Why the Economics Profession Must Actively Participate in the Privacy Protection Debate

When Google or the U.S. Census Bureau publish detailed statistics on browsing habits or neighborhood characteristics, some privacy is lost for everybody while supplying public information. To date, economists have not focused on the privacy loss inherent in data publication. In their stead, these issues have been advanced almost exclusively by computer scientists who are primarily interested in technical problems associated with protecting privacy. Economists should join the discussion, first, to determine where to balance privacy protection against data quality; a social choice problem. Furthermore, economists must ensure new privacy models preserve the validity of public data for economic research

Bayesian orthogonal component analysis for sparse representation

This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This under-complete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with an unknown orthogonal mixing matrix. This issue is formulated in a Bayesian framework. First, the unknown sparse sources are modeled as Bernoulli-Gaussian processes. To promote sparsity, a weighted mixture of an atom at zero and a Gaussian distribution is proposed as prior distribution for the unobserved sources. A non-informative prior distribution defined on an appropriate Stiefel manifold is elected for the mixing matrix. The Bayesian inference on the unknown parameters is conducted using a Markov chain Monte Carlo (MCMC) method. A partially collapsed Gibbs sampler is designed to generate samples asymptotically distributed according to the joint posterior distribution of the unknown model parameters and hyperparameters. These samples are then used to approximate the joint maximum a posteriori estimator of the sources and mixing matrix. Simulations conducted on synthetic data are reported to illustrate the performance of the method for recovering sparse representations. An application to sparse coding on under-complete dictionary is finally investigated.Comment: Revised version. Accepted to IEEE Trans. Signal Processin

Private Learning Implies Online Learning: An Efficient Reduction

We study the relationship between the notions of differentially private learning and online learning in games. Several recent works have shown that differentially private learning implies online learning, but an open problem of Neel, Roth, and Wu \cite{NeelAaronRoth2018} asks whether this implication is {\it efficient}. Specifically, does an efficient differentially private learner imply an efficient online learner? In this paper we resolve this open question in the context of pure differential privacy. We derive an efficient black-box reduction from differentially private learning to online learning from expert advice

The Virtues of Frugality - Why cosmological observers should release their data slowly

Cosmologists will soon be in a unique position. Observational noise will gradually be replaced by cosmic variance as the dominant source of uncertainty in an increasing number of observations. We reflect on the ramifications for the discovery and verification of new models. If there are features in the full data set that call for a new model, there will be no subsequent observations to test that model's predictions. We give specific examples of the problem by discussing the pitfalls of model discovery by prior adjustment in the context of dark energy models and inflationary theories. We show how the gradual release of data can mitigate this difficulty, allowing anomalies to be identified, and new models to be proposed and tested. We advocate that observers plan for the frugal release of data from future cosmic variance limited observations.Comment: 5 pages, expanded discussion of Lambda and of blind anlysis, added refs. Matches version to appear in MNRAS Letter

A Systematic Review And Meta-Analysis Of Motivational Interviewing Training Effectiveness Among Students-In-Training

Effectively using motivational interviewing (MI) in practice can be difficult. However, there are a number of studies that examine training students across helping professions with the goal of facilitating students use MI more effectively. Although there is no standardized training manual, students often learn specific MI skills (e.g., open-ended questions, reflections) and knowledge (e.g., MI spirit) in hopes that they will apply those techniques to encounters with clients. The purpose of this systematic review and meta-analysis was to quantify the effectiveness of teaching students motivational interviewing. In total, 15 randomized and non-randomized studies met inclusion criteria and were examined in the current review of 8 dependent variables. A large and significant aggregated Hedges’ g of 0.90 (95% CI [0.45, 1.35]) was found. However, large heterogeneity was observed in all but one of the dependent variables. Moderation analyses revealed no significant moderating effects for risk of bias or type of comparison group; however, training length was a significant moderator. Limitations of the current meta-analysis include the small sample size and lack of consistency among training duration, measurement, and data collection and resulting heterogeneity. Future research appears warranted to further assess student MI training effectiveness, especially using more rigorous and standardized procedures, as well as determining enduring effects of the training