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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 SGD in high-dimensional GLMs with streaming data
Published in submitted, 2024
Online statistical inference refers to an inferential method that updates the model parameters as data is sequentially available. This approach has broad applications in network security, quantitative finance, and recommendation systems. In contrast to offline techniques where the entire dataset is used for training, online learning algorithms are anticipated to offer computational efficiency while still delivering statistical results comparable to their offline counterparts.
Recommended citation: Han, R., Luo, L., Luo, Y., Lin, Y., & Huang, J. (2024). Adaptive debiased SGD in high-dimensional GLMs with streaming data. To submit.
Learning guarantee of reward modeling using deep neural networks
Published in submited, 2025
In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks. We establish a novel non-asymptotic regret bound for deep reward estimators in a non-parametric setting, which depends explicitly on the network architecture. Furthermore, to underscore the critical importance of clear human beliefs, we introduce a margin-type condition that assumes the conditional winning probability of the optimal action in pairwise comparisons is significantly distanced from 1/2. This condition enables a sharper regret bound, which substantiates the empirical efficiency of Reinforcement Learning from Human Feedback and highlights clear human beliefs in its success. Notably, this improvement stems from high-quality pairwise comparison data implied by the margin-type condition, is independent of the specific estimators used, and thus applies to various learning algorithms and models.
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.