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On the Optimal Regret of Locally Private Linear Contextual Bandit
Contextual bandit with linear reward functions is among one of the most extensively studied models in bandit and online learning research.
Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning
Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT).
Privacy Preserving Adaptive Experiment Design
In this paper, we investigate the tradeoff between loss of social welfare and statistical power in contextual bandit experiment.
Fight Fire with Fire: Combating Adversarial Patch Attacks using Pattern-randomized Defensive Patches
Existing defense methods often necessitate modifications to the target model or result in unacceptable time overhead.
We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature
Recently, the object detection based on deep learning has proven to be vulnerable to adversarial patch attacks.
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