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We study the problem of mismatched binary hypothesis testing between i.i.d. distributions. We analyze the tradeoff between the pairwise error probability exponents when the actual distributions generating the observation are different from…

Information Theory · Computer Science 2022-04-28 Parham Boroumand , Albert Guillén i Fàbregas

The fact that the author of an excellent textbook on electromagnetism could be duped by "hidden momentum" vividly illustrates the problematic nature of its use.

Classical Physics · Physics 2010-05-17 Timothy H. Boyer

One of the two basic theorems in [5] on the existence of solutions of PDEs is improved with the use of a group analysis type argument.

General Mathematics · Mathematics 2007-05-23 Elemer E Rosinger

Machine learning algorithms in socially sensitive domains (e.g., credit decisions) often focus on equalizing predictive outcomes. However, satisfying these metrics does not guarantee that models use the same reasoning for different groups.…

Machine Learning · Computer Science 2026-05-14 Gideon Popoola , John Sheppard

A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information…

Econometrics · Economics 2024-05-20 Silvia Goncalves , Serena Ng

We discuss the methods of Evans and Moshonov [Bayesian Analysis 1 (2006) 893--914, Bayesian Statistics and Its Applications (2007) 145--159] concerning checking for prior-data conflict and their relevance to the method proposed in this…

Methodology · Statistics 2009-09-29 M. Evans

Is perfect error correction always worth the trouble? A framework is presented for the analysis of error detection and correction in multi-level systems of communication that takes into account degrees of freedom attended and ignored by…

Information Theory · Computer Science 2020-01-15 Tom Sgouros

This paper has been withdrawn by the author due to a crucial sign error in Proposition 3.1.

Probability · Mathematics 2009-06-15 Yu Zhang

The author has recognized an error in the section of the text regarding induced Compton scattering. The paper has been withdrawn pending a revision to this section.

Astrophysics · Physics 2010-12-13 Jean-Pierre Macquart

Benchmarking models is a key factor for the rapid progress in machine learning (ML) research. Thus, further progress depends on improving benchmarking metrics. A standard metric to measure the behavioral alignment between ML models and…

Neurons and Cognition · Quantitative Biology 2025-11-10 Thomas Klein , Sascha Meyen , Wieland Brendel , Felix A. Wichmann , Kristof Meding

We appended an errata to the original submission. The purpose of this errata is to point out two errors in [2] and give a weakened version of those statements made.

Probability · Mathematics 2024-07-02 Erhan Bayraktar , Gaoyue Guo

We propose a fundamental theory on ensemble learning that answers the central question: what factors make an ensemble system good or bad? Previous studies used a variant of Fano's inequality of information theory and derived a lower bound…

Machine Learning · Computer Science 2023-11-17 Terufumi Morishita , Gaku Morio , Shota Horiguchi , Hiroaki Ozaki , Nobuo Nukaga

In this work, we investigate the relationship between model generalization and counterfactual explainability in supervised learning. We introduce the notion of $\varepsilon$-valid counterfactual probability ($\varepsilon$-VCP) -- the…

Machine Learning · Computer Science 2025-05-30 Fabiano Veglianti , Flavio Giorgi , Fabrizio Silvestri , Gabriele Tolomei

Counterfactual explanations (CFEs) offer a tangible and actionable way to explain recommendations by showing users a "what-if" scenario that demonstrates how small changes in their history would alter the system's output. However, existing…

Information Retrieval · Computer Science 2025-08-13 Arjan Hasami , Masoud Mansoury

Counterfactual explanations for machine learning models are used to find minimal interventions to the feature values such that the model changes the prediction to a different output or a target output. A valid counterfactual explanation…

Machine Learning · Computer Science 2023-03-23 Shravan Kumar Sajja , Sumanta Mukherjee , Satyam Dwivedi

We present empirical data on misprints in citations to twelve high-profile papers. The great majority of misprints are identical to misprints in articles that earlier cited the same paper. The distribution of the numbers of misprint…

Physics and Society · Physics 2011-09-13 M. V. Simkin , V. P. Roychowdhury

Counterfactual Explanations (CFEs) interpret machine learning models by identifying the smallest change to input features needed to change the model's prediction to a desired output. For classification tasks, CFEs determine how close a…

Machine Learning · Computer Science 2025-10-01 Margarita A. Guerrero , Cristian R. Rojas

We consider the fundamental problem of matching a template to a signal. We do so by M-estimation, which encompasses procedures that are robust to gross errors (i.e., outliers). Using standard results from empirical process theory, we derive…

Statistics Theory · Mathematics 2020-09-10 Ery Arias-Castro , Lin Zheng

Wiseman has claimed that Bell was wrong in stating that determinism was inferred rather than assumed in the summary of the EPR argument in his 1964 paper. The reply of Wiseman and his co-authors to my comment misstates my reasons for…

Quantum Physics · Physics 2016-08-24 Edward J. Gillis

Some mathematical errors of the paper commented upon [W.-M. Suen, Phys. Rev. D 40, (1989) 315] are corrected.

General Relativity and Quantum Cosmology · Physics 2009-11-07 H. -J. Schmidt