5 papers · 1 filter
Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
Chaiwon Kim, Jongyeong Lee, Min-hwan Oh
We study the decoupled multi-armed bandit problem, where the learner separately selects one arm for exploration and one, possibly different, arm for exploitation at each round. In…
Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
Jongyeong Lee, Junya Honda, Shinji Ito +1
Follow-the-Regularized-Leader (FTRL) policies have achieved Best-of-Both-Worlds (BOBW) results in various settings through hybrid regularizers, whereas analogous results for Follow…
AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift
Eunsu Baek, Keondo Park, Jeonggil Ko +3
Current AI advances largely rely on scaling neural models and expanding training datasets to achieve generalization and robustness. Despite notable successes, this paradigm incurs…
Symmetry-Aware GFlowNets
Hohyun Kim, Seunggeun Lee, Min-hwan Oh
Generative Flow Networks (GFlowNets) offer a powerful framework for sampling graphs in proportion to their rewards. However, existing approaches suffer from systematic biases due t…
Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning
Hyungkyu Kang, Min-hwan Oh
In this paper, we study offline preference-based reinforcement learning (PbRL), where learning is based on pre-collected preference feedback over pairs of trajectories. While offli…