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Conformal prediction is a popular framework of uncertainty quantification that constructs prediction sets with coverage guarantees. To uphold the exchangeability assumption, many conformal prediction methods necessitate an additional…

Machine Learning · Computer Science 2025-07-11 Hao Zeng , Kangdao Liu , Bingyi Jing , Hongxin Wei

We construct generally applicable short-time perturbative expansions for some fidelities, such as the input-output fidelity, the entanglement fidelity, and the average fidelity. Successive terms of these expansions yield characteristic…

Quantum Physics · Physics 2009-10-30 Lu-Ming Duan , Guang-Can Guo

While conformal predictors reap the benefits of rigorous statistical guarantees on their error frequency, the size of their corresponding prediction sets is critical to their practical utility. Unfortunately, there is currently a lack of…

Machine Learning · Statistics 2024-03-12 Guneet S. Dhillon , George Deligiannidis , Tom Rainforth

The QCD coupling, $\alpha_s$, is not a physical observable since it depends on conventions related to the renormalization procedure. Here we discuss a redefinition of the coupling where changes of scheme are parametrised by a single…

High Energy Physics - Phenomenology · Physics 2016-12-19 Diogo Boito , Matthias Jamin , Ramon Miravitllas

Conformal Prediction is a widely studied technique to construct prediction sets of future observations. Most conformal prediction methods focus on achieving the necessary coverage guarantees, but do not provide formal guarantees on the size…

Machine Learning · Computer Science 2025-02-25 Chao Gao , Liren Shan , Vaidehi Srinivas , Aravindan Vijayaraghavan

Conformal invariants of manifolds of non-positive scalar curvature are studied in association with growth in volume and fundamental group.

dg-ga · Mathematics 2008-02-03 M. C. Leung

Differential regularization is used to investigate the one-loop quantum corrections to Chern-Simons-Maxwell spinor and scalar electrodynamics. We illustrate the techniques to write the loop amplitudes in coordinate space. The short-distance…

High Energy Physics - Theory · Physics 2009-10-30 M. Chaichian , W. F. Chen , H. C. Lee

Order $\beta_0^n \alpha_s^{n+1}$ QCD corrections to top quark decays into $W^+$ and $H^+$ bosons are computed to all orders in perturbation theory. Predictions for the radiative corrections to the top quark width are compared with the…

High Energy Physics - Phenomenology · Physics 2009-10-30 Thomas Mehen

In previous work we have developed a relativistic quark model of mesons which is consistent with all QCD constraints at zeroth and first order in the heavy quark expansion. Here we obtain first order model predictions for the differential…

High Energy Physics - Phenomenology · Physics 2009-10-22 B. Holdom , M. Sutherland

A criterion for comonadicity of the extension-of- scalars functor associated to an extension of (not necessarily commutative) rings is given. As an application of this criterion, some known results on the comonadicity of such functors are…

Quantum Algebra · Mathematics 2007-05-23 Bachuki Mesablishvili

Unitarity corrections to the BFKL evolution at next to leading order determine a new component of the evolution kernel which is shown to possess conformal invariance properties. Expressions for the complete spectrum of the new component and…

High Energy Physics - Phenomenology · Physics 2007-05-23 Claudio Coriano' , Alan R. White

In this paper we show that the apparent failure of QCD lattice perturbation theory to account for Monte Carlo measurements of perturbative quantities results from choosing the bare lattice coupling constant as the expansion parameter. Using…

High Energy Physics - Lattice · Physics 2009-10-22 G. P. Lepage , P. B. Mackenzie

The purpose of this note is to discuss some aspects of recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in ``Design of Fractional-order Variants of Complex…

Optimization and Control · Mathematics 2020-07-28 Shujaat Khan , Abdul Wahab , Imran Naseem , Muhammad Moinuddin

Conformal prediction, a post-hoc, distribution-free, finite-sample method of uncertainty quantification that offers formal coverage guarantees under the assumption of data exchangeability. Unfortunately, the resulting uncertainty regions…

Machine Learning · Computer Science 2026-04-21 Nikolaos Bousias , Lars Lindemann , George Pappas

Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction is a user-friendly paradigm for…

Machine Learning · Computer Science 2022-12-08 Anastasios N. Angelopoulos , Stephen Bates

Conformal prediction (CP) provides a framework for constructing prediction sets with guaranteed coverage, assuming exchangeable data. However, real-world scenarios often involve distribution shifts that violate exchangeability, leading to…

Machine Learning · Computer Science 2025-05-27 Shadi Alijani , Homayoun Najjaran

In high-energy particle physics, extracting parton distribution functions (PDFs) from lattice quantum chromodynamics (QCD) calculations remains a significant challenge, particularly due to the divergent nature of perturbative expansions at…

High Energy Physics - Phenomenology · Physics 2024-11-26 Jia-Lu Zhang

We show how to refine conformal block expansion convergence estimates from hep-th/1208.6449. In doing so we find a novel explicit formula for the 3d conformal blocks on the real axis.

High Energy Physics - Theory · Physics 2016-02-17 Slava Rychkov , Pierre Yvernay

We examine the effect of quantum longitudinal rescaling of coordinates, on the action of quantum chromodynamics (with quarks) to one loop. We use an aspherical Wilsonian integration (previously applied to the pure Yang-Mills theory and to…

High Energy Physics - Theory · Physics 2012-03-30 Axel Cortés Cubero , Peter Orland

This paper introduces a framework for uncertainty quantification in regression models defined in metric spaces. Leveraging a newly defined notion of homoscedasticity, we develop a conformal prediction algorithm that offers finite-sample…

Machine Learning · Statistics 2025-07-22 Gábor Lugosi , Marcos Matabuena