Hsiu-Hsuan Yeh

Hsiu-Hsuan Yeh

I am devoted to understanding the intuition behind each concept.

Presentations

Louvain Method

May 28, 2024

Course Presentation, Final Presentation of ECON7217, Economic Analysis of Social Networks, Taipei, Taiwan

The Louvain Method is one of the most widely adopted community detection algorithms. Its popularity stems from its efficiency and ease of comprehension. Despite being intuitive, most of the technical details are missing in the original paper. Therefore, the goal of this presentation is to derive all relevant formulas, such as the modularity gain for a node moving out of the old community, moving into the new community, and the overall modularity gain for a single inter-community move for a node. Moreover, I conclude this presentation by conveying the intuition for a positive modularity gain movement.

Introduction to Utilizing LLMs in Research

January 26, 2024

Talk, online,

This talk aims to share my experience in constructing prompts to instruct LLMs. The topics include, but are not limited to: common prompting strategies, popular LLMs that are not only used for conducting research but also serve as the backend technology for software, such as chatbots, code debugging agents, data science analytics, comparsion between prompting and fine-tuning, and more. Additionally, I briefly demonstrate how I leverage the prevailing LangChain Python API to implement the Few-Shot Chain-of-Thought prompting.

Introduction to Classical ML and Its Application in Econometric Research

June 14, 2023

Talk, Microeconomics Study Group at Institute of Economics, Academia Sinica, Taipei, Taiwan

In this talk, I first introduce several basic concepts related to classical machine learning algorithms, such as overfitting, regularization, and adaptive boosting. I also showcase how to implement adaptive boosting in the Julia programming language to provide a more concrete understanding of how it works. Then, I move on to introduce a labor economics paper that utilizes machine learning algorithms to predict the treatment and control groups of the minimum wage policy based on demographic characteristics. Finally, I demonstrate how I employ this two-stage framework, which uses various machine learning algorithms to predict treatment and control groups used in the next stage of causal inference on Taiwanese data.