Synthesis of MCMC and Belief Propagation

no code implementations • NeurIPS 2016 • Sung-Soo Ahn, Michael Chertkov, Jinwoo Shin

In this paper, we introduce MCMC algorithms correcting the approximation error of BP, i. e., we provide a way to compensate for BP errors via a consecutive BP-aware MCMC.

QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning

no code implementations • 22 Jun 2020 • Kyunghwan Son, Sung-Soo Ahn, Roben Delos Reyes, Jinwoo Shin, Yung Yi

QTRAN is a multi-agent reinforcement learning (MARL) algorithm capable of learning the largest class of joint-action value functions up to date.

reinforcement-learning Reinforcement Learning (RL) +2

Learning from Failure: De-biasing Classifier from Biased Classifier

no code implementations • NeurIPS 2020 • Junhyun Nam, Hyuntak Cha, Sung-Soo Ahn, Jaeho Lee, Jinwoo Shin

Neural networks often learn to make predictions that overly rely on spurious corre- lation existing in the dataset, which causes the model to be biased.