Academic Remediation of Non-Disability, Middle School Students

Strategies were developed to address perceived teacher practitioner concerns regarding three areas considered to be negatively impacting student academic performance and three skills perceived to be crucial to student academic success. The six categories addressed were identified through the author\u27s development and implementation of a survey used among middle school teachers

Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners

In the arena of privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) has outstripped the objective perturbation mechanism in popularity and interest. Though unrivaled in versatility, DP-SGD requires a non-trivial privacy overhead (for privately tuning the model's hyperparameters) and a computational complexity which might be extravagant for simple models such as linear and logistic regression. This paper revamps the objective perturbation mechanism with tighter privacy analyses and new computational tools that boost it to perform competitively with DP-SGD on unconstrained convex generalized linear problems

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy

Posterior sampling, i.e., exponential mechanism to sample from the posterior distribution, provides $\varepsilon$-pure differential privacy (DP) guarantees and does not suffer from potentially unbounded privacy breach introduced by $(\varepsilon,\delta)$-approximate DP. In practice, however, one needs to apply approximate sampling methods such as Markov chain Monte Carlo (MCMC), thus re-introducing the unappealing $\delta$-approximation error into the privacy guarantees. To bridge this gap, we propose the Approximate SAample Perturbation (abbr. ASAP) algorithm which perturbs an MCMC sample with noise proportional to its Wasserstein-infinity ($W_\infty$) distance from a reference distribution that satisfies pure DP or pure Gaussian DP (i.e., $\delta=0$). We then leverage a Metropolis-Hastings algorithm to generate the sample and prove that the algorithm converges in W$_\infty$ distance. We show that by combining our new techniques with a careful localization step, we obtain the first nearly linear-time algorithm that achieves the optimal rates in the DP-ERM problem with strongly convex and smooth losses

Long-term impact on healthcare resource utilization of statin treatment, and its cost effectiveness in the primary prevention of cardiovascular disease: a record linkage study

Aims: To assess the impact on healthcare resource utilization, costs, and quality of life over 15 years from 5 years of statin use in men without a history of myocardial infarction in the West of Scotland Coronary Prevention Study (WOSCOPS).<p></p> Methods: Six thousand five hundred and ninety-five participants aged 45–54 years were randomized to 5 years treatment with pravastatin (40 mg) or placebo. Linkage to routinely collected health records extended follow-up for secondary healthcare resource utilization to 15 years. The following new results are reported: cause-specific first and recurrent cardiovascular hospital admissions including myocardial infarction, heart failure, stroke, coronary revascularization and angiography; non-cardiovascular hospitalization; days in hospital; quality-adjusted life years (QALYs); costs of pravastatin treatment, treatment safety monitoring, and hospital admissions.<p></p> Results: Five years treatment of 1000 patients with pravastatin (40 mg/day) saved the NHS £710 000 (P < 0.001), including the cost of pravastatin and lipid and safety monitoring, and gained 136 QALYs (P = 0.017) over the 15-year period. Benefits per 1000 subjects, attributable to prevention of cardiovascular events, included 163 fewer admissions and a saving of 1836 days in hospital, with fewer admissions for myocardial infarction, stroke, heart failure and coronary revascularization. There was no excess in non-cardiovascular admissions or costs (or in admissions associated with diabetes or its complications) and no evidence of heterogeneity of effect over sub-groups defined by baseline cardiovascular risk.<p></p> Conclusion: Five years' primary prevention treatment of middle-aged men with a statin significantly reduces healthcare resource utilization, is cost saving, and increases QALYs. Treatment of even younger, lower risk individuals is likely to be cost-effective.<p></p&gt