Robust semi-supervised learning: projections, limits & constraints

In many domains of science and society, the amount of data being gathered is increasing rapidly. To estimate input-output relationships that are often of interest, supervised learning techniques rely on a specific type of data: labeled examples for which we know both the input and an outcome. The problem of semi-supervised learning is how to use, increasingly abundantly available, unlabeled examples, with unknown outcomes, to improve supervised learning methods. This thesis is concerned with the question if and how these improvements are possible in a "robust", or safe, way: can we guarantee these methods do not lead to worse performance than the supervised solution?We show that for some supervised classifiers, most notably, the least squares classifier, semi-supervised adaptations can be constructed where this non-degradation in performance can indeed be guaranteed, in terms of the surrogate loss used by the classifier. Since these guarantees are given in terms of the surrogate loss, we explore why this is a useful criterion to evaluate performance. We then prove that semi-supervised versions with strict non-degradation guarantees are not possible for a large class of commonly used supervised classifiers. Other aspects covered in the thesis include optimistic learning, the peaking phenomenon and reproducibility.COMMIT - Project P23LUMC / Geneeskunde Repositoriu

Enquête sur l'enquête 'Les réseaux économiques souterrains en cité de transit (1981-2010)' de Jean-François Laé et Numa Murard

L’enquête « Les réseaux économiques souterrains en cité de transit » a été réalisée par Jean-François Laé, professeur émérite de l’Université Paris 8 Vincennes – Saint Denis et Numa Murard, professeur émérite de l’Université Paris Diderot. Elle a la particularité d’avoir été menée en deux fois, puisqu’elle a donné lieu à une première enquête réalisée au début des années 1980 puis à un retour sur enquête en 2010. L’origine de cette recherche remonte à l’expérience de Jean-François Laé comme travailleur social dans une cité dite de transit de la ville d’Elbeuf, en Seine-Maritime. Après sa rencontre avec Numa Murard au CERFI (Centre d’études, de recherche et de formation institutionnelle), ils décident tous deux de réaliser cette enquête, ayant obtenu des financements de la CNAF (Caisse nationale des affaires familiales) et du ministère de l’Urbanisme et du Logement. Elle donnera lieu à la rédaction d’un rapport et à la publication d’un ouvrage en 1985, L’Argent des pauvres. Trente ans plus tard, les deux chercheurs décident de revenir sur les terrains de leur première enquête, dans le cadre d’un documentaire radiophonique. Un ouvrage sera publié suite à ce retour, intitulé Deux générations dans la débine et paru en 2012. Pour l’enquête initiale comme pour le retour sur enquête, les deux chercheurs se sont immergés en ethnographes dans la vie quotidienne des habitants de la cité de transit. S’ils se sont principalement focalisés sur la vie économique des enquêtés, ils ont ouvert un ensemble de thématique allant bien au-delà de ce que laisse à penser le titre de l’enquête. Si la méthodologie est particulière, la méthode d’exposition l’est tout autant puisqu’elle ressort de ce que Jean-François Laé et Numa Murard appellent la « sociologie narrative ». Le corpus de documents fourni par les chercheurs a trait aux deux étapes de cette recherche. Il réunit notamment un carnet de terrain et le rapport publié suite à la première enquête, de même que différentes notes préparatoires, des photos et des transcriptions d’enregistrements collectés lors du retour sur enquête. S’il est parcellaire du fait de la perte de certains documents, ce corpus donne une idée précise des méthodes d’enquête des deux chercheurs et ouvre des pistes de réutilisation, notamment dans un cadre pédagogique. Deux entretiens ont été réalisés par l'équipe beQuali avec les auteurs de l'enquête : le premier avec Jean-François Laé,Numa Murard et Fabien Deshayes au CRESPPA, le deuxième avec Jean-François Laé et Numa Murard au CDSP

Impaired fertility in men diagnosed with inflammatory arthritis: results of a large multicentre study (iFAME-Fertility)

Objectives The impact of inflammatory arthritis (IA) on male fertility remains unexplored. Our objective was to evaluate the impact of IA on several male fertility outcomes; fertility rate (number of biological children per man), family planning, childlessness and fertility problems.Methods We performed a multicentre cross-sectional study (iFAME-Fertility). Men with IA 40 years or older who indicated that their family size was complete were invited to participate. Participants completed a questionnaire that included demographic, medical and fertility-related questions. To analyse the impact of IA on fertility rate, patients were divided into groups according to the age at the time of their diagnosis: = 41 years (after the peak).Results In total 628 participants diagnosed with IA were included. Men diagnosed = 41 years (1.88 (SD 1.14)). This was statistically significant (p=0.0004). The percentages of men diagnosed = 41 years.Conclusions This is the first study that shows that IA can impair male fertility. Men diagnosed with IA before and during the peak of reproductive age had a lower fertility rate, higher childlessness rate and more fertility problems. Increased awareness and more research into the causes behind this association are urgently needed.Pathophysiology and treatment of rheumatic disease

Drug-gene interactions of antihypertensive medications and risk of incident cardiovascular disease: A pharmacogenomics study from the CHARGE consortium

Background Hypertension is a major risk factor for a spectrum of cardiovascular diseases (CVD), including myocardial infarction, sudden death, and stroke. In the US, over 65 million people have high blood pressure and a large proportion of these individuals are prescribed antihypertensive medications. Although large long-term clinical trials conducted in the last several decades have identified a number of effective antihypertensive treatments that reduce the risk of future clinical complications, responses to therapy and protection from cardiovascular events vary among individuals. Methods Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk ofmajor cardiovascular outcomes. The classes of drug treatments included angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and diuretics. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, each study performed array-based genome-wide genotyping, imputed to HapMap Phase II reference panels, and used additive genetic models in proportional hazards or logistic regressionmodels to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide polymorphisms (SNPs) in a discovery analysis among 15,375 European Ancestry participants (3,527 CVD cases) with targeted follow-up in a case-only study of 1,751 European Ancestry GenHAT participants as well as among 4,141 African-Americans (1,267 CVD cases). Results Although drug-SNP interactions were biologically plausible, exposures and outcomes were well measured, and power was sufficient to detect modest interactions, we did not identify any statistically significant interactions from the four antihypertensive therapy meta-analyses (Pinteraction > 5.0×10-8). Similarly, findings were null for meta-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genom

Improving Cross-Validation Classifier Selection Accuracy through Meta-learning

In order to choose from the large number of classification methods available for use, cross-validation error estimates are often employed. We present this cross-validation selection strategy in the framework of meta-learning and show that conceptually, meta-learning techniques could provide better classifier selections than traditional cross-validation selection. Using various simulation studies we illustrate and discuss this possibility. Through a collection of datasets resembling real-world data, we investigate whether these improvements could possibly exist in the real-world as well. Although the approach presented here currently requires significant investment when applied to practical applications, the concept of being able to outperform cross-validation selection opens the door to new classifier selection strategies.Pattern Recognition LabIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning

Contains fulltext : 199900.pdf (publisher's version ) (Open Access

Nuclear discrepancy for single-shot batch active learning

Active learning algorithms propose what data should be labeled given a pool of unlabeled data. Instead of selecting randomly what data to annotate, active learning strategies aim to select data so as to get a good predictive model with as little labeled samples as possible. Single-shot batch active learners select all samples to be labeled in a single step, before any labels are observed.We study single-shot active learners that minimize generalization bounds to select a representative sample, such as the maximum mean discrepancy (MMD) active learner.We prove that a related bound, the discrepancy, provides a tighter worst-case bound. We study these bounds probabilistically, which inspires us to introduce a novel bound, the nuclear discrepancy (ND). The ND bound is tighter for the expected loss under optimistic probabilistic assumptions. Our experiments show that the MMD active learner performs better than the discrepancy in terms of the mean squared error, indicating that tighter worst case bounds do not imply better active learning performance. The proposed active learner improves significantly upon the MMD and discrepancy in the realizable setting and a similar trend is observed in the agnostic setting, showing the benefits of a probabilistic approach to active learning. Our study highlights that assumptions underlying generalization bounds can be equally important as bound-tightness, when it comes to active learning performance. Code for reproducing our experimental results can be found at https://github.com/tomviering/ NuclearDiscrepancy

Robust Importance-Weighted Cross-Validation Under Sample Selection Bias

Contains fulltext : 214642.pdf (publisher's version ) (Closed access)2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), 13-16 October 2019 Pittsburgh, PA US

The association of comorbidity with Parkinson's disease-related hospitalizations

INTRODUCTION: Unplanned hospital admissions associated with Parkinson's disease could be partly attributable to comorbidities. METHODS: We studied nationwide claims databases and registries. Persons with newly diagnosed Parkinson's disease were identified based on the first Parkinson's disease-related reimbursement claim by a medical specialist. Comorbidities were classified based on the Charlson Comorbidity Index. We studied hospitalization admissions because of falls, psychiatric diseases, pneumonia and urinary tract infections, PD-related hospitalizations-not otherwise specified. The association between comorbidities and time-to-hospitalization was estimated using Cox proportional hazard modelling. To better understand pathways leading to hospitalizations, we performed multiple analyses on causes for hospitalizations. RESULTS: We identified 18 586 people with newly diagnosed Parkinson's disease. The hazard of hospitalization was increased in persons with peptic ulcer disease (HR 2.20, p = 0.009), chronic obstructive pulmonary disease (HR 1.61, p < 0.001), stroke (HR 1.37, p = 0.002) and peripheral vascular disease (HR 1.31, p = 0.02). In the secondary analyses, the hazard of PD-related hospitalizations-not otherwise specified (HR 3.24, p = 0.02) and pneumonia-related hospitalization (HR 2.90, p = 0.03) was increased for those with comorbid peptic ulcer disease. The hazard of fall-related hospitalization (HR 1.57, p = 0.003) and pneumonia-related hospitalization (HR 2.91, p < 0.001) was increased in persons with chronic obstructive pulmonary disease. The hazard of pneumonia-related hospitalization was increased in those with stroke (HR 1.54, p = 0.03) or peripheral vascular disease (HR 1.60, p = 0.02). The population attributable risk of comorbidity was 8.4%. CONCLUSION: Several comorbidities increase the risk of Parkinson's disease related-hospitalization indicating a need for intervention strategies targeting these comorbid disorders