12 citations · 36 across the 24 of their papers we have counts for
5 papers · 1 filter
Learning to Discover Skills through Guidance
Hyunseung Kim, Byungkun Lee, Hojoon Lee +4
In the field of unsupervised skill discovery (USD), a major challenge is limited exploration, primarily due to substantial penalties when skills deviate from their initial trajecto…
Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems
Hojoon Lee, Dongyoon Hwang, Kyushik Min +1
Interactive Recommender Systems (IRSs) have attracted a lot of attention, due to their ability to model interactive processes between users and recommender systems. Numerous approa…
ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal
Hojoon Lee, Hawon Jeong, Byungkun Lee +2
In this paper, we introduce ST-RAP, a novel Spatio-Temporal framework for Real estate APpraisal. ST-RAP employs a hierarchical architecture with a heterogeneous graph neural networ…
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning
Hojoon Lee, Koanho Lee, Dongyoon Hwang +3
Recently, unsupervised representation learning (URL) has improved the sample efficiency of Reinforcement Learning (RL) by pretraining a model from a large unlabeled dataset. The un…
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
Hojoon Lee, Hanseul Cho, Hyunseung Kim +5
In Reinforcement Learning (RL), enhancing sample efficiency is crucial, particularly in scenarios when data acquisition is costly and risky. In principle, off-policy RL algorithms…