1.1k citations · 1.1k across the 10 of their papers we have counts for
11 papers
DelistBench: Evaluating Search-Enabled LLMs for Auditable Corporate-Event Database Completion
Xuan Yao, Shuping Li, Yang Dai +2
Financial institutions need an independent way to detect missing, stale, and misclassified corporate-event records in vendor databases. We introduce Search-to-Record, a database-as…
Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch
Paul Brunzema, Louis Tiao, Nhat Le +3
Bayesian optimization (BO) has become the standard tool for sample-efficient optimization and owes its efficiency to uncertainty-aware search driven by generic statistical priors.…
Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems
Jiang Liu, John Martabano Landy, Yao Xuan +14
Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversion rates, and other optimization…
Quantum-Boosted High-Fidelity Deep Learning
Feng-ao Wang, Shaobo Chen, Yao Xuan +12
A fundamental limitation of probabilistic deep learning is its predominant reliance on Gaussian priors. This simplistic assumption prevents models from accurately capturing the com…
Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions
Gengchen Mai, Yao Xuan, Wenyun Zuo +5
Generating learning-friendly representations for points in space is a fundamental and long-standing problem in ML. Recently, multi-scale encoding schemes (such as Space2Vec and NeR…
Machine Learning and Polymer Self-Consistent Field Theory in Two Spatial Dimensions
Yao Xuan, Kris T. Delaney, Hector D. Ceniceros +1
A computational framework that leverages data from self-consistent field theory simulations with deep learning to accelerate the exploration of parameter space for block copolymers…
Towards General-Purpose Representation Learning of Polygonal Geometries
Gengchen Mai, Chiyu Jiang, Weiwei Sun +6
Neural network representation learning for spatial data is a common need for geographic artificial intelligence (GeoAI) problems. In recent years, many advancements have been made…
Pandemic Control, Game Theory and Machine Learning
Yao Xuan, Robert Balkin, Jiequn Han +2
Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this AMS Notices article, we focus…
Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions
Gengchen Mai, Yao Xuan, Wenyun Zuo +2
Generating learning-friendly representations for points in a 2D space is a fundamental and long-standing problem in machine learning. Recently, multi-scale encoding schemes (such a…
Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning Algorithm
Yao Xuan, Robert Balkin, Jiequn Han +2
Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this paper, we propose a multi-reg…
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
Shiyang Li, Xiaoyong Jin, Yao Xuan +4
Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. In this…