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Related papers: Occam's razor meets WMAP

200 papers

Invariant Causal Prediction (Peters et al., 2016) is a technique for out-of-distribution generalization which assumes that some aspects of the data distribution vary across the training set but that the underlying causal mechanisms remain…

Machine Learning · Computer Science 2021-03-30 Elan Rosenfeld , Pradeep Ravikumar , Andrej Risteski

One of the most tantalizing results from the WMAP experiment is the suggestion that the power at large scales is anomalously low when compared to the prediction of the ``standard'' Lambda-CDM model. The same anomaly, although with somewhat…

Astrophysics · Physics 2009-11-10 Anastasia Niarchou , Andrew H. Jaffe , Levon Pogosian

Recent Wilkinson Microwave Anisotropy Probe (WMAP) data confirm the Cosmic Microwave Background (CMB) quadrupole anomaly. We further elaborate our previous proposal that the quadrupole power can be naturally suppressed in axis-symmetric…

Astrophysics · Physics 2008-11-26 L. Campanelli , P. Cea , L. Tedesco

We consider the problem of off-policy evaluation for reinforcement learning, where the goal is to estimate the expected reward of a target policy $\pi$ using offline data collected by running a logging policy $\mu$. Standard…

Machine Learning · Computer Science 2020-07-09 Ming Yin , Yu-Xiang Wang

Given an Orthogonal Array we analyze the aberrations of the sub-fractions which are obtained by the deletion of some of its points. We provide formulae to compute the Generalized Word-Length Pattern of any sub-fraction. In the case of the…

Statistics Theory · Mathematics 2018-09-12 Roberto Fontana , Fabio Rapallo

The Cosmic Microwave Background (CMB) reaches homogeneity at relatively modest angular scales compared to the expectation of the standard $\Lambda$CDM model revealing an important challenge to the theoretical predictions. We analyze this…

The first year results of WMAP tentatively indicate running of the spectral index as well as a deficit of power in the low multipoles in the CMB spectrum. The former can be rather easily understood in the noncommutative inflation model, and…

Astrophysics · Physics 2009-11-10 Qing-Guo Huang , Miao Li

Though debated, the existence of claimed large-scale anomalies in the CMB is not totally dismissed. In parallel to the debate over their statistical significance, recent work focussed on masks and secondary anisotropies as potential sources…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-15 A. Rassat , J. -L. Starck , F. -X. Dupe

A study of the chiral behavior of pseudoscalar masses and decay constants is carried out in quenched lattice QCD with Wilson fermions. Using the modified quenched approximation (MQA) to cure the exceptional configuration problem, accurate…

High Energy Physics - Lattice · Physics 2009-10-31 W. Bardeen , A. Duncan , E. Eichten , H. Thacker

In this work we present constraints on different shapes of primordial non-Gaussianity using the Wilkinson Microwave Anisotropy Probe (WMAP) 7-year data and the spherical Mexican hat wavelet fnl estimator including the linear term…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-03 A. Curto , E. Martinez-Gonzalez , R. B. Barreiro

The semileptonic B-> Xu l nu decays allow a pretty clean determination of the CKM matrix element |Vub|. Nevertheless, the presence of weak-annihilation effects near the end-point of the q2 spectrum introduces uncertainties in the inclusive…

High Energy Physics - Phenomenology · Physics 2008-11-26 J. J. Sanz-Cillero

We study the differential decay rate for the rare $\Lambda_b \to \Lambda (\to N \pi)\ell^+\ell^-$ transition, including a determination of the complete angular distribution, assuming unpolarized $\Lambda_b$ baryons. On the basis of a…

High Energy Physics - Phenomenology · Physics 2016-01-12 Philipp Böer , Thorsten Feldmann , Danny van Dyk

The Shapley value is a ubiquitous framework for attribution in machine learning, encompassing feature importance, data valuation, and causal inference. However, its exact computation is generally intractable, necessitating efficient…

Machine Learning · Computer Science 2026-02-03 Fabian Fumagalli , Landon Butler , Justin Singh Kang , Kannan Ramchandran , R. Teal Witter

Minimal Dark Matter (MDM) is a theoretical framework highly appreciated for its minimality and yet its predictivity. Of the two only viable candidates singled out in the original analysis, the scalar eptaplet has been found to decay too…

High Energy Physics - Phenomenology · Physics 2016-05-04 Eugenio Del Nobile , Marco Nardecchia , Paolo Panci

Quantile regression is an important tool for estimation of conditional quantiles of a response Y given a vector of covariates X. It can be used to measure the effect of covariates not only in the center of a distribution, but also in the…

Statistics Theory · Mathematics 2017-10-03 Victor Chernozhukov

We consider large random matrices with a general slowly decaying correlation among its entries. We prove universality of the local eigenvalue statistics and optimal local laws for the resolvent away from the spectral edges, generalizing the…

Probability · Mathematics 2020-06-01 László Erdős , Torben Krüger , Dominik Schröder

In this work we use matrix models to study the problem of strength distributions. This is motivated by noticing near exponential fall offs of strengths in calculated magnetic dipole excitations. We emphasize that the quality of the…

Nuclear Theory · Physics 2021-04-27 Larry Zamick , Arun Kingan

We are using domain wall fermions to study $K \to \pi \pi$ matrix elements by measuring $K \to \pi$ and $K \to 0$ matrix elements on the lattice and employing chiral perturbation theory to relate these to the desired physical result. The…

High Energy Physics - Lattice · Physics 2009-10-31 Robert D. Mawhinney

This talk presents results of weak matrix elements calculated from simulations done on 170 $32^3 \times 64$ lattices at $\beta = 6.0$ using quenched Wilson fermions. We discuss the extraction of pseudoscalar decay constants $f_\pi$, $f_K$,…

High Energy Physics - Lattice · Physics 2007-05-23 Rajan Gupta , Tanmoy Bhattacharya

We consider the problem of learning a coefficient vector $x_{0}$ in $R^{N}$ from noisy linear observations $y=Fx_{0}+w$ in $R^{M}$ in the high dimensional limit $M,N$ to infinity with $\alpha=M/N$ fixed. We provide a rigorous derivation of…

Machine Learning · Statistics 2020-02-12 Cédric Gerbelot , Alia Abbara , Florent Krzakala