1 citations · 1 across the 6 of their papers we have counts for
6 papers
Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions
Di Wu, Ling Liang, Haizhao Yang
Bayesian Optimal Experimental Design (BOED) provides a rigorous framework for decision-making tasks in which data acquisition is often the critical bottleneck, especially in resour…
OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents
Raghav Thind, Youran Sun, Ling Liang +1
Optimization plays a vital role in scientific research and practical applications. However, formulating a concrete optimization problem described in natural language into a mathema…
From Equations to Insights: Unraveling Symbolic Structures in PDEs with LLMs
Rohan Bhatnagar, Ling Liang, Krish Patel +1
Motivated by the remarkable success of artificial intelligence (AI) across diverse fields, the application of AI to solve scientific problems, often formulated as partial different…
PINS: Proximal Iterations with Sparse Newton and Sinkhorn for Optimal Transport
Di Wu, Ling Liang, Haizhao Yang
Optimal transport (OT) is a widely used tool in machine learning, but computing high-accuracy solutions for large instances remains costly. Entropic regularization and the Sinkhorn…
PNOD: An Efficient Projected Newton Framework for Exact Optimal Experimental Designs
Ling Liang, Haizhao Yang
Computing the exact optimal experimental design has been a longstanding challenge in various scientific fields. This problem, when formulated using a specific information function,…
Accelerating Multi-Block Constrained Optimization Through Learning to Optimize
Ling Liang, Cameron Austin, Haizhao Yang
Learning to Optimize (L2O) approaches, including algorithm unrolling, plug-and-play methods, and hyperparameter learning, have garnered significant attention and have been successf…