Machine learning for targeted display advertising: Transfer learning in action

This paper presents a detailed discussion of problem formulation and data representation issues in the design, deployment, and operation of a massive-scale machine learning system for targeted display advertising. Notably, the machine learning system itself is deployed and has been in continual use for years, for thousands of advertising campaigns (in contrast to simply having the models from the system be deployed). In this application, acquiring sufficient data for training from the ideal sampling distribution is prohibitively expensive. Instead, data are drawn from surrogate domains and learning tasks, and then transferred to the target task. We present the design of this multistage transfer learning system, highlighting the problem formulation aspects. We then present a detailed experimental evaluation, showing that the different transfer stages indeed each add value. We next present production results across a variety of advertising clients from a variety of industries, illustrating the performance of the system in use. We close the paper with a collection of lessons learned from the work over half a decade on this complex, deployed, and broadly used machine learning system.Statistics Working Papers Serie

Genetic therapy for the nervous system

Genetic therapy is undergoing a renaissance with expansion of viral and synthetic vectors, use of oligonucleotides (RNA and DNA) and sequence-targeted regulatory molecules, as well as genetically modified cells, including induced pluripotent stem cells from the patients themselves. Several clinical trials for neurologic syndromes appear quite promising. This review covers genetic strategies to ameliorate neurologic syndromes of different etiologies, including lysosomal storage diseases, Alzheimer's disease and other amyloidopathies, Parkinson's disease, spinal muscular atrophy, amyotrophic lateral sclerosis and brain tumors. This field has been propelled by genetic technologies, including identifying disease genes and disruptive mutations, design of genomic interacting elements to regulate transcription and splicing of specific precursor mRNAs and use of novel non-coding regulatory RNAs. These versatile new tools for manipulation of genetic elements provide the ability to tailor the mode of genetic intervention to specific aspects of a disease state