Published in under major revision at Journal of the American Statistical Association, 2026
Why does A beat B, B beat C, but C beat A? In standard theory, this is a paradox. In our framework, it is simply a missing dimension.
Recommended citation: Luo, Y. Fang, X., Han, R. & Xu, Y. Deep Ranking with Heterogeneous Effect. Under major revision at Journal of the American Statistical Association.
Published in under major revision at The American Statistician, 2026
Do you follow the ATP tennis tour? Have you ever placed a bet at the race track? Who is the G.O.A.T.? Whether it’s ranking sports legends or training the latest Large Language Models (LLMs), the challenge is the same: how do we find the best when we cannot compare everyone against everyone?
Recommended citation: Fang, X., Han, R., Luo, Y. & Xu, Y. (2026). Recent Advances in the Bradley–Terry Model: Theory, Algorithms, and Applications. arXiv preprint arXiv:2601.14727.
Published in KDD, 2026
Architecture‑dependent, non-asymptotic regret bounds that can be sharpened under a simple margin condition (when human preferences are more “clear” than you think).
Recommended citation: Luo, Y., Ge, Y., Han, R. & Shen, G. (2026). Learning Guarantee of Reward Modeling Using Deep Neural Networks. SIGKDD2026 Research Track (to appear).
Published in J. Am. Statist. Assoc., 2026
Real-time learning from streaming data! We propose a one-pass, time- and memory-efficient method for high-dimensional GLMs—Adaptive Debiased Lasso (ADL)—that updates coefficients and standard errors on arrival via stochastic gradients with online debiasing.
Recommended citation: Han, R., Luo, L., Luo, Y., Lin, Y., & Huang, J. (2026). Adaptive debiased lasso in high-dimensional GLMs with streaming data. J. Am. Statist. Assoc. (to appear).