30 citations · 75 across the 8 of their papers we have counts for
8 papers
SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
Jaehyung Kim, Jaehyun Nam, Sangwoo Mo +5
Large language models (LLMs) have made significant advancements in various natural language processing tasks, including question answering (QA) tasks. While incorporating new infor…
Preference Transformer: Modeling Human Preferences using Transformers for RL
Changyeon Kim, Jongjin Park, Jinwoo Shin +3
Preference-based reinforcement learning (RL) provides a framework to train agents using human preferences between two behaviors. However, preference-based RL has been challenging t…
Meta-Learning with Self-Improving Momentum Target
Jihoon Tack, Jongjin Park, Hankook Lee +2
The idea of using a separately trained target model (or teacher) to improve the performance of the student model has been increasingly popular in various machine learning domains,…
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
Jongjin Park, Younggyo Seo, Jinwoo Shin +3
Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the rewar…
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning
Jongjin Park, Younggyo Seo, Chang Liu +4
Behavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations. However, behavioral cloning often suffers from the causal…
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
Jongjin Park, Sukmin Yun, Jongheon Jeong +1
Semi-supervised learning (SSL) has been a powerful strategy to incorporate few labels in learning better representations. In this paper, we focus on a practical scenario that one a…