Yucheng Lu

According to our database, Yucheng Lu authored at least 16 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Decentralized Learning: Theoretical Optimality and Practical Improvements.

Yucheng Lu, Christopher De Sa

J. Mach. Learn. Res., 2023

Scale up with Order: Finding Good Data Permutations for Distributed Training.

Wentao Guo, Khiem Pham, Yucheng Lu, Tiancheng Yuan, Charlie F. Ruan, Christopher De Sa

CoRR, 2023

CD-GraB: Coordinating Distributed Example Orders for Provably Accelerated Training.

A. Feder Cooper, Wentao Guo, Khiem Pham, Tiancheng Yuan, Charlie Ruan, Yucheng Lu, Christopher De Sa

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks.

Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang, Christopher De Sa, Christopher Ré, Ce Zhang

Proceedings of the International Conference on Machine Learning, 2023

STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition.

Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa, Amir Yazdanbakhsh

Proceedings of the International Conference on Machine Learning, 2023

Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam.

Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

GraB: Finding Provably Better Data Permutations than Random Reshuffling.

Yucheng Lu, Wentao Guo, Christopher De Sa

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A General Analysis of Example-Selection for Stochastic Gradient Descent.

Yucheng Lu, Si Yi Meng, Christopher De Sa

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Variance Reduction in Training Forecasting Models with Subgroup Sampling.

Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster

CoRR, 2021

Hyperparameter Optimization Is Deceiving Us, and How to Stop It.

A. Feder Cooper, Yucheng Lu, Christopher De Sa

CoRR, 2021

Hyperparameter Optimization Is Deceiving Us, and How to Stop It.

A. Feder Cooper, Yucheng Lu, Jessica Forde, Christopher De Sa

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Optimal Complexity in Decentralized Training.

Yucheng Lu, Christopher De Sa

Proceedings of the 38th International Conference on Machine Learning, 2021

Variance Reduced Training with Stratified Sampling for Forecasting Models.

Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Towards Optimal Convergence Rate in Decentralized Stochastic Training.

Yucheng Lu, Zheng Li, Christopher De Sa

CoRR, 2020

MixML: A Unified Analysis of Weakly Consistent Parallel Learning.

Yucheng Lu, Jack Nash, Christopher De Sa

CoRR, 2020

Moniqua: Modulo Quantized Communication in Decentralized SGD.

Yucheng Lu, Christopher De Sa

Proceedings of the 37th International Conference on Machine Learning, 2020