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

An instance of natural occurrences of such constraints is when mixed strategies are used, which correspond to a probability simplex constraint.

Notef = andg = are functions. Theorem 4.4 withrespecttok k1-norm; see Example A.3 in Section A.2.1.

Building on this result, we show several examples of how variance of predictions can be exploited in learning.

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

Since the provider's goal of maximizing revenue can only be achieved

Arecent lineofresearch hasshownthatgradient-based algorithms withrandom initialization can converge to the global minima of the training loss for overparameterized (i.e.,sufficiently wide)deepneuralnetworks. However,thecondition onthewidth oftheneural networktoensure theglobal convergence isvery stringent, which is often a high-degree polynomial in the training sample size n (e.g., O(n24)).