Beta Regression
How should we one perform a regression analysis in which the dependent variable is restricted to the standard unit interval such as rates and proportions? Ferrari and Cribari-Neto, 2004 proposed a regression model for continuous variates that assume values in the standard unit interval, e.g., rates, proportions, or concentrations indices. The model is based on the assumption that the response is beta-distributed, they called their model the beta regression model. The regression parameters are interpretable in terms of the mean of y (the variable of interest) and the model is naturally heteroskedastic and easily accommodates asymmetries. A variant of the beta regression model that allows for nonlinearities and variable dispersion was proposed by Simas et al., 2010.
zoib: An R package for Bayesian Inference for Beta Regression and Zero/One Inflated Beta Regression
A Short Course in Beta Regression Models

Best Subsets
Best Subsets Regression is a method used to help determine which predictor (independent) variables should be included in a multiple regression model. This method involves examining all of the models created from all possible combination of predictor variables. Best Subsets Regression uses R2 to check for the best model. It would not be fun or fast to compute this method without the use of a statistical software program. First, all models that have only one predictor variable included are checked and the two models with the highest R2 are selected. Then all models that have only two predictor variables included are checked and the two models with the highest R2 are chosen, again. This process continues until all combinations of all predictors variables have been taken into account.
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms

AttriGuard
Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user’s private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes). Existing defenses leverage game theory or heuristics based on correlations between the public data and attributes. These defenses are not practical. Specifically, game-theoretic defenses require solving intractable optimization problems, while correlation-based defenses incur large utility loss of users’ public data. In this paper, we present AttriGuard, a practical defense against attribute inference attacks. AttriGuard is computationally tractable and has small utility loss. Our AttriGuard works in two phases. Suppose we aim to protect a user’s private attribute. In Phase I, for each value of the attribute, we find a minimum noise such that if we add the noise to the user’s public data, then the attacker’s classifier is very likely to infer the attribute value for the user. We find the minimum noise via adapting existing evasion attacks in adversarial machine learning. In Phase II, we sample one attribute value according to a certain probability distribution and add the corresponding noise found in Phase I to the user’s public data. We formulate finding the probability distribution as solving a constrained convex optimization problem. We extensively evaluate AttriGuard and compare it with existing methods using a real-world dataset. Our results show that AttriGuard substantially outperforms existing methods. Our work is the first one that shows evasion attacks can be used as defensive techniques for privacy protection. …

Hierarchical Block Sparse Neural Network (HBsNN)
Sparse deep neural networks(DNNs) are efficient in both memory and compute when compared to dense DNNs. But due to irregularity in computation of sparse DNNs, their efficiencies are much lower than that of dense DNNs on general purpose hardwares. This leads to poor/no performance benefits for sparse DNNs. Performance issue for sparse DNNs can be alleviated by bringing structure to the sparsity and leveraging it for improving runtime efficiency. But such structural constraints often lead to sparse models with suboptimal accuracies. In this work, we jointly address both accuracy and performance of sparse DNNs using our proposed class of neural networks called HBsNN ( Hierarchical Block Sparse Neural Networks). …