1 code implementation • 19 Apr 2024 • Huilin Yin, Jiaxiang Li, Pengju Zhen, Jun Yan
This method searches the sensitive region of trajectory prediction models and generates the adversarial trajectories by using the vehicle-following method and incorporating information about forthcoming trajectories.
1 code implementation • 18 Feb 2024 • Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen
In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has become standard.
no code implementations • 25 Sep 2023 • Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
We present Zeroth-order Riemannian Averaging Stochastic Approximation (\texttt{Zo-RASA}) algorithms for stochastic optimization on Riemannian manifolds.
no code implementations • 3 Nov 2022 • Jiaxiang Li, Shiqian Ma, Tejes Srivastava
The iteration complexity of the proposed algorithm for obtaining an $\epsilon$-stationary point is analyzed under mild assumptions.
no code implementations • 12 Oct 2022 • Wei Huang, Jiaxiang Li, Shuming Jiao, Zibang Zhang
Single-pixel imaging (SPI) is a novel optical imaging technique by replacing the pixelated sensor array in a conventional camera with a single-pixel detector.
no code implementations • 12 Jun 2022 • Jiaxiang Li, Shiqian Ma
This paper studies FL over Riemannian manifolds, which finds important applications such as federated PCA and federated kPCA.
no code implementations • 7 May 2022 • Shuming Jiao, Jiaxiang Li, Wei Huang, Zibang Zhang
Single-pixel imaging (SPI) is a novel optical imaging technique by replacing a two-dimensional pixelated sensor with a single-pixel detector and pattern illuminations.
no code implementations • 25 Mar 2020 • Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
We consider stochastic zeroth-order optimization over Riemannian submanifolds embedded in Euclidean space, where the task is to solve Riemannian optimization problem with only noisy objective function evaluations.