update

Author: rl-max

_bibliography/papers.bib

@@ -1,8 +1,28 @@
 ---
 ---
-
 @string{aps = {American Physical Society,}}
 
+@article{lee2024influence,
+  bibtex_show={true},
+  title={Understanding Impact of Human Feedback via Influence Functions},
+  author={Min, Taywon and Lee, Haeone and Ryu, Hanho and Kwon, Yongchan and Lee, Kimin},
+  journal={In submission.},
+  year={2024},
+  preview={},
+  html={},
+  pdf={}
+}
+
+@article{lee2024benchmarking,
+  bibtex_show={true},
+  title={Benchmarking Mobile Device Control Agents across Diverse Configurations},
+  author={Lee, Juyong and Min, Taywon and An, Minyong and Hahm, Dongyoon and Lee, Haeone and Kim, Changyeon and Lee, Kimin},
+  journal={ICLR 2024 Workshop on Generative Models for Decision Making.},
+  html={https://arxiv.org/abs/2404.16660},
+  pdf={2404.16660v2.pdf},
+  year={2024}
+}
+
 @article{lee2023comgan,
   bibtex_show={true},
   title={ComGAN: Toward GANs Exploiting Multiple Samples},
@@ -12,14 +32,4 @@ @article{lee2023comgan
   preview={equality_regularization.png},
   html={https://arxiv.org/abs/2304.12098},
   pdf={2304.12098.pdf}
-}
-
-@slides{lee2024intelligence,
-  title={Creating Artificial Intelligence from the World},
-  author={Lee, Haeone},
-  journal={personally owned},
-  year={2024},
-  preview={real_world.png},
-  html={https://drive.google.com/file/d/1UOC47IlDT7hhEW7Gd2N32Dag6NgVZ9gd/view?usp=sharing},
-  pdf={Creating_Artificial_Intelligence_from_the_World.pdf}
 }

_pages/about.md

@@ -7,7 +7,7 @@ subtitle: haeone.lee@kaist.ac.kr
 profile:
   align: right
   image: prof_pic.jpg
-  image_circular: true # crops the image to make it circular
+  image_circular: false # crops the image to make it circular
   more_info: >
     <p> 📍 Seoul, Korea </p>
     <p> </p>
@@ -28,4 +28,19 @@ I am a master student in KAIST Graduate school of AI, advised by Prof. [Kimin Le
 
 I am interested in developing a safe and proficient decision-making agent in real-world(e.g., robots). To this end, I aim to develop a method that can extract behavioral rules from existing data efficiently and help the agent continuously self-improve. Relevant topics include imitation learning on human data and developing scalable reinforcement learning (RL) algorithms that can work both on/offline. 
 
-Relevant keywords include imitation learning, hierarchical RL, explorations in RL, and robot learning.
+Relevant keywords include imitation learning, hierarchical RL, explorations in RL, and robot learning.
+
+---
+layout: page
+permalink: /publications/
+title: publications
+description: publications by categories in reversed chronological order. generated by jekyll-scholar.
+nav: true
+nav_order: 2
+---
+<!-- _pages/publications.md -->
+<div class="publications">
+
+{% bibliography -f {{ site.scholar.bibliography }} %}
+
+</div>