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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…

Cited by 0SourcePDFScholar
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 5SourcePDFScholar