1 citations · 1 across the 10 of their papers we have counts for
11 papers
FDE-Bench: Evaluating End-to-End Delivery from Underspecified Real-World Business Requests
Huaiming Li, Can Huang, Chufan Wu +18
Real-world business requests often leave key requirements unstated because frontline practitioners struggle to articulate rules they know from experience. Forward-deployed engineer…
Optimization on Affine-Transversal Hilbert Submanifolds: Part I -- Theoretical Foundations
Yongcun Song, Luhao Xue, Xiaoming Yuan +1
In this paper, we establish the theoretical foundations for the generic optimization problem in a Hilbert space whose feasible set is an affine-transversal Hilbert submanifold give…
Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games
Haoran Li, Zengle Ge, Ziyang Zhang +10
Recent advances in LLM-driven code evolution have enabled automated discovery by iteratively generating and improving programs. However, applying these methods to adversarial multi…
Stochastic Gradient Descent with Momentum is Algorithmically Stable
Yunwen Lei, Zimeng Wang, Xiaoming Yuan
Stochastic gradient descent with momentum (SGDM) is one of the most widely used optimization algorithms in machine learning. While optimization properties of SGDM have been extensi…
Learning Theory of the SVRG: Generalization and Convergence Analysis
Yunwen Lei, Zimeng Wang, Xiaoming Yuan
Variance reduction (VR) methods employ stochastic gradients with decreasing variance, and they have been widely applied to solve large-scale optimization problems in machine learni…
A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction
Jinhui Ren, Huaiming Li, Yabin Liu +9
High-fidelity vehicle drag evaluation is constrained less by solver runtime than by workflow friction: geometry cleanup, meshing retries, queue contention, and reproducibility fail…
Learning to Control: The iUzawa-Net for Nonsmooth Optimal Control of Linear PDEs
Yongcun Song, Xiaoming Yuan, Hangrui Yue +1
We propose an optimization-informed deep neural network approach, named iUzawa-Net, aiming for the first solver that enables real-time solutions for a class of nonsmooth optimal co…
BestServe: Serving Strategies with Optimal Goodput in Collocation and Disaggregation Architectures
Xiannan Hu, Tianyou Zeng, Xiaoming Yuan +3
Serving large language models (LLMs) to millions of users requires efficient resource allocation and parallelism strategies. It is a labor intensive trial-and-error process to find…
A Single-Loop Stochastic Proximal Quasi-Newton Method for Large-Scale Nonsmooth Convex Optimization
Yongcun Song, Zimeng Wang, Xiaoming Yuan +1
We propose a new stochastic proximal quasi-Newton method for minimizing the sum of two convex functions in the particular context that one of the functions is the average of a larg…
An Operator Learning Approach to Nonsmooth Optimal Control of Nonlinear PDEs
Yongcun Song, Xiaoming Yuan, Hangrui Yue +1
Optimal control problems with nonsmooth objectives and nonlinear partial differential equation (PDE) constraints are challenging, mainly because of the underlying nonsmooth and non…
The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach
Yongcun Song, Xiaoming Yuan, Hangrui Yue
We study the combination of the alternating direction method of multipliers (ADMM) with physics-informed neural networks (PINNs) for a general class of nonsmooth partial differenti…