WederivePAC-Bayesian learning guarantees forheavy-tailed losses, andobtain a novel optimal Gibbs posterior which enjoys finite-sample excess risk bounds atlogarithmic confidence. Ourcoretechnique itselfmakesuseofPAC-Bayesian inequalities in order to derive a robust risk estimator, which by design is easy to compute.

Y u-Shen Liu is the corresponding author.

Neural Information Processing Systems http://nips.cc/

Research in artificial intelligence, and particularly deep reinforcement learning (RL), relies on evaluating aggregate performance on a diverse suite of tasks to assess progress.

Anillustrativeexample inFigure 1(a)showcases thatthecorrelations analysis among amino acids (AAs) sites could reveal contacts or conservative regions in the folding structure.