We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised tasks rooted in mathematical formalism theory, to distance metric corporate bond markets. Compared equities, bonds are relatively illiquid trade quote data these securities sparse. Thus, measure distance/similarity among is particularly useful variety practical applications trading bonds, including identification similar tradable alternatives, pricing with few...

Due to the dynamic nature of financial markets, maintaining models that produce precise predictions over time is difficult. Often goal isn't just point prediction but determining uncertainty. Quantifying uncertainty, especially aleatoric uncertainty due unpredictable market drivers, helps investors understand varying risk levels. Recently, quantile regression forests (QRF) have emerged as a promising solution: Unlike most basic methods need separate for each quantile, estimate entire...

Reduction between theories in physics is often approached as an a priori relation the sense that reduction taken to depend only on comparison of mathematical structures two theories. I argue such purely formal approaches fail capture one crucial 'reduction', whereby theory encompasses set real behaviours are well modelled by other. this depends not but also empirical facts about where succeed at describing systems, and therefore posteriori relation. discuss several detailed implications...

Supporters of the de Broglie-Bohm (dBB) interpretation quantum theory argue that because theory, like classical mechanics, concerns motions point particles in 3D space, it is specially suited to recover behavior. I offer a novel account classicality dBB if only show such an falls out almost trivially from results developed largely interpretation-neutral context decoherence theory. then this undermines any special claim purported have on unification and realms.

We initiate a novel approach to explain the out of sample performance random forest (RF) models by exploiting fact that any RF can be formulated as an adaptive weighted K nearest-neighbors model. Specifically, we use proximity between points in feature space learned re-write predictions exactly average target labels training data points. This linearity facilitates local notion explainability generates attributions for model prediction across observations set, and thereby complements...

Due to the dynamic nature of financial markets, maintaining models that produce precise predictions over time is difficult. Often goal isn't just point prediction but determining uncertainty. Quantifying uncertainty, especially aleatoric uncertainty due unpredictable market drivers, helps investors understand varying risk levels. Recently, quantile regression forests (QRF) have emerged as a promising solution: Unlike most basic methods need separate for each quantile, estimate entire...

We initiate a novel approach to explain the predictions and out of sample performance random forest (RF) regression classification models by exploiting fact that any RF can be mathematically formulated as an adaptive weighted K nearest-neighbors model. Specifically, we employ recent result that, for both tasks, prediction rewritten exactly sum training targets, where weights are proximities between corresponding pairs data points. show this linearity facilitates local notion explainability...

The relationship that is widely presumed to hold between physical theories and their successors, in which the successors some sense explain success of they replace, known commonly as 'reduction.' I argue one traditional approach theory reduction physics, founded on notion a superseded should simply be mathematical limit supersedes it, misleading general picture whereby encompasses domain empirical validity another. defend an alternative account builds upon certain type dynamical systems...

Abstract The earliest formulation of the Higgs naturalness argument has been criticized on grounds that it relies a particular cutoff-based regularization scheme. One response to this criticism circumvent worry by reformulating in terms renormalized, regulator-independent parametrization. An alternative is deny regulator dependence poses problem for argument, because nature itself furnishes particular, physically correct any effective field theory (EFT) form EFT’s physical cutoff, together...

I distinguish two types of reduction within the context quantum-classical relations, which designate "formal" and "empirical". Formal holds or fails to hold solely by virtue mathematical relationship between theories; it is therefore a two-place, priori relation theories. Empirical requires one theory encompass range physical behaviors that are well-modeled in another theory; certain sense, three-place, posteriori connecting theories domain reality both serve describe. Focusing on classical...

I show explicitly how concerns about wave function collapse and ontology can be decoupled from the bulk of technical analysis necessary to recover localized, approximately Newtonian trajectories quantum theory. In doing so, demonstrate that account classical behavior provided by decoherence theory straightforwardly tailored give accounts on multiple interpretations theory, including Everett, de Broglie-Bohm GRW interpretations. further this interpretation-neutral, decoherence-based conforms...

The Higgs naturalness principle served as the basis for so far failed prediction that signatures of physics beyond Standard Model (SM) would be discovered at LHC. One influential formulation principle, which prohibits fine tuning bare parameters, rests on assumption a particular set values these parameters constitute "fundamental parameters" theory, and serve to mathematically define theory. On other hand, an old argument by Wetterich suggests merely reflects arbitrary, inconvenient choice...