Brief Industry Paper: optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms. - Ao Zhou; Jianlei Yang 001; Yeqi Gao; Tong Qiao; Yingjie Qi; Xiaoyi Wang; Yunli Chen; Pengcheng Dai; Weisheng Zhao; Chunming Hu (RTAS, 2021)

Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms. - Ao Zhou; Jianlei Yang 001; Yeqi Gao; Tong Qiao; Yingjie Qi; Xiaoyi Wang; Yunli Chen; Pengcheng Dai; Weisheng Zhao; Chunming Hu (CoRR, 2021)

An O(k log n) Time Fourier Set Query Algorithm. - Yeqi Gao; Zhao Song 002; Baocheng Sun 002 (CoRR, 2022)

Solving Tensor Low Cycle Rank Approximation. - Yichuan Deng 002; Yeqi Gao; Zhao Song 002 (IEEE Big Data, 2023)

An Over-parameterized Exponential Regression. - Yeqi Gao; Sridhar Mahadevan; Zhao Song 002 (CoRR, 2023)

Differentially Private Attention Computation. - Yeqi Gao; Zhao Song 002; Xin Yang (CoRR, 2023)

In-Context Learning for Attention Scheme: from Single Softmax Regression to Multiple Softmax Regression via a Tensor Trick. - Yeqi Gao; Zhao Song 002; Shenghao Xie (CoRR, 2023)

Fast Quantum Algorithm for Attention Computation. - Yeqi Gao; Zhao Song 002; Xin Yang; Ruizhe Zhang 001 (CoRR, 2023)

GradientCoin: A Peer-to-Peer Decentralized Large Language Models. - Yeqi Gao; Zhao Song 002; Junze Yin (CoRR, 2023)

A Sublinear Adversarial Training Algorithm. - Yeqi Gao; Lianke Qin; Zhao Song 002; Yitan Wang (ICLR, 2024)

Quantum Speedup for Spectral Approximation of Kronecker Products. - Yeqi Gao; Zhao Song 002; Ruizhe Zhang 001 (CoRR, 2024)

Binary Hypothesis Testing for Softmax Models and Leverage Score Models. - Yeqi Gao; Yuzhou Gu; Zhao Song 002 (CoRR, 2024)

An Iterative Algorithm for Rescaled Hyperbolic Functions Regression. - Yeqi Gao; Zhao Song 002; Junze Yin (AISTATS, 2025)

A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time. - Yeqi Gao; Zhao Song 002; Weixin Wang 002; Junze Yin (UAI, 2025)