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Yongsik Lee

3 accepted papers

2025

MA$^2$E: Addressing Partial Observability in Multi-Agent Reinforcement Learning with Masked Auto-Encoder

ICLR 2025poster

Centralized Training and Decentralized Execution (CTDE) is a widely adopted paradigm to solve cooperative multi-agent reinforcement learning (MARL) problems. Despite the successes achieved with CTDE, partial observability still limits cooperation among agents. While previous studies have attempted t…

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2024

Preference Alignment with Flow Matching

NeurIPS 2024poster

We present Preference Flow Matching (PFM), a new framework for preference alignment that streamlines the integration of preferences into an arbitrary class of pre-trained models. Existing alignment methods require fine-tuning pre-trained models, which presents challenges such as scalability, ineffic…

2023

Restoration of Hand-Drawn Architectural Drawings Using Latent Space Mapping With Degradation Generator

CVPR 2023poster

This work presents the restoration of drawings of wooden built heritage. Hand-drawn drawings contain the most important original information but are often severely degraded over time. A novel restoration method based on the vector quantized variational autoencoders is presented. Latent space represe…

Cited by 5Source ↗PDF ↗Scholar ↗