Our algorithm is based on an abstract (and simple) reduction to online convex optimization, which efficiently converts an arbitrary online convex optimizer to a boosting algorithm. Moreover, this reduction extends to the statistical as well astheonlinerealizablesettings, thusunifying the4casesofstatistical/online and agnostic/realizableboosting.

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design.

Usually, this is done by projecting the score vector onto a probability simplex, and such projections are often characterized as Lipschitz continuous approximations of the argmax function, whose Lipschitz constant is controlled by a parameter that is similar to a softmax temperature.

Information-theoretic analysis ofdeep neural networks (DNN) has attracted recent interest due to intriguing fundamental results and new hypotheses. Applying information theory to DNNs may provide novel tools for explainable AI via estimation of information flows [1-5], as well as new ways to encourage models to extract and generalize information [1, 6-8].