Converting approximate error bounds into exact ones

Converting Approximate Error Bounds into Exact Ones

In order to produce error bounds quickly and easily, people often apply to error bounds linearized propagation rules. This is done instead of a precise error analysis. The payoff: Estimates so produced are not guaranteed to be true bounds. One can at most hope that they are good approximations of true bounds. This paper discusses a way to convert such approximate error bounds into true bounds. ...

Error Bounds for Approximate Value Iteration

Approximate Value Iteration (AVI) is an method for solving a Markov Decision Problem by making successive calls to a supervised learning (SL) algorithm. Sequence of value representations Vn are processed iteratively by Vn+1 = AT Vn where T is the Bellman operator and A an approximation operator. Bounds on the error between the performance of the policies induced by the algorithm and the optimal...

Error Bounds for Approximate Policy Iteration

In Dynamic Programming, convergence of algorithms such as Value Iteration or Policy Iteration results -in discounted problemsfrom a contraction property of the back-up operator, guaranteeing convergence to its fixedpoint. When approximation is considered, known results in Approximate Policy Iteration provide bounds on the closeness to optimality of the approximate value function obtained by suc...

Error Bounds for Approximate Poli y Iteration

Approximate NN queries on Streams with Guaranteed Error/performance Bounds

In data stream applications, data arrive continuously and can only be scanned once as the query processor has very limited memory (relative to the size of the stream) to work with. Hence, queries on data streams do not have access to the entire data set and query answers are typically approximate. While there have been many studies on the k Nearest Neighbors (kNN) problem in conventional multid...