arXiv:2602.10332v1 Announce Type: cross Abstract: In the partially-observed outcome setting, a recent set of proposals known as “prediction-powered inference” (PPI) involve (i) applying a pre-trained machine learning model to predict the response, and then (ii) using these predictions to obtain an estimator of the parameter of interest with asymptotic variance no greater than that which would be obtained using only the labeled observations. While existing PPI proposals consider estimators arising from M-estimation, in this paper we generalize PPI […]

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arXiv:2502.18428v2 Announce Type: replace-cross Abstract: We study the asymptotic spectral distribution of the conjugate kernel random matrix $YY^top$, where $Y= f(WX)$ arises from a two-layer neural network model. We consider the setting where $W$ and $X$ are random rectangular matrices with i.i.d. entries, where the entries of $W$ follow a heavy-tailed distribution, while those of $X$ have light tails. Our assumptions on $W$ include a broad class of heavy-tailed distributions, such as symmetric $alpha$-stable laws with $alpha in […]

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I have been working as a Business Analyst (BA) in teams of various scales for over 7 years. During this time, I’ve frequently encountered a misunderstanding of the BA’s role and tasks from a developer’s perspective. In this article, I want to share my experience to help developers build effective communication with their BA and extract maximum value from this collaboration. 1. Introduction Throughout my professional journey, I’ve often seen the opinion that a Business Analyst is just […]

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submitted by /u/snakemas [link] [comments]

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Table Of Links Abstract 1 Introduction 2 Background 3 Privacy-Relevant Methods 4 Identifying API Privacy-relevant Methods 5 Labels for Personal Data Processing 6 Process of Identifying Personal Data 7 Data-based Ranking of Privacy-relevant Methods 8 Application to Privacy Code Review 9 Related Work Conclusion, Future Work, Acknowledgement And References Background Code review, originally aimed at ensuring software quality by identifying bugs and performance issues [11], has expanded to address security vulnerabilities and, more recently, privacy concerns under data […]

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arXiv:2601.02391v1 Announce Type: new Abstract: Wearable devices such as AI glasses are transforming voice assistants into always-available, hands-free collaborators that integrate seamlessly with daily life, but they also introduce challenges like egocentric audio affected by motion and noise, rapid micro-interactions, and the need to distinguish device-directed speech from background conversations. Existing benchmarks largely overlook these complexities, focusing instead on clean or generic conversational audio. To bridge this gap, we present WearVox, the first benchmark designed to rigorously evaluate […]

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Let’s be honest, code testing is everything but a joyful task. However, a good unit testing framework makes this process as smooth as possible. Eventually, testing becomes a regular and continuous process, accompanied by the assurance that our code will operate just as exact and seamlessly as a Swiss clockwork.

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arXiv:2511.16377v2 Announce Type: replace-cross Abstract: We investigate how to optimally design local differential privacy (LDP) mechanisms that reduce data unfairness and thereby improve fairness in downstream classification. We first derive a closed-form optimal mechanism for binary sensitive attributes and then develop a tractable optimization framework that yields the corresponding optimal mechanism for multi-valued attributes. As a theoretical contribution, we establish that for discrimination-accuracy optimal classifiers, reducing data unfairness necessarily leads to lower classification unfairness, thus providing a direct […]

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