Concert, Jockey Club Auditorium, the Hong Kong Polytechnic University, 2023
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Published in submitted, 2024
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. (2024). Adaptive debiased lasso in high-dimensional GLMs with streaming data. To submit.
Published in KDD2026, 2025
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. (2025). Learning Guarantee of Reward Modeling Using Deep Neural Networks. arXiv preprint arXiv:2505.06601.
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Workshop, University 1, Department, 2015
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