Our findings reveal that even small collectives (controlling less than 0.01% of the training data) can achieve up to

Discriminative methods were used to execute such tasks and achieved state-of-the-art performance.

In safety-critical applications, machine learning models should generalize well under worst-case distribution shifts, that is, have a small robust risk.

Motivated by the above findings, we propose FedCCFA, a federated learning framework with classifier clustering and feature alignment.

Often, however, the causal relationship may be entirely absent or account only for a part of the initially observed correlation.

In this paper we tackle the problem of recovering the phase of complex linear measurements whenonlymagnitude information isavailableandwecontrol the input. We are motivated by the recent development of dedicated optics-based hardware for rapid random projections which leverages the propagation of light inrandom media.