Source Themes

Detecting when to retrain a model based on drift.

Assessing data quality metrics for Machine Learning.

Using Reinforcement Learning based techniques to enable clients in Federated Learning derive updates only from relevant data.

Federated Learning for spatio-temporal datasets.

Training dataset generation curtailed to user metrics.

Selecting the appropriate task specific data sources in Federated Learning.

Detecting outlier samples in Federated Learning.

Using Cooperative Game theory approaches to mitigate the impace of noisy clients in Federated Learning.

Shapley values based approach to detect slices of data that needs to be analyzed to debug the performance of a black-box ML model.

Auto encoder inspired deep architectures for finding outlier robust network embedding for nodes in an attributed networks through adversarial training.