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cs.LG2026
TabQL: In-Context Q-Learning with Tabular Foundation Models
Qisai Liu, Zhanhong Jiang, Timilehin Ayanlade +4
We propose Tabular Q-Learning (TabQL), a reinforcement learning framework that replaces the conventional parametric Q-network in Deep Q-Learning (DQN) with a tabular foundation mod…
cs.LG2023
Vulnerability-Aware Instance Reweighting For Adversarial Training
Olukorede Fakorede, Ashutosh Kumar Nirala, Modeste Atsague +1
Adversarial Training (AT) has been found to substantially improve the robustness of deep learning classifiers against adversarial attacks. AT involves obtaining robustness by inclu…