83 citations · 158 across the 4 of their papers we have counts for
4 papers
Federated Learning via Synthetic Data
Jack Goetz, Ambuj Tewari
Federated learning allows for the training of a model using data on multiple clients without the clients transmitting that raw data. However the standard method is to transmit mode…
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning
Jack Goetz, Ambuj Tewari
Active learning seeks to build the best possible model with a budget of labelled data by sequentially selecting the next point to label. However the training set is no longer \text…
Active Federated Learning
Jack Goetz, Kshitiz Malik, Duc Bui +3
Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, the…
Federated User Representation Learning
Duc Bui, Kshitiz Malik, Jack Goetz +4
Collaborative personalization, such as through learned user representations (embeddings), can improve the prediction accuracy of neural-network-based models significantly. We propo…