We study the problem of experiment planning with function approximation in contextual bandit problems.

Statistical, 36(2):111-147, R. Sukkar hidden 2012 Medicine, pages Z. Sun, progression data.

Several popularly used FL algorithms for this setting includeFedAvg (McMahan et al., 2017), FedProx(Lietal.,2020b), We analyze its convergence behavior, expose problems, andpropose alternativesmore suitable forscaling upandgeneralization.

Adversarial robustness is an increasingly critical property of classifiers in applications. The design of robust algorithms relies on surrogate losses since the optimization of the adversarial loss with most hypothesis sets is NP-hard.

The whole is greater than its parts and the parts constitute the whole.

The sliding window model of computation captures scenarios in which data is arriving continuously,butonly thelatestwelements should beused foranalysis.