Our contributions are: (1) We develop a generalized framework to optimize non-differentiable ranking-based functions byextending theerror-drivenoptimization ofAPLoss.(2)Weprovethat

We study the task of learning SIMs in the agnostic (a.k.a.

In this work, we introduce a machine learning based method to search for adynamic proof within these proof systems.

Recently, such difficulty in multi-agent learning has been eased owing to the introduction ofcentralized training for decentralized execution(CTDE) [11, 45], which allows agents to access the global information andopponents' actions during thetraining phase.

The generated candidates are novel at both the molecular and chemical scaffold levels when compared to the training data.