird, a well-established citizen-science program for collecting bird observations, as a game called Avicaching. Our AI-based reward allocation was shown highly effective, surpassing the expectations of the eBird organizers and bird conservation experts. As a second example, I present a novel constant approximation algorithm to solve the so-called Marginal Maximum-A-Posteriori (MMAP) problem for finding the optimal policy maximizing the expectation of a stochastic objective. To tackle this problem, I propose the embedding of its intractable counting subproblems as queries to NP-oracles subject to additional XOR constraints. As a result, the entire problem is encoded as a single NP-equivalent optimization. The approach outperforms state-of-the-art solvers based on variational inference as well as MCMC sampling on probabilistic inference benchmarks, deep learning applications, as well as on a novel decision-making application in network design for wildlife conservation. Lastly, I will talk about how a novel integration of reasoning and learning has led to the discovery of new solar light absorbers by solving a dimensionality reduction problem to characterize the crystal structures of metal oxide materials using X-ray diffraction data.
Yexiang Xue is a Ph.D. candidate in the Department of Computer Science at Cornell University, working with Professors Carla Gomes and Bart Selman. Upon graduation, he will join Purdue University as an assistant professor in computer science starting Fall 2018. His research aims at combining large-scale constraint-based reasoning and optimization 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, his research focuses on scalable and accurate probabilistic reasoning techniques, statistical modeling of data, and robust decision-making under uncertainty. Mr. Xue’s work is motivated by key problems across multiple scientific domains, including artificial intelligence, machine learning, renewable energy, materials science, citizen science, urban computing, and ecology, with an emphasis on developing cross-cutting computational methods for applications in the areas of computational sustainability and scientific discovery. Mr. Xue’s work received the Innovative Application Award at IAAI-17 and was featured as the cover article and the Editor’s Choice in the journal Combinatorial Science of the American Chemical Society.