Moniqua: Modulo Quantized Communication in Decentralized SGD. - Yucheng Lu 003; Christopher De Sa (ICML, 2020)

MixML: A Unified Analysis of Weakly Consistent Parallel Learning. - Yucheng Lu 003; Jack Nash; Christopher De Sa (CoRR, 2020)

Towards Optimal Convergence Rate in Decentralized Stochastic Training. - Yucheng Lu 003; Zheng Li; Christopher De Sa (CoRR, 2020)

Variance Reduced Training with Stratified Sampling for Forecasting Models. - Yucheng Lu 003; Youngsuk Park; Lifan Chen; Yuyang Wang 001; Christopher De Sa; Dean P. Foster (ICML, 2021)

Optimal Complexity in Decentralized Training. - Yucheng Lu 003; Christopher De Sa (ICML, 2021)

Hyperparameter Optimization Is Deceiving Us, and How to Stop It. - A. Feder Cooper; Yucheng Lu 003; Jessica Forde; Christopher De Sa (NeurIPS, 2021)

Hyperparameter Optimization Is Deceiving Us, and How to Stop It. - A. Feder Cooper; Yucheng Lu 003; Christopher De Sa (CoRR, 2021)

Variance Reduction in Training Forecasting Models with Subgroup Sampling. - Yucheng Lu 003; Youngsuk Park; Lifan Chen; Yuyang Wang 001; Christopher De Sa; Dean P. Foster (CoRR, 2021)

A General Analysis of Example-Selection for Stochastic Gradient Descent. - Yucheng Lu 003; Si Yi Meng; Christopher De Sa (ICLR, 2022)

GraB: Finding Provably Better Data Permutations than Random Reshuffling. - Yucheng Lu 003; Wentao Guo; Christopher De Sa (NeurIPS, 2022)

Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. - Yucheng Lu 003; Conglong Li; Minjia Zhang; Christopher De Sa; Yuxiong He (ICLR, 2023)

STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition. - Yucheng Lu 003; Shivani Agrawal; Suvinay Subramanian; Oleg Rybakov; Christopher De Sa; Amir Yazdanbakhsh (ICML, 2023)

CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks. - Jue Wang; Yucheng Lu 003; Binhang Yuan; Beidi Chen; Percy Liang; Christopher De Sa; Christopher Ré; Ce Zhang 001 (ICML, 2023)

CD-GraB: Coordinating Distributed Example Orders for Provably Accelerated Training. - A. Feder Cooper; Wentao Guo; Khiem Pham; Tiancheng Yuan; Charlie Ruan; Yucheng Lu 003; Christopher De Sa (NeurIPS, 2023)

Scale up with Order: Finding Good Data Permutations for Distributed Training. - Wentao Guo; Khiem Pham; Yucheng Lu 003; Tiancheng Yuan; Charlie F. Ruan; Christopher De Sa (CoRR, 2023)

Decentralized Learning: Theoretical Optimality and Practical Improvements. - Yucheng Lu 003; Christopher De Sa (J. Mach. Learn. Res., 2023)