Theoretical and empirical studies [21,77,9,55,49,5,12]postulate that such flatter regions generalize better than sharper minima, e.g., due to the flat minimizer's robustness against loss function shifts between trainandtestdata,asillustrated inFig.1.

Kernel methods augmented with random features give scalable algorithms for learning from big data.

Human-AI collaboration looks at harnessing the complementary strengths of both humans and AI.

We call this family of functions intra order-preserving functions. We propose a new neural network architecture that represents a class of intra order-preserving functions by combining common neural network components.

Theoptimalbaseline, however, israrelyusedinpractice (Sutton & Barto (2018); foran exception, see (Peters & Schaal, 2008)). Equation (1) thentakesthefollowingform: r E R(x)= E (R(x) B)r log (x).