Federal data shows identity theft losses can extend far beyond direct financial damage as victims spend weeks repairing credit files.

AI regulation requires harmonization between state and federal approaches, but fragmented policies risk undermining US competitive advantage in this critical technology.

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Area under ROC (AUC) is a metric which is widely used for measuring the classification performance for imbalanced data.

We present a variety of projection-based linear regression algorithms with a focus on modern machine-learning models and their algorithmic performance. We study the role of the relaxation parameter in generalized Kaczmarz algorithms and establish a priori regret bounds with explicit $λ$-dependence to quantify how much an algorithm's performance deviates from its optimal performance. A detailed analysis of relaxation parameter is also provided. Applications include: explicit regret bounds for the framework of Kaczmarz algorithm models, non-orthogonal Fourier expansions, and the use of regret estimates in modern machine learning models, including for noisy data, i.e., regret bounds for the noisy Kaczmarz algorithms. Motivated by machine-learning practice, our wider framework treats bounded operators (on infinite-dimensional Hilbert spaces), with updates realized as (block) Kaczmarz algorithms, leading to new and versatile results.

We study how Bayesian optimization (BO) can be accelerated on a target task with historical knowledge transferred from related source tasks. Existing works on BO with knowledge transfer either do not have theoretical guarantees or achieve the same regret as BO in the non-transfer setting, $\tilde{\mathcal{O}}(\sqrt{T γ_f})$, where $T$ is the number of evaluations of the target function and $γ_f$ denotes its information gain. In this paper, we propose the DeltaBO algorithm, in which a novel uncertainty-quantification approach is built on the difference function $δ$ between the source and target functions, which are allowed to belong to different reproducing kernel Hilbert spaces (RKHSs). Under mild assumptions, we prove that the regret of DeltaBO is of order $\tilde{\mathcal{O}}(\sqrt{T (T/N + γ_δ)})$, where $N$ denotes the number of evaluations from source tasks and typically $N \gg T$. In many applications, source and target tasks are similar, which implies that $γ_δ$ can be much smaller than $γ_f$. Empirical studies on both real-world hyperparameter tuning tasks and synthetic functions show that DeltaBO outperforms other baseline methods and support our theoretical claims.

Is hip tightness to blame for your back or knee pain? The cool tattoos are optional. Breakthroughs, discoveries, and DIY tips sent every weekday. Because, even among those of us who exercise regularly, the further we get from childhood the more limited our varieties of movement typically become, leading to weaker muscles, brittler bones and less mobile joints. "We don't move laterally as much anymore, as we get older and we're not playing sports. Even if you're long-distance running, you're just moving in one plane [of motion]," says Patrick Suarez, OCS, SCS, a physical therapist based in Albany, New York.

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