I am a PhD student at System Intelligence lab in KAIST. I am fortunate to be advised by Professor Jinkyoo Park. Here is my cv.

My research interest lies in solving complex and high-dimensional black-box optimization problems through the lens of conditional generative modeling. I’m currently interested in Diffusion Models, Generative Flow Networks (GFlowNets), and their applications to real-world tasks, e.g, biological sequence design, material discovery, and mechanical design. I’m also interested in various decision making problems such as bandits, Reinforcement Learning and Multi-Agent RL.

Recently, I found out that many crucial problems in ML can be reduced as a posterior inference problem. To this end, I’m currently interested in developing algorithms for amortizing intractable multi-modal posterior inference that can impact real-world applications.

🔥 News

• 2025.02:  🎉🎉 2 papers accepted in 2025 (1 ICLR, 1 CVPR)
• 2024.12:  🎉🎉 5 papers accepted in 2024 (1 ICLR, 1 ICML, 1 KDD, 2 NeurIPS)
• 2024.09:  🎉🎉 I started remote internship at HKUST, Hosted by Ling Pan

📖 Educations

• 2024.03 - current, Ph.D in Industrial and Systems Engineering, KAIST (Korea Advanced Institute of Science and Technology)
• 2022.09 - 2024.02, M.S in Graduate School of AI, KAIST
• 2018.03 - 2022.08, B.S in Industrial and Systems Engineering & Computer Science, KAIST

💻 Internships

• 2024.09 - current, Remote Internship at HKUST (Hong Kong University of Science and Technology), hosted by Ling Pan
• 2021.03 - 2021.08, Internship at Kakao Recommendation Team

📝 Publications

(*: Equal Contribution)

• Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
  [paper], [code]
  Taeyoung Yun*, Kiyoung Om*, Jaewoo Lee, Sujin Yun, and Jinkyoo Park
  Arxiv 2025

• Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation
  [paper], [code]
  Taeyoung Yun, Dinghuai Zhang, Jinkyoo Park, and Ling Pan
  CVPR 2025

• Adaptive Teachers for Amortized Samplers
  [paper], [code]
  Minsu Kim*, Sanghyeok Choi*, Taeyoung Yun, Emmanuel Bengio, Leo Feng, Jarrid Rector-Brooks, Sungsoo Ahn, Jinkyoo Park, Nikolay Malkin, and Yoshua Bengio
  ICLR 2025

• Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization
  [paper], [code]
  Taeyoung Yun, Sujin Yun, Jaewoo Lee, and Jinkyoo Park
  NeurIPS 2024

• GTA: Generative Trajetory Augmentation with Guidance for Offline Reinforcement Learning
  [paper], [code]
  Jaewoo Lee*, Sujin Yun*, Taeyoung Yun, and Jinkyoo Park
  NeurIPS 2024 (based on ICLR Workshop)

• An Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management Systems
  [paper], [code]
  Taeyoung Yun*, Kanghoon Lee*, Sujin Yun, Ilmyung Kim, Won-Woo Jung, Min-Cheol Kwon, Kyujin Choi, Yoohyeon Lee, and Jinkyoo Park
  KDD 2024

• Learning to Scale Logits for Temperature-conditional GFlowNets
  [paper], [code]
  Minsu Kim*, Juhwan Ko*, Taeyoung Yun*, Dinghuai Zhang, Ling Pan, Woochang Kim, Jinkyoo Park, and Yoshua Bengio
  ICML 2024 (based on NIPS Workshop)

• Local Search GFlowNets
  [paper], [code]
  Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, and Jinkyoo Park
  ICLR (Spotlight) 2024

🎖 Honors and Awards

• 2021.12 Excellence Award on DACON NH Big Data Competition.
• 2021.09 Dean’s List (Honor for Top 2% Students).