A bstract We apply the numerical conformal bootstrap to correlators of Coulomb and Higgs branch operators in 4 d $$ \mathcal{N} N = 2 superconformal theories. start by revisiting previous results on single operators. In particular, we present improved bounds OPE coefficients for some selected Argyres-Douglas ...

The monolithic approach to policy representation in Markov Decision Processes (MDPs) looks for a single policy that can be represented as a function from states to actions. For the monolithic approach to succeed (and this is not always possible), a complex feature representation is often necessary since the policy is a complex object that has to prescribe what actions to take all over the state...

We use Monte Carlo methods to study the properties of the bootstrap Breusch-Godfrey test for autocorrelated errors in two versions a) by bootstrapping under the null hypothesis, restricted and b) by bootstrapping under the alternative hypothesis, unrestricted. We use the residual bootstrap for the bootstrap-BG test. Our analysis regarding the size of the test reveals that both bootstrap tests h...

As the sizes of high-end computing systems continue to grow to massive scales, efficient boostrapping for distributed software infrastructures is becoming a greater challenge. Distributed infrastructure bootstrapping is the procedure of instantiating all processes of the distributed system on the appropriate hardware nodes and disseminating to these processes the information that they need to c...

We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier ...

We present a practical co-training method for bootstrapping statistical parsers using a small amount of manually parsed training material and a much larger pool of raw sentences. Experimental results show that unlabelled sentences can be used to improve the performance of statistical parsers. In addition, we consider the problem of bootstrapping parsers when the manually parsed training materia...