Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization (DiBO)
Published in ICML (Poster), 2025
Posts by Collection
publications
Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization (CiBO)
Published in NeurIPS SPIGM Workshop (Oral), 2025
Diffusion Alignment as Variational Expectation-Maximization
Published in ICLR (Poster), 2026
Diffusion Fine-Tuning via Reparameterized Policy Gradient of the Soft Q-Function
Published in ICLR (Poster), 2026
Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation
Published in arXiv preprint, 2026
talks
Control as Probabilistic Inference
Published:
In this seminar hosted by KAIST’s System Intelligence Lab (SILAB), the talk focused on interpreting control problems through the lens of probabilistic inference.
Geometry-aware Posterior Inference for High dimensional black-box optimization
Published:
In this seminar hosted by KAIST’s System Intelligence Lab (SILAB), the talk focused on generative model based optimization on Riemannian Manifold.
teaching
Deep Learning Programming Study
Summer Lecture, Korea University, 2023
- Served as a teaching assistant for a summer school led by Professor Sungbin Lim.
- Covered PyTorch-based deep learning programming, including neural network implementation, automatic differentiation, and training workflows.
Teaching Assistant on IE343 Statistical Machine Learning
Undergraduate course, KAIST, Industrial & Systems Engineering, 2024
- Served as a teaching assistant for IE343 Statistical Machine Learning at KAIST.
- Covered core topics in statistical machine learning, including supervised learning, regression, classification, and model evaluation.