5 citations · 5 across the 12 of their papers we have counts for
16 papers
Hidden Spin-Valley Hall Effect and Gate-Switchable Electric Hall Effect in 2D Altermagnets
Jiajun Lu, Mu Tian, Chaoxi Cui +3
Hidden beneath the vanishing charge Hall response of altermagnets are spin-up and spin-down Hall currents of opposite sign---concealed, not absent---whose cancellation is enforced…
Order-Optimal Systematic Permutation Codes for Correcting t Deletions
Bolin Wu, Quan D. Bui, Kai Niu +2
This paper investigates the construction of full-systematic permutation codes capable of correcting multiple deletions under two complementary models, namely symbol-invariant delet…
Relative Generalization Invariance of LLM Pretraining
Fengzhuo Zhang, Shuche Wang, Shenggui Li +7
Large Language Model (LLM) pretraining performance is jointly shaped by three components of the training triplet: the optimizer, model architecture, and training data stream. Howev…
Muon Learns More Robust and Transferable Features than Adam
Tianyu Ruan, Fengzhuo Zhang, Shuche Wang +1
Muon has recently emerged as a state-of-the-art optimizer for pretraining Large Language Models (LLMs) and vision classifiers. Despite its efficiency advantage over Adam and SGD, t…
Why Muon Outperforms Adam: A Curvature Perspective
Shuche Wang, Fengzhuo Zhang, Jiaxiang Li +2
Muon improves training efficiency over Adam in large language-model training by about two times, but the local geometric source of this advantage remains unclear. Our work takes a…
Online Learning with Gradient-Variation Interval Regret
Yan-Feng Xie, Shuche Wang, Peng Zhao +1
This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over every time interval. We propose…
List Reconstruction Problem with List Size Two
Binh Vu, Shuche Wang, Van Khu Vu
The problem of computing the cardinality of the intersection of multiple balls in the Hamming space has attracted a lot of attention recently due to their applications in the list…
Bandit Convex Optimization with Gradient Prediction Adaptivity
Shuche Wang, Adarsh Barik, Vincent Y. F. Tan
Bandit convex optimization (BCO) is a fundamental online learning framework with partial feedback, where the learner observes only the loss incurred at the chosen decision point in…
Parameter-free Algorithms for the Stochastically Extended Adversarial Model
Shuche Wang, Adarsh Barik, Peng Zhao +1
We develop the first parameter-free algorithms for the Stochastically Extended Adversarial (SEA) model, a framework that bridges adversarial and stochastic online convex optimizati…
Muon Outperforms Adam in Tail-End Associative Memory Learning
Shuche Wang, Fengzhuo Zhang, Jiaxiang Li +6
The Muon optimizer is consistently faster than Adam in training Large Language Models (LLMs), yet the mechanism underlying its success remains unclear. This paper demystifies this…
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent
Shuche Wang, Vincent Y. F. Tan
Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across sev…
Permutation and Multi-permutation Codes Correcting Multiple Deletions
Shuche Wang, The Nguyen, Yeow Meng Chee +1
Permutation codes in the Ulam metric, which can correct multiple deletions, have been investigated extensively recently. In this work, we are interested in the maximum size of perm…
Codes for Correcting Limited-Magnitude Sticky Deletions
Shuche Wang, Van Khu Vu, Vincent Y. F. Tan
Codes for correcting sticky insertions/deletions and limited-magnitude errors have attracted significant attention due to their applications of flash memories, racetrack memories,…
Codes for Correcting Asymmetric Adjacent Transpositions and Deletions
Shuche Wang, Van Khu Vu, Vincent Y. F. Tan
Codes in the Damerau--Levenshtein metric have been extensively studied recently owing to their applications in DNA-based data storage. In particular, Gabrys, Yaakobi, and Milenkovi…
Non-binary Codes for Correcting a Burst of at Most t Deletions
Shuche Wang, Yuanyuan Tang, Jin Sima +2
The problem of correcting deletions has received significant attention, partly because of the prevalence of these errors in DNA data storage. In this paper, we study the problem of…
Low-redundancy codes for correcting multiple short-duplication and edit errors
Yuanyuan Tang, Shuche Wang, Hao Lou +2
Due to its higher data density, longevity, energy efficiency, and ease of generating copies, DNA is considered a promising storage technology for satisfying future needs. However,…