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“No man ever steps in the same river twice, for it is not the same river and he is not the same man.” -Heraclitus, 5th century BCE Suppose there is some online information about you that you’d prefer to keep private. It could be something you worry others would find unprofessional, something you consider embarrassing, or something that is inaccurate. For any reason, you want that information not to show up when people look up your name. Privacy […]

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Release: datasette 1.0a28 I was upgrading Datasette Cloud to 1.0a27 and discovered a nasty collection of accidental breakages caused by changes in that alpha. This new alpha addresses those directly: Fixed a compatibility bug introduced in 1.0a27 where execute_write_fn() callbacks with a parameter name other than conn were seeing errors. (#2691) The database.close() method now also shuts down the write connection for that database. New datasette.close() method for closing down all databases and resources associated with a Datasette instance. This is called automatically when the server […]

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arXiv:2603.06431v2 Announce Type: replace-cross Abstract: Neural network methods for PDEs require reliable error control in function space norms. However, trained neural networks can typically only be probed at a finite number of point values. Without strong assumptions, point evaluations alone do not provide enough information to derive tight deterministic and guaranteed bounds on function space norms. In this work, we move beyond a purely black-box setting and exploit the neural network structure directly. We present a framework for […]

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arXiv:2603.02196v2 Announce Type: replace-cross Abstract: An agent must try new behaviors to explore and improve. In high-stakes environments, an agent that violates safety constraints may cause harm and must be taken offline, curtailing any future interaction. Imitating old behavior is safe, but excessive conservatism discourages exploration. How much behavior change is too much? We show how to use any safe reference policy as a probabilistic regulator for any optimized but untested policy. Conformal calibration on data from the […]

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arXiv:2604.14975v1 Announce Type: cross Abstract: To obtain more accurate model parameters and improve prediction accuracy, we proposed a regularized Kriging model that penalizes the hyperparameter theta in the Gaussian stochastic process, termed the Theta-regularized Kriging. We derived the optimization problem for this model from a maximum likelihood perspective. Additionally, we presented specific implementation details for the iterative process, including the regularized optimization algorithm and the geometric search cross-validation tuning algorithm. Three distinct penalty methods, Lasso, Ridge, and Elastic-net […]

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arXiv:2604.14908v1 Announce Type: cross Abstract: We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from finite codebooks, serve multiple single-antenna user equipments (UEs) with a unique beam per UE and discrete data transmission rates. BSs learn about transmission success based on ACK/NACK feedback. To encode service goals, we introduce a satisficing throughput threshold $tau_r$ and cast joint beam and rate adaptation as a combinatorial semi-bandit […]

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arXiv:2604.14587v1 Announce Type: cross Abstract: Lion optimizer is a popular learning-based optimization algorithm in machine learning, which shows impressive performance in training many deep learning models. Although convergence property of the Lion optimizer has been studied, its generalization analysis is still missing. To fill this gap, we study generalization property of the Lion via algorithmic stability based on the mathematical induction. Specifically, we prove that the Lion has a generalization error of $O(frac{1}{Ntau^T})$, where $N$ is training sample […]

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arXiv:2604.14809v1 Announce Type: new Abstract: We study a classification problem with three key challenges: pervasive informative missingness, the integration of partial prior expert knowledge into the learning process, and the need for interpretable decision rules. We propose a framework that encodes prior knowledge through an expert-guided class-conditional model for one or more classes, and use this model to construct a small set of interpretable goodness-of-fit features. The features quantify how well the observed data agree with the expert […]

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arXiv:2604.14322v1 Announce Type: new Abstract: We derive a robust update rule for the online infinite hidden Markov model (iHMM) for when the streaming data contains outliers and the model is misspecified. Leveraging recent advances in generalised Bayesian inference, we define robustness via the posterior influence function (PIF), and provide conditions under which the online iHMM has bounded PIF. Imposing robustness inevitably induces an adaptation lag for regime switching. Our method, which is called Batched Robust iHMM (BR-iHMM), balances […]

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arXiv:2604.13183v2 Announce Type: new Abstract: Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. Its core difficulty lies in severe semantic inconsistency caused by viewpoint variation and poor generalization under domain shift. Existing methods mainly rely on 2D correspondence, but they are easily distracted by redundant shared information across views, leading to less transferable representations. To address this, we propose GeoLink, a 3D-aware semantic-consistent framework for Generalizable cross-view […]

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