Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β\beta-Mixing Processes

Pac-Bayes bounds are among the most accurate generalization bounds for classifiers learned from independently and identically distributed (IID) data, and it is particularly so for margin classifiers: there have been recent contributions showing how practical these bounds can be either to perform model selection (Ambroladze et al., 2007) or even to directly guide the learning of linear classifiers (Germain et al., 2009). However, there are many practical situations where the training data show some dependencies and where the traditional IID assumption does not hold. Stating generalization bounds for such frameworks is therefore of the utmost interest, both from theoretical and practical standpoints. In this work, we propose the first - to the best of our knowledge - Pac-Bayes generalization bounds for classifiers trained on data exhibiting interdependencies. The approach undertaken to establish our results is based on the decomposition of a so-called dependency graph that encodes the dependencies within the data, in sets of independent data, thanks to graph fractional covers. Our bounds are very general, since being able to find an upper bound on the fractional chromatic number of the dependency graph is sufficient to get new Pac-Bayes bounds for specific settings. We show how our results can be used to derive bounds for ranking statistics (such as Auc) and classifiers trained on data distributed according to a stationary {\ss}-mixing process. In the way, we show how our approach seemlessly allows us to deal with U-processes. As a side note, we also provide a Pac-Bayes generalization bound for classifiers learned on data from stationary $\varphi$-mixing distributions.Comment: Long version of the AISTATS 09 paper: http://jmlr.csail.mit.edu/proceedings/papers/v5/ralaivola09a/ralaivola09a.pd

Business Students' Preferences in the Use of French and Raven's Five Bases of Power

B.S. (Bachelor of Science

Combination octreotide, midodrine, and albumin may improve survival in patients with Hepatorenal syndrome, but the evidence is weak

A critical appraisal and clinical application of Skagen C, Einstein M, Lucey MR, Said A. Combination treatment with octreotide, midodrine, and albumin improves survival in patients with type 1 and type 2 hepatorenal syndrome. J Clin Gastroenterol. 2009 Aug;43(7):680-5. doi: 10.1097/MCG.0b013e318188947

Learning the optimal scale for GWAS through hierarchical SNP aggregation

Motivation: Genome-Wide Association Studies (GWAS) seek to identify causal genomic variants associated with rare human diseases. The classical statistical approach for detecting these variants is based on univariate hypothesis testing, with healthy individuals being tested against affected individuals at each locus. Given that an individual's genotype is characterized by up to one million SNPs, this approach lacks precision, since it may yield a large number of false positives that can lead to erroneous conclusions about genetic associations with the disease. One way to improve the detection of true genetic associations is to reduce the number of hypotheses to be tested by grouping SNPs. Results: We propose a dimension-reduction approach which can be applied in the context of GWAS by making use of the haplotype structure of the human genome. We compare our method with standard univariate and multivariate approaches on both synthetic and real GWAS data, and we show that reducing the dimension of the predictor matrix by aggregating SNPs gives a greater precision in the detection of associations between the phenotype and genomic regions

Book review: Politics and expertise: how to use science in a democratic society by Zeynep Pamuk

In Politics and Expertise: How to Use Science in a Democratic Society, Zeynep Pamuk reimagines the relationship between democratic politics and scientific expertise, exploring the possibility of new political institutions that would make experts more accountable to the lay public. In a post-COVID world where contestation of both science and public institutions is on the rise, Pamuk’s book will remain a central point of reference for institutional theorists in the years to come, writes Mikołaj Szafrański. Politics and Expertise: How to Use Science in a Democratic Society. Zeynep Pamuk. Princeton University Press. 2021

TREATMENT DROPOUT PREDICTORS OF OEF/OIF/OND VETERANS WITHIN A MULITFACETED INPATIENT TREATMENT PROGRAM

Veterans of Operation Enduring Freedom (OEF), Operation Iraqi Freedom (OIF) and Operation New Dawn (OND) dropout of psychotherapy more often than Vietnam and Gulf War Veterans. Attrition reduces the effectiveness of evidence-based treatments, resulting in fewer benefits for Veterans. Outpatient treatment studies have identified age, symptom severity, and personality characteristics along with a number of other variables as predictors of dropout. However, to the best of our knowledge, to date no study has examined rates or predictors of attrition within OEF/OIF/OND Veterans seeking voluntary inpatient treatment. This study examined 436 (Male = 296, Female = 140) OEF/OIF/OND Veterans seeking inpatient treatment for PTSD and other psychological disorders. Males (24.3%) displayed significantly higher rates of attrition than females (11.4%). Treatment completers and dropouts differed on a variety of variables including, PTSD diagnosis, rate of improvement during treatment and substance abuse. Regression results for female OEF/OIF/OND Veterans indicated five significant unique predictors of attrition (PTSD diagnosis, bi-polar diagnosis, lower rate of improvement during treatment, lower suicidality ratings and race). Caucasian females were more likely to withdraw from treatment than noncaucasians. Regression results for male OEF/OIF/OND Veterans indicated six unique predictors of attrition (no PTSD diagnosis, positive urinary drug screening, lower rate of improvement during treatment, higher service connection for mental health, younger age and higher military rank).Psychology, Department o

Oscillation of solutions of some nonlinear difference equations

Sufficient conditions for the oscillation of some nonlinear difference equations are established

Composite kernel learning

The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Multiple Kernel Learning (MKL) enables to learn the kernel, from an ensemble of basis kernels, whose combination is optimized in the learning process. Here, we propose Composite Kernel Learning to address the situation where distinct components give rise to a group structure among kernels. Our formulation of the learning problem encompasses several setups, putting more or less emphasis on the group structure. We characterize the convexity of the learning problem, and provide a general wrapper algorithm for computing solutions. Finally, we illustrate the behavior of our method on multi-channel data where groups correpond to channels. 1