8 papers
Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?
Dingzhi Yu, Hongyi Tao, Yuanyu Wan +2
AdamW is the de facto optimizer for training large language models (LLMs), yet the theory behind it still lives mostly in finite-variance regimes. This is increasingly unsatisfying…
Mirror Descent Under Generalized Smoothness
Dingzhi Yu, Wei Jiang, Hongyi Tao +2
Smoothness is crucial for attaining fast rates in first-order optimization. However, many optimization problems in modern machine learning involve non-smooth objectives. Recent stu…
Sign-Based Optimizers Are Effective Under Heavy-Tailed Noise
Dingzhi Yu, Hongyi Tao, Yuanyu Wan +2
While adaptive gradient methods are the workhorse of modern machine learning, sign-based optimization algorithms such as Lion and Muon have recently demonstrated superior empirical…
When and Why SignSGD Outperforms SGD: A Theoretical Study Based on -norm Lower Bounds
Hongyi Tao, Dingzhi Yu, Lijun Zhang
Sign-based optimization algorithms, such as SignSGD and Muon, have garnered significant attention for their remarkable performance in training large foundation models. Despite this…
StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models
Dingzhi Yu, Rui Pan, Yuxing Liu +1
Sign-based optimization algorithms, such as SignSGD, have garnered significant attention for their remarkable performance in distributed learning and training large foundation mode…
Improved Approximate Regret for Decentralized Online Continuous Submodular Maximization via Reductions
Yuanyu Wan, Yu Shen, Dingzhi Yu +2
To expand the applicability of decentralized online learning, previous studies have proposed several algorithms for decentralized online continuous submodular maximization (D-OCSM)…