1 citations · 1 across the 4 of their papers we have counts for
5 papers
EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments
Deyao Zhu, Xin Zhou, Shengling Qin +44
Pretraining scaling laws reveal that model capability improves predictably with data and compute. But learning from real world environments after deployment remains far less unders…
Accelerating Optimization via Differentiable Stopping Time
Zhonglin Xie, Yiman Fong, Haoran Yuan +1
Optimization is an important module of modern machine learning applications. Tremendous efforts have been made to accelerate optimization algorithms. A common formulation is achiev…
Accelerated Natural Gradient Method for Parametric Manifold Optimization
Chenyi Li, Shuchen Zhu, Zhonglin Xie +1
Parametric manifold optimization problems frequently arise in various machine learning tasks, where state functions are defined on infinite-dimensional manifolds. We propose a unif…
OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling
Hongliang Lu, Zhonglin Xie, Yaoyu Wu +3
Despite the rapid development of large language models (LLMs), a fundamental challenge persists: the lack of high-quality optimization modeling datasets hampers LLMs' robust modeli…
ODE-based Learning to Optimize
Zhonglin Xie, Wotao Yin, Zaiwen Wen
Recent years have seen a growing interest in understanding acceleration methods through the lens of ordinary differential equations (ODEs). Despite the theoretical advancements, tr…