Reward allocation, also known as the credit assignment problem, has been an important topic ineconomics, engineering, andmachine learning.

We demonstrate the practical merits and limitations of existing theoretical tests and their bootstrapped variants.

Despite the numerous generalization upper bounds that have been proved over the last two decades, nothing is known about the tightness of these bounds.

In the current paper we relax the information structure of these classical frameworks by allowing arbitrary information arrivalprocesses.

Findthesmallest0 (n, k, ), where (n, k, )= c1 r logn+ log ( 1/ ) k . Then, withprobabilityatleast1 , theresultingclassifiergn satisfiesthefollowing: foreverypointx 2 supp(µ), if n C adv (x) max log 1 adv (x) , log 1 thengn(x)= g (x).

We show the universality of depth-2 group convolutional neural networks (GCNNs) inaunified and constructivemanner based ontheridgelet theory.

Let (t)= 1cm ecM 1 . Thenthe BreD induced isdefinedD ( 2, 1)= ( 2) ( 1) hr ( 1), 2 1i.

Whenrewardsare fully observed, we show that the expected reward objectiveexhibits suboptimal plateaus and exponentially many local optima in the worst case.

In this paper we consider linearly constrained stochastic approximation problems with federated learning asaspecial case.

However, estimating and optimizingα-divergences require to use importance sampling, which may havelarge orinfinite variance due to heavy tails ofimportance weights.