3 papers
cs.LG2026
FestDPO: Few-step Generator Alignment with Direct Preference Optimization
Jaewoo Lee, Kyuil Sim, Hyeongyu Kang +3
Few-step generative models can generate high-fidelity samples within a few function evaluations. Despite this efficiency, generated samples may not exhibit desirable properties. Wh…
cs.RO2023
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Kanghoon Lee, Jiachen Li, David Isele +3
Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior p…
cs.LG2018
Stochastic Doubly Robust Gradient
Kanghoon Lee, Jihye Choi, Moonsu Cha +2
When training a machine learning model with observational data, it is often encountered that some values are systemically missing. Learning from the incomplete data in which the mi…