4 papers
Scaling In-Context Online Learning Capability of LLMs via Cross-Episode Meta-RL
Xiaofeng Lin, Sirou Zhu, Yilei Chen +6
Large language models (LLMs) achieve strong performance when all task-relevant information is available upfront, as in static prediction and instruction-following problems. However…
CCFC: Core & Core-Full-Core Dual-Track Defense for LLM Jailbreak Protection
Jiaming Hu, Haoyu Wang, Debarghya Mukherjee +1
Jailbreak attacks pose a serious challenge to the safe deployment of large language models (LLMs). We introduce CCFC (Core & Core-Full-Core), a dual-track, prompt-level defense fra…
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation
Jiaming Hu, Debarghya Mukherjee, Ioannis Ch. Paschalidis
In many real-world applications, ensuring the robustness and stability of deep neural networks (DNNs) is crucial, particularly for image classification tasks that encounter various…
Distributionally Robust Learning in Survival Analysis
Yeping Jin, Lauren Wise, Ioannis Ch. Paschalidis
We introduce an innovative approach that incorporates a Distributionally Robust Learning (DRL) approach into Cox regression to enhance the robustness and accuracy of survival predi…