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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About
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Posts
Tutorial: Build your first neural network from scratch
Published:
In this episode, we want to investigate a simple working neural network (NN) anatomically, without the use of any deep learning package. You might find it very interesting to design every single element that is essential to perform forward and backward propagation. Every piece of component can be put up together like playing a LEGO game!
LaTeX on Github Pages+minimal mistakes+MathJax
Published:
To have $\LaTeX$ equations on this website, which is run on Github Pages using Jekyll and AcademicPages (a fork of Minimal Mistakes), I had to make a few guesses, so here are the secrets.
music
HKGNA Concert
Concert, Jockey Club Auditorium, the Hong Kong Polytechnic University, 2023
Featured Music:
- Borodin Polovetsian Dances from Prince Igor, Act II No. 8 & 17
- Russo Street Music, Op. 65, 1st & 4th mvt
Intermission - J. Williams Escapades for saxophone and orchestra
- A. L. Webber The Phantom of the Opera (arranged by C.Cluster)
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Adaptive debiased lasso in high-dimensional GLMs with streaming data
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.
Learning guarantee of reward modeling using deep neural networks
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.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
I am not teaching, would you teach me?
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.