7 accepted papers
Large language models fine-tuned on instruction–code pairs may memorize and subsequently leak sensitive training data. Existing differentially private (DP) code generation methods primarily protect code snippets while assuming prompts are public, which fails in realistic scenarios where prompts may …
As Diffusion Models (DM) generate increasingly realistic images, related issues such as copyright and misuse have become a growing concern. Watermarking is one of the promising solutions. Existing methods inject the watermark into the *single-domain* of initial Gaussian noise for generation, which s…
Ensemble deep reinforcement learning (DRL) is a popular approach to mitigate the risk of overspecialization to a particular distribution and improve performance. However, it is commonly plagued by high computational resource requirements that arise from the introduction of multiple value and policy…
Deep reinforcement learning (DRL) gives the promise that an agent learns good policy from high-dimensional information, whereas representation learning removes irrelevant and redundant information and retains pertinent information. In this work, we demonstrate that the learned representation of the…
The combination of deep reinforcement learning (DRL) with ensemble methods has been proved to be highly effective in addressing complex sequential decision-making problems. This success can be primarily attributed to the utilization of multiple models, which enhances both the robustness of the polic…
Targeting for detecting anomalies of various sizes for complicated normal patterns, we propose a Template-guided Hierarchical Feature Restoration method, which introduces two key techniques, bottleneck compression and template-guided compensation, for anomaly-free feature restoration. Specially, our…
Offline reinforcement learning (RL) aims to optimize policy from large pre-recorded datasets without interaction with the environment. This setting offers the promise of utilizing diverse and static datasets to obtain policies without costly, risky, active exploration. However, commonly used off-pol…