Peer-reviewedLicence not statedThe Annals of Statistics
Online tensor learning: Computational and statistical trade-offs, adaptivity and optimal regret
- 1Department of Statistics and Data Science, School of Management, Fudan University
- 2Department of Mathematics, Hong Kong University of Science and Technology
- 3Department of Statistics, University of Michigan
Related
- Statistical Testing for Multiple Instance Learning via Selective Inference with Applications to Computational Pathology
- Sign Betting for Misspecified Regression: Variance Adaptivity and Optimal Approximation
- Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivity
- Towards optimal selection schedules for online active learning
- Near-Optimal √K-Regret for Online Learning in Uninformed Markov Games
BibTeX
@article{li2026online,
title = {{Online tensor learning: Computational and statistical trade-offs, adaptivity and optimal regret}},
author = {Jingyang Li and Jian‐Feng Cai and Yang Chen and Dong Xia},
journal = {The Annals of Statistics},
year = {2026},
month = jun,
doi = {10.1214/25-aos2588},
url = {http://arxiv.org/abs/2306.03372},
}
RIS
TY - JOUR
TI - Online tensor learning: Computational and statistical trade-offs, adaptivity and optimal regret
AU - Jingyang Li
AU - Jian‐Feng Cai
AU - Yang Chen
AU - Dong Xia
PY - 2026
DA - 2026/06/01
JO - The Annals of Statistics
DO - 10.1214/25-aos2588
UR - http://arxiv.org/abs/2306.03372
SN - 0090-5364
SN - 2168-8966
ER -