Gyumin Lee

A Semi-Supervised Kernel Two-Sample Test

Mon, 06 Jan 2025 00:00:00 +0000

This paper is still in preparation. This paper will be available to read in the openreview link in my “bio” page(link with book logo).

Introduction to Statistics

Sun, 01 Sep 2024 00:00:00 +0000

Instructor: Professor Mijung Kim (Dept. of Applied Statistics, Yonsei University)

Textbook: “Probability and Statistics for Engineers and Scientists” by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying E. Ye

Topics: Descriptive statistics & graphical diagnostics, probability & random variables (discrete and continuous), sampling & sampling distribution, one- and two-sample estimation, one- and two-sample tests of hypotheses

Assessment: Online/Offline class attendance (9%) + Weekly ALEKS assignment (21%) + Midterm (35%) + Final (35%)

For Students: Please regularly check the Announcements section on the LearnUS course page. It contains important updates and information relevant to the course.

If you encounter any issues or have further questions, feel free to email me. I will respond as soon as possible.

Mathematical Statistics II

Sun, 01 Sep 2024 00:00:00 +0000

Instructor: Professor Ilmun Kim (Dept. of Applied Statistics, Yonsei University)

Textbook: “Introduction to Mathematical Statistics” by Robert V. Hogg

Topics: Statistical inference including maximum likelihood function, sufficiency, optimal tests of hypotheses.

Assessment: Online/Offline class attendance (20%) + Exam 1 (20%) + Exam 2 (20%) + Exam 3 (20%)+ Exam 4 (20%)

For Students: Please regularly check the Announcements section on the LearnUS course page. It contains important updates and information relevant to the course.

If you encounter any issues or have further questions, feel free to email me. I will respond as soon as possible.

A Semi-Supervised Kernel Two-Sample Test

Wed, 05 Jun 2024 10:00:00 +0000

Click on the Slides button above to view the built-in slides feature.

Projects

Sun, 19 May 2024 00:00:00 +0000

Mathematical Statistics II

Sat, 02 Mar 2024 00:00:00 +0000

Instructor: Professor Ilmun Kim (Dept. of Applied Statistics, Yonsei University)

Textbook: “Introduction to Mathematical Statistics” by Robert V. Hogg

Topics: Statistical inference including maximum likelihood function, sufficiency, optimal tests of hypotheses.

Assessment: Online/Offline class attendance (20%) + Exam 1 (20%) + Exam 2 (20%) + Exam 3 (20%)+ Exam 4 (20%)

For Students: Please regularly check the Announcements section on the LearnUS course page. It contains important updates and information relevant to the course.

If you encounter any issues or have further questions, feel free to email me. I will respond as soon as possible.

A Semi-Supervised Kernel Two-Sample Test

Wed, 22 Nov 2023 08:30:00 +0000

Click on the Slides button above to view the built-in slides feature.

A Semi-Supervised Kernel Two-Sample Test

Wed, 22 Nov 2023 08:30:00 +0000

Click on the Slides button above to view the built-in slides feature.

HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts

Sat, 28 Oct 2023 00:00:00 +0000

🎉 Easily create your own simple yet highly customizable blog

Fri, 27 Oct 2023 00:00:00 +0000

Welcome 👋

Table of Contents

Overview

The Hugo Blox website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site
The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS
You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more

Get Started

👉 Create a new site
📚 Personalize your site
💬 Chat with the Hugo Blox community or Hugo community
🐦 Twitter: @GetResearchDev @GeorgeCushen #MadeWithHugoBlox
💡 Request a feature or report a bug for Hugo Blox
⬆️ Updating Hugo Blox? View the Update Guide and Release Notes

Crowd-funded open-source software

To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.

As a token of appreciation for sponsoring, you can unlock these awesome rewards and extra features 🦄✨

Ecosystem

Bibtex To Markdown: Automatically import publications from BibTeX

Inspiration

Learn what other creators are building with this template.

Features

Page builder - Create anything with no-code blocks and elements
Edit any type of content - Blog posts, publications, talks, slides, projects, and more!
Create content in Markdown, Jupyter, or RStudio
Plugin System - Fully customizable color and font themes
Display Code and Math - Code syntax highlighting and LaTeX math supported
Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more!
Beautiful Site - Simple and refreshing one-page design
Industry-Leading SEO - Help get your website found on search engines and social media
Media Galleries - Display your images and videos with captions in a customizable gallery
Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site
Multi-language - 35+ language packs including English, 中文, and Português
Multi-user - Each author gets their own profile page
Privacy Pack - Assists with GDPR
Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects
One-Click Deployment - No servers. No databases. Only files.

Themes

Hugo Blox and its templates come with automatic day (light) and night (dark) mode built-in. Visitors can choose their preferred mode by clicking the sun/moon icon in the header.

Choose a stunning theme and font for your site. Themes are fully customizable.

License

Released under the MIT license.

Pandas

Thu, 26 Oct 2023 00:00:00 +0000

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures.

PyTorch

Thu, 26 Oct 2023 00:00:00 +0000

PyTorch is a Python package that provides tensor computation (like NumPy) with strong GPU acceleration.

scikit-learn

Thu, 26 Oct 2023 00:00:00 +0000

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

🧠 Sharpen your thinking with a second brain

Thu, 26 Oct 2023 00:00:00 +0000

Create a personal knowledge base and share your knowledge with your peers.

Hugo Blox web framework empowers you with one of the most flexible note-taking capabilities out there.

Create a powerful knowledge base that works on top of a local folder of plain text Markdown files.

Use it as your second brain, either publicly sharing your knowledge with your peers via your website, or via a private GitHub repository and password-protected site just for yourself.

Mindmaps

Hugo Blox supports a Markdown extension for mindmaps.

With this open format, can even edit your mindmaps in other popular tools such as Obsidian.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Anh here’s a more advanced mindmap with formatting, code blocks, and math:


```markmap
- Mindmaps
- Links
- [Hugo Blox Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$
```

renders as

- Mindmaps
- Links
- [Hugo Blox Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Highlighting

Highlight important text with mark:

<mark>Highlighted text</mark>

Callouts

Use callouts (aka asides, hints, or alerts) to draw attention to notes, tips, and warnings.

By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.

{{% callout note %}}
A Markdown aside is useful for displaying notices, hints, or definitions to your readers.
{{% /callout %}}

renders as

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Or use the warning callout type so your readers don’t miss critical details:

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

📈 Communicate your results effectively with the best data visualizations

Wed, 25 Oct 2023 00:00:00 +0000

Hugo Blox is designed to give technical content creators a seamless experience. You can focus on the content and Hugo Blox handles the rest.

Use popular tools such as Plotly, Mermaid, and data frames.

Charts

Hugo Blox supports the popular Plotly format for interactive data visualizations. With Plotly, you can design almost any kind of visualization you can imagine!

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the Plotly JSON Editor useful.

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Data Frames

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

👩🏼‍🏫 Teach academic courses

Tue, 24 Oct 2023 00:00:00 +0000

Hugo Blox Builder is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube D2vj0WcvH5c >}}

Bilibili:

{{< bilibili BV1WV4y1r7DF >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your page’s folder, and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. Enable math by setting the math: true option in your page’s front matter, or enable math for your entire site by toggling math in your config/_default/params.yaml file:

features:
 math:
 enable: true

To render inline or block math, wrap your LaTeX math with $...$ or $$...$$, respectively.

Example math block:

$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$

renders as

$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$

Example inline math $\nabla F(\mathbf{x}_{n})$ renders as $\nabla F(\mathbf{x}_{n})$.

Example multi-line math using the math linebreak (\\):

$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

Experience

Tue, 24 Oct 2023 00:00:00 +0000

✅ Manage your projects

Mon, 23 Oct 2023 00:00:00 +0000

Easily manage your projects - create ideation mind maps, Gantt charts, todo lists, and more!

Ideation

Hugo Blox supports a Markdown extension for mindmaps.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done, des1, 2014-01-06,2014-01-08
Active :active, des2, 2014-01-07, 3d
Parallel 1 : des3, after des1, 1d
Parallel 2 : des4, after des1, 1d
Parallel 3 : des5, after des3, 1d
Parallel 4 : des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
 - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

Introduction to Statistics

Fri, 01 Sep 2023 00:00:00 +0000

Instructor: Professor Mijung Kim (Dept. of Applied Statistics, Yonsei University)

Textbook: “Probability and Statistics for Engineers and Scientists” by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying E. Ye

Assessment: Online/Offline class attendance (9%) + Weekly ALEKS assignment (21%) + Midterm (35%) + Final (35%)

For Students: Please regularly check the Announcements section on the LearnUS course page. It contains important updates and information relevant to the course.

If you encounter any issues or have further questions, feel free to email me. I will respond as soon as possible.

Mathematical Statistics II

Fri, 01 Sep 2023 00:00:00 +0000

Instructor: Professor Ilmun Kim (Dept. of Applied Statistics, Yonsei University)

Textbook: “Introduction to Mathematical Statistics” by Robert V. Hogg

Topics: Statistical inference including maximum likelihood function, sufficiency, optimal tests of hypotheses.

Assessment: Online/Offline class attendance (20%) + Exam 1 (20%) + Exam 2 (20%) + Exam 3 (20%)+ Exam 4 (20%)

For Students: Please regularly check the Announcements section on the LearnUS course page. It contains important updates and information relevant to the course.

If you encounter any issues or have further questions, feel free to email me. I will respond as soon as possible.

Semi-Supervised Learning - Inference

Tue, 29 Aug 2023 11:00:00 +0000

Click on the Slides button above to view the built-in slides feature.

Machine Learning/AI-Based Research Methodologies in Child and Family Studies

Wed, 23 Aug 2023 00:00:00 +0000

Instructor: Professor Wonkyung Jang (Dept. Instructional Leadership and Academic Curriculum, The University of Oklahoma)

Textbook: Lecture notes

Topics: Introduction to Data Science and R Programming (Importance and concepts of data science, Introduction to the R programming language, its basic syntax, and RStudio environment setup. Understanding data types, variables, and data structures, Overview of data visualization algorithms, Statistical pitfalls in social sciences: Common errors and strategies to avoid them. Hands-on practice with basic statistical methods (e.g., T-test, ANOVA, Regression) using R), Machine Learning/AI Techniques for Structured and Unstructured Data (Introduction to supervised and unsupervised learning methodologies, Overview of text mining: Text preprocessing and natural language processing techniques. Hands-on machine learning practice using R, focusing on U.S. child and family studies datasets)

For Students: Before the first session, it is recommended to install the data science program R (https://cran.yu.ac.kr/) and RStudio (https://posit.co/download/rstudio-desktop/).

If you encounter any issues or have further questions, feel free to email me. I will respond as soon as possible.

Mathematical Statistics II

Thu, 02 Mar 2023 00:00:00 +0000

Instructor: Professor Ilmun Kim (Dept. of Applied Statistics, Yonsei University)

Textbook: “Introduction to Mathematical Statistics” by Robert V. Hogg

Topics: Statistical inference including maximum likelihood function, sufficiency, optimal tests of hypotheses.

Assessment: Online/Offline class attendance (20%) + Exam 1 (20%) + Exam 2 (20%) + Exam 3 (20%)+ Exam 4 (20%)

For Students: Please regularly check the Announcements section on the LearnUS course page. It contains important updates and information relevant to the course.

If you encounter any issues or have further questions, feel free to email me. I will respond as soon as possible.

Time Series Forecasting with Hypernetworks Generating Parameters in Advance

Tue, 22 Nov 2022 00:00:00 +0000

Mathematical Statistics I

Thu, 01 Sep 2022 00:00:00 +0000

Instructor: Professor Yongho Jeon (Dept. of Applied Statistics, Yonsei University)

Textbook: “Introduction to Mathematical Statistics” by Robert V. Hogg

Topics: (Chapter 1 ~ Chapter 4) Probability and distributions, multivariate distributions, exponential family, mixture distributions, order statistics, statistical inference, convergence

Assessment: Online/Offline class attendance (5%) + Exam 1 (20%) + Exam 2 (25%) + Exam 3 (25%)+ Exam 4 (25%)

For Tutees: The class will be structured to include a review of the week’s content followed by solving related exercises. If there are any specific topics or issues you’d like to discuss, please let me know at least a day in advance.

If you encounter any issues or have further questions, feel free to email me. I will respond as soon as possible.

Survival Data Analysis

Thu, 01 Sep 2022 00:00:00 +0000

Instructor: Professor Sangwook Kang (Dept. of Applied Statistics, Yonsei University)

Textbook: Lecture notes

Topics: Censoring & truncation, distributions used in survival analysis, parametric statistical analysis, Kaplan-Meier estimator of survival function, estimation of hazard function, logrank test, accelerated failure time/ proportional hazards / proportional odds model, cox regression models & model diagnostics

Assessment: Class attendance (5%) + Course project (10%) + Assignments (25%) + Midterm (30%)+ Final (30%)

For Tutees: The class will be structured to include a review of the week’s content followed by solving related exercises. Additionally, we will practice applying new methodologies using the R program, so please make sure to bring your laptop. If there are any specific topics or issues you’d like to discuss, please let me know at least a day in advance.

If you encounter any issues or have further questions, feel free to email me. I will respond as soon as possible.

Gyumin Lee

A Semi-Supervised Kernel Two-Sample Test

Introduction to Statistics

Mathematical Statistics II

A Semi-Supervised Kernel Two-Sample Test

Projects

Mathematical Statistics II

A Semi-Supervised Kernel Two-Sample Test

A Semi-Supervised Kernel Two-Sample Test

HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts

🎉 Easily create your own simple yet highly customizable blog

Overview

Get Started

Crowd-funded open-source software

❤️ Click here to become a sponsor and help support Hugo Blox’s future ❤️

Ecosystem

Inspiration

Features

Themes

License

Pandas

PyTorch

scikit-learn

🧠 Sharpen your thinking with a second brain

Mindmaps

Highlighting

Callouts

Did you find this page helpful? Consider sharing it 🙌

📈 Communicate your results effectively with the best data visualizations

Charts

Diagrams

Data Frames

Did you find this page helpful? Consider sharing it 🙌

👩🏼‍🏫 Teach academic courses

Video

Podcast

Test students

Math

Code

Inline Images

Did you find this page helpful? Consider sharing it 🙌

Experience

✅ Manage your projects

Ideation

Diagrams

Todo lists

Did you find this page helpful? Consider sharing it 🙌

Introduction to Statistics

Mathematical Statistics II

Semi-Supervised Learning - Inference

Machine Learning/AI-Based Research Methodologies in Child and Family Studies

Mathematical Statistics II

Time Series Forecasting with Hypernetworks Generating Parameters in Advance

Mathematical Statistics I

Survival Data Analysis