6 papers · 1 filter
Structured Adaptive Tensor Prediction for Streaming Data
Zhen Qin, Yang Chen
Matrix-valued time series arise in a wide range of applications, such as spatio-temporal data from medical imaging and geophysics. Existing methods are mainly designed for static s…
Learning to Adapt: In-Context Learning Beyond Stationarity
Zhen Qin, Jiachen Jiang, Zhihui Zhu
Transformer models have become foundational across a wide range of scientific and engineering domains due to their strong empirical performance. A key capability underlying their s…
On the Convergence of Gradient Descent on Learning Transformers with Residual Connections
Zhen Qin, Jinxin Zhou, Jiachen Jiang +1
Transformer models have emerged as fundamental tools across various scientific and engineering disciplines, owing to their outstanding performance in diverse applications. Despite…
In-Context Learning for Non-Stationary MIMO Equalization
Jiachen Jiang, Zhen Qin, Zhihui Zhu
Channel equalization is fundamental for mitigating distortions such as frequency-selective fading and inter-symbol interference. Unlike standard supervised learning approaches that…
A Scalable Factorization Approach for High-Order Structured Tensor Recovery
Zhen Qin, Michael B. Wakin, Zhihui Zhu
Tensor decompositions, which represent an -order tensor using approximately factors of much smaller dimensions, can significantly reduce the number of parameters. This is pa…
Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition
Zhen Qin, Zhihui Zhu
Recently, a tensor-on-tensor (ToT) regression model has been proposed to generalize tensor recovery, encompassing scenarios like scalar-on-tensor regression and tensor-on-vector re…