Training Complex Models with Multi-Task Weak Supervision

As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker forms of supervision that provide noisier but cheaper labels are often used. However, these weak supervision sources have diverse and unknown accuracies, may output correlated labels, and may label different tasks or apply at different levels of granularity. We propose a framework for integrating and modeling such weak supervision sources by viewing them as labeling different related sub-tasks of a problem, which we refer to as the multi-task weak supervision setting. We show that by solving a matrix completion-style problem, we can recover the accuracies of these multi-task sources given their dependency structure, but without any labeled data, leading to higher-quality supervision for training an end model. Theoretically, we show that the generalization error of models trained with this approach improves with the number of unlabeled data points, and characterize the scaling with respect to the task and dependency structures. On three fine-grained classification problems, we show that our approach leads to average gains of 20.2 points in accuracy over a traditional supervised approach, 6.8 points over a majority vote baseline, and 4.1 points over a previously proposed weak supervision method that models tasks separately

Does reflective supervision have a future in English local authority child and family social work?

Purpose – (1) to discuss the underlying assumption that social workers need reflective supervision specifically, as opposed to managerial or any other form of supervision or support; and (2) to consider whether our focus on the provision of reflective supervision may be preventing us from thinking more broadly and creatively about what support local authority child and family social workers need and how best to provide it. Methodology/approach – Argument based on own research and selective review of the literature Findings – Reflective supervision has no future in local authority child and family social work because (1) there is no clear understanding of what reflective supervision is, (2) there is no clear evidence for is effectiveness, and (3) a sizeable proportion of local authority child and family social workers in England do not receive reflective supervision and many never have. Originality/value – Challenges the received wisdom about the value of reflective supervision and advocates exploring alternative models for supporting best practice in child and family social work

A peer-driven community-based supervisory model: development from an evaluation of an ethics workshop for doctoral students undertaking research with children

Differing doctoral supervision models currently exist. Three key conceptual supervisory models relevant to doctoral students from within the healthcare professions were identified from a literature review: the ‘functional pre-modern’ model, the ‘team’ model and the ‘community group’ model. However, whilst these models exist, for the most part, supervision remains embedded within home academic institutions. Method and material: (1) An extensive review of the literature was undertaken, drawing on: Australian Education Index, British Education Index, the British Humanities Index, the British Nursing Index, EBSCOHOST EJS and Google™ Scholar; (2) an outcome-oriented evaluation of a workshop delivered to seven current or prospective doctoral candidates from within the health care professions and researching with children and/or young people, concerning the conduct of ethical research was undertaken Results: Five key categories related to ‘best things about the day’ were identified from a four-item, anonymous questionnaire appraising the day. These concerned: round table discussions, plenary seminars, workshop organisation, value of experiential learning and future workshop opportunities. From these themes an ‘innovative’ peer-driven, community based model of doctoral supervision was developed that is extrinsic to and complements the supervision provided in students’ home academic institutions. Conclusions: The innovative supervisory model developed through an outcome-oriented evaluation of a workshop for doctoral candidates has particular relevance for doctoral students who are healthcare professionals generally and nurses in particular, especially those studying in highly specialised areas where there may be a dearth of subject specific supervisors

Early Warning Models for Banking Supervision in Romania

In this paper we propose an early warning system for the Romanian banking sector, as an addition to the standardized CAAMPL rating system used by the National Bank of Romania for assessing the local credit institutions. We aim to find the determinants for downgrades as well as for a bank to have a weak overall position, to estimate the respective probabilities and to be able to perform rating predictions. Having this purpose, we build two models with binary dependent variables and one ordered logistic model that accounts for all possible future ratings. One result is that indicators for current position, market share, profitability and assets quality determine rating downgrades, whereas capital adequacy, liquidity and macroeconomic environment are not represented in the model. Banks that will have a weak overall position in one year can be predicted using also indicators for current position, market share, profitability and assets quality, as well as, in this case, capital adequacy and macroeconomic environment, the latter only for the binary dependent variable model, leaving liquidity indicators out again. Based on the ordered logistic model’s capacity for rating prediction, we estimated one year horizon scores and ratings for each bank and we aggregated these results for predicting a measure of assessing the local banking sector as a whole.early warning system, CAAMPL rating system