4 papers
Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training
Bongsoo Yi, Rongjie Lai, Yao Li
Adversarial training has proven effective in improving the robustness of deep neural networks against adversarial attacks. However, this enhanced robustness often comes at the cost…
Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions
Yue Kang, Mingshuo Liu, Bongsoo Yi +4
Generalized linear bandits have been extensively studied due to their broad applicability in real-world online decision-making problems. However, these methods typically assume tha…
Quantum Lipschitz Bandits
Bongsoo Yi, Yue Kang, Yao Li
The Lipschitz bandit is a key variant of stochastic bandit problems where the expected reward function satisfies a Lipschitz condition with respect to an arm metric space. With its…
Biased Dueling Bandits with Stochastic Delayed Feedback
Bongsoo Yi, Yue Kang, Yao Li
The dueling bandit problem, an essential variation of the traditional multi-armed bandit problem, has become significantly prominent recently due to its broad applications in onlin…