Search Results for author:
Found 4 papers, 0 papers with code
Optimizing Dynamic Structures with Bayesian Generative Search
Kernel selection for kernel-based methods is prohibitively expensive due to the NP-hard nature of discrete optimization.
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.
Personalized Neural Architecture Search for Federated Learning
Federated Learning (FL) is a recently proposed learning paradigm for decentralized devices to collaboratively train a predictive model without exchanging private data.
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
