12 citations · 24 across the 14 of their papers we have counts for
4 papers · 1 filter
SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments
Romain Deffayet, Thibaut Thonet, Dongyoon Hwang +3
Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in…
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…
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…