Optimization
Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learning Yiqin Lv Qi Wang
Accordingly, extensive evaluations demonstrate the significance of our proposal and its scalability to multimodal large models in boosting robustness.
Policy Gradient for Rectangular Robust Markov Decision Processes Anonymous Author(s) Affiliation Address email
We provide a closed-form expression for the worst occupation measure.
Policy Gradient for Rectangular Robust Markov Decision Processes
However, they do not account for transition uncertainty, whereas learning robust policies can be computationally expensive.
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
Fine-tuning pre-trained models is a popular approach in machine learning for solving complex tasks with moderate data.
Counterfactual Evaluation of Peer-Review Assignment Policies
As a result, these and other large conferences must rely on automated systems to decide what members of the impaneled reviewer pool will review each paper.
