54 citations · 107 across the 5 of their papers we have counts for
5 papers
Direct Preference-based Policy Optimization without Reward Modeling
Gaon An, Junhyeok Lee, Xingdong Zuo +3
Preference-based reinforcement learning (PbRL) is an approach that enables RL agents to learn from preference, which is particularly useful when formulating a reward function is ch…
Optimal channel selection with discrete QCQP
Yeonwoo Jeong, Deokjae Lee, Gaon An +2
Reducing the high computational cost of large convolutional neural networks is crucial when deploying the networks to resource-constrained environments. We first show the greedy ap…
Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks
Seungyong Moon, Gaon An, Hyun Oh Song
Deep neural networks have become the driving force of modern image recognition systems. However, the vulnerability of neural networks against adversarial attacks poses a serious th…
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
Gaon An, Seungyong Moon, Jang-Hyun Kim +1
Offline reinforcement learning (offline RL), which aims to find an optimal policy from a previously collected static dataset, bears algorithmic difficulties due to function approxi…
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
Seungyong Moon, Gaon An, Hyun Oh Song
Solving for adversarial examples with projected gradient descent has been demonstrated to be highly effective in fooling the neural network based classifiers. However, in the black…