1 citations · 1 across the 11 of their papers we have counts for
11 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…
LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard
Varun Rao, Youran Sun, Mahendra Kumar +3
This paper investigates the application of large language models (LLMs) to financial tasks. We fine-tuned foundation models using the Open FinLLM Leaderboard as a benchmark. Buildi…
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…
NewVEM: A Newton Vertex Exchange Method for a Class of Constrained Self-Concordant Minimization Problems
Ling Liang, Kim-Chuan Toh, Haizhao Yang
We propose \textbf{NewVEM}, a Newton vertex exchange method for efficiently solving self-concordant minimization problems under generalized simplex constraints. The algorithm featu…
Nesterov's Accelerated Jacobi-Type Methods for Large-scale Symmetric Positive Semidefinite Linear Systems
Ling Liang, Qiyuan Pang, Kim-Chuan Toh +1
Solving symmetric positive semidefinite linear systems is an essential task in many scientific computing problems. While Jacobi-type methods, including the classical Jacobi method…
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
Ling Liang, Haizhao Yang
We consider a regularized expected reward optimization problem in the non-oblivious setting that covers many existing problems in reinforcement learning (RL). In order to solve suc…
Neural Network Approximation for Pessimistic Offline Reinforcement Learning
Di Wu, Yuling Jiao, Li Shen +2
Deep reinforcement learning (RL) has shown remarkable success in specific offline decision-making scenarios, yet its theoretical guarantees are still under development. Existing wo…