A new PAC bound for intersection-closed concept classes

Auer, Péter; Ortner, Ronald

doi:10.1007/s10994-006-8638-3

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Uncertainty sampling attempts to target the principal measure of inadequate testing resources, namely, the positivity rate. Uncertainty sampling is different from merely targeting the positivity rate, however, because it accounts for the uncertainty in the rate . This is important because underserved communities may have a seemingly low positivity rate owing to randomness and low testing volume.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Allocation Schemes of COVID-19 Testing Resources in a Community-Based Door-to-Door Testing Program

2021

JAMA Health Forum

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IMPORTANCE Overcoming social barriers to COVID-19 testing is an important issue, especially given the demographic disparities in case incidence rates and testing. Delivering culturally appropriate testing resources using data-driven approaches in partnership with community-based health workers is promising, but little data are available on the design and effect of such interventions.OBJECTIVES To assess and evaluate a door-to-door COVID-19 testing initiative that allocates visits by community health workers by selecting households in areas with a high number of index cases, by using uncertainty sampling for areas where the positivity rate may be highest, and by relying on local knowledge of the health workers. DESIGN, SETTING, AND PARTICIPANTSThis cohort study was performed from December 18, 2020, to February 18, 2021. Community health workers visited households in neighborhoods in East San Jose, California, based on index cases or uncertainty sampling while retaining discretion to use local knowledge to administer tests. The health workers, also known as promotores de salud (hereinafter referred to as promotores) spent a mean of 4 days a week conducting door-to-door COVID-19 testing during the 2-month study period. All residents of East San Jose were eligible for COVID-19 testing. The promotores were selected from the META cooperative (Mujeres Empresarias Tomando Acción [Entrepreneurial Women Taking Action]). INTERVENTIONSThe promotores observed self-collection of anterior nasal swab samples for SARS-CoV-2 reverse transcriptase-polymerase chain reaction tests. MAIN OUTCOMES AND MEASURESA determination of whether door-to-door COVID-19 testing was associated with an increase in the overall number of tests conducted, the demographic distribution of the door-to-door tests vs local testing sites, and the difference in positivity rates among the 3 door-to-door allocation strategies. RESULTS A total of 785 residents underwent door-to-door testing, and 756 were included in the analysis. Among the 756 individuals undergoing testing (61.1% female; 28.2% aged 45-64 years), door-to-door COVID-19 testing reached different populations than standard public health surveillance, with 87.6% (95% CI, 85.0%-89.8%) being Latinx individuals. The closest available testing site only reached 49.0% (95% CI, 48.3%-49.8%) Latinx individuals. Uncertainty sampling provided the most effective allocation, with a 10.8% (95% CI, 6.8%-16.0%) positivity rate, followed by 6.4% (95% CI, 4.1%-9.4%) for local knowledge, and 2.6% (95% CI, 0.7%-6.6%) for index area selection. The intervention was also associated with increased overall testing capacity by 60% to 90%, depending on the testing protocol.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Allocation Schemes of COVID-19 Testing Resources in a Community-Based Door-to-Door Testing Program

2021

JAMA Health Forum

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“…In such language collections, we can think of mind change optimal methods as choosing the simplest hypothesis consistent with the data when a unique simplest hypothesis is available. 5 …”

Section: Necessary and Sufficient Conditions For Strongly Mind Changementioning

confidence: 99%

“…We would like to characterize the learning problems for which this tension arises, and how great the trade-off can be. For example, if a language collection L is closed under intersection, then conjecturing ∩(L| ) for every data sequence yields an SMC-optimal learner that never procrastinates (the so-called "closure algorithm" [5]). The language collection LINEAR and the learner LIN are an instance of an intersection-closed language class and the corresponding closure algorithm.…”

Section: Summary and Future Workmentioning

confidence: 99%

Mind Change Efficient Learning

Luo

2005

Learning Theory

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This paper studies efficient learning with respect to mind changes. Our starting point is the idea that a learner that is efficient with respect to mind changes minimizes mind changes not only globally in the entire learning problem, but also locally in subproblems after receiving some evidence. Formalizing this idea leads to the notion of strong mind change optimality. We characterize the structure of language classes that can be identified with at most mind changes by some learner (not necessarily effective): a language class L is identifiable with mind changes iff the accumulation order of L is at most . Accumulation order is a classic concept from point-set topology. We show that accumulation order is related to other established notions of structural complexity, such as thickness and intrinsic complexity. To aid the construction of learning algorithms, we show that the characteristic property of strongly mind change optimal learners is that they output conjectures (languages) with maximal accumulation order. We illustrate the theory by describing strongly mind change optimal learners for various problems such as identifying linear subspaces, one-variable patterns, and fixed-length patterns.

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Mechanizing Induction

Ortner¹,

Leitgeb²

2011

Handbook of the History of Logic

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A new PAC bound for intersection-closed concept classes

Cited by 7 publications

References 11 publications

Evaluation of Allocation Schemes of COVID-19 Testing Resources in a Community-Based Door-to-Door Testing Program

Evaluation of Allocation Schemes of COVID-19 Testing Resources in a Community-Based Door-to-Door Testing Program

Mind Change Efficient Learning

Mechanizing Induction

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