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Sharper Analysis of Single-Loop Methods for Bilevel Optimization
Yubo Zhou, Jun Shu, Luo Luo +4
Bilevel optimization underpins many machine learning applications, including hyperparameter optimization, meta-learning, neural architecture search, and reinforcement learning. Whi…
Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning
Yao Fu, Chunxia Zhang, Junmin Liu +3
Generalization remains a pivotal challenge in deep learning, where traditional optimizers like Stochastic Gradient Descent (SGD) often converge to sharp minima, leading to overfitt…
On the Convergence of Single-Loop Stochastic Bilevel Optimization with Approximate Implicit Differentiation
Yubo Zhou, Luo Luo, Guang Dai +1
Stochastic Bilevel Optimization has emerged as a fundamental framework for meta-learning and hyperparameter optimization. Despite the practical prevalence of single-loop algorithms…
ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
Zhishen Sun, Sizhe Dang, Guang Dai +1
Reinforcement learning (RL) has become a key training step for improving mathematical reasoning in large language models (LLMs), but it often has high GPU memory usage, which makes…
FZOO: Fast Zeroth-Order Optimizer for Fine-Tuning Large Language Models towards Adam-Scale Speed
Sizhe Dang, Yangyang Guo, Yanjun Zhao +4
Fine-tuning large language models (LLMs) often faces GPU memory bottlenecks: the backward pass of first-order optimizers like Adam increases memory usage to more than 10 times the…
Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization
Chengli Tan, Yubo Zhou, Haishan Ye +7
Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving. However, many studies suggest that they are prone…
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization
Haishan Ye, Yinghui Huang, Hao Di +1
We propose an enhanced zeroth-order stochastic Frank-Wolfe framework to address constrained finite-sum optimization problems, a structure prevalent in large-scale machine-learning…
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
Hao Di, Haishan Ye, Yueling Zhang +3
Variance reduction techniques are designed to decrease the sampling variance, thereby accelerating convergence rates of first-order (FO) and zeroth-order (ZO) optimization methods.…
Second-Order Fine-Tuning without Pain for LLMs:A Hessian Informed Zeroth-Order Optimizer
Yanjun Zhao, Sizhe Dang, Haishan Ye +3
Fine-tuning large language models (LLMs) with classic first-order optimizers entails prohibitive GPU memory due to the backpropagation process. Recent works have turned to zeroth-o…
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population
Hao Di, Yi Yang, Haishan Ye +1
Personalization aims to characterize individual preferences and is widely applied across many fields. However, conventional personalized methods operate in a centralized manner, po…