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Found 6 papers, 2 papers with code
SABAL: Sparse Approximation-based Batch Active Learning
We propose a novel and general framework (i. e., SABAL) that formulates batch active learning as a sparse approximation problem.
Scalable Robust Federated Learning with Provable Security Guarantees
Federated averaging, the most popular aggregation approach in federated learning, is known to be vulnerable to failures and adversarial updates from clients that wish to disrupt training.
Does Adversarial Transferability Indicate Knowledge Transferability?
Despite the immense success that deep neural networks (DNNs) have achieved, \emph{adversarial examples}, which are perturbed inputs that aim to mislead DNNs to make mistakes, have recently led to great concerns.
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference.
Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability
Knowledge transferability, or transfer learning, has been widely adopted to allow a pre-trained model in the source domain to be effectively adapted to downstream tasks in the target domain.
Learning Sparse Distributions using Iterative Hard Thresholding
Iterative hard thresholding (IHT) is a projected gradient descent algorithm, known to achieve state of the art performance for a wide range of structured estimation problems, such as sparse inference.
