1 citations · 1 across the 10 of their papers we have counts for
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Memory-Efficient Subspace Optimization with Residual Correction: From Momentum SGD to Muon
Sifan Yang, Hongyi Tao, Dingzhi Yu +5
Memory-efficient subspace optimization has emerged as a practical approach to reducing the memory cost of LLM training while preserving optimization performance. Previous algorithm…
Efficient Preconditioned Optimization Methods with Frequent Directions
Sifan Yang, Yuanyu Wan, Yibo Wang +1
Adaptive optimization methods have received considerable attention in recent years due to their strong empirical performance. However, they generally require maintaining a precondi…
Communication-Efficient Agnostic Federated Learning via Faster Convergence and Compression
Haomin Bai, Junyan Sun, Sifan Yang +2
Agnostic federated learning (AFL) seeks a model that performs reliably across heterogeneous workers, but communication remains a bottleneck. We improve communication efficiency…
From Switching to Dynamic Regret: A Simple Reduction via Unbiased Random Sequences
Yibo Wang, Wenhao Yang, Sifan Yang +3
In non-stationary online learning, dynamic regret has attracted increasing attention as a measure of how well an online learner performs against a time-varying comparator sequence.…
Efficient Multinomial Logistic Bandit via Frequent Directions
Linzhe He, Yu-Jie Zhang, Sifan Yang +1
This paper studies efficient online algorithms for multinomial logistic bandits (MLogB), where the feedback distribution over outcomes follows a multinomial logistic model of…
Distributed Online Convex Optimization with Compressed Communication: Optimal Regret and Applications
Sifan Yang, Dan-Yue Li, Lijun Zhang
Distributed online convex optimization (D-OCO) is a powerful paradigm for modeling distributed scenarios with streaming data. However, the communication cost between local learners…
Distributed Online Convex Optimization with Efficient Communication: Improved Algorithm and Lower bounds
Sifan Yang, Wenhao Yang, Wei Jiang +1
We investigate distributed online convex optimization with compressed communication, where learners connected by a network collaboratively minimize a sequence of global loss fu…