2 citations · 2 across the 11 of their papers we have counts for
11 papers
Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less
Yuxing Liu, Jianyu Wang, Tong Zhang
Optimizers play an important role in both pretraining and finetuning stages when training large language models (LLMs). In this paper, we present an observation that full finetunin…
StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models
Dingzhi Yu, Rui Pan, Yuxing Liu +2
Sign-based optimization algorithms, such as SignSGD, have garnered attention for their performance in distributed learning and training large foundation models. Despite their empir…
Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergences
Jiarui Cao, Zixuan Wei, Yuxin Liu
We reveal a precise mathematical framework about a new family of generative models which we call Gradient Flow Drifting. With this framework, we prove an equivalence between the re…
Unbiased Gradient Low-Rank Projection
Rui Pan, Yang Luo, Yuxing Liu +2
Memory-efficient optimization is critical for training increasingly large language models (LLMs). A popular strategy involves gradient low-rank projection, storing only the project…
Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
Yuxing Liu, Yuze Ge, Rui Pan +2
Learning rate warmup is a popular and practical technique in training large-scale deep neural networks. Despite the huge success in practice, the theoretical advantages of this str…
GUIDE: Towards Scalable Advising for Research Ideas
Yaowenqi Liu, Bingxu Meng, Rui Pan +4
The field of AI research is advancing at an unprecedented pace, enabling automated hypothesis generation and experimental design across diverse domains such as biology, mathematics…
ASGO: Adaptive Structured Gradient Optimization
Kang An, Yuxing Liu, Rui Pan +4
Training deep neural networks is a structured optimization problem, because the parameters are naturally represented by matrices and tensors rather than by vectors. Under this stru…
AdaGrad under Anisotropic Smoothness
Yuxing Liu, Rui Pan, Tong Zhang
Adaptive gradient methods have been widely adopted in training large-scale deep neural networks, especially large foundation models. Despite the huge success in practice, their the…
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition
Yunyan Bai, Yuxing Liu, Luo Luo
This paper considers the optimization problem of the form , where satisfies the…
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
Rui Pan, Yuxing Liu, Xiaoyu Wang +1
Heavy-ball momentum with decaying learning rates is widely used with SGD for optimizing deep learning models. In contrast to its empirical popularity, the understanding of its theo…
On the Complexity of Decentralized Smooth Nonconvex Finite-Sum Optimization
Luo Luo, Yunyan Bai, Lesi Chen +2
We study the decentralized optimization problem , where the local function on the -th a…