Business Analytics google
Business analytics (BA) refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods. …

Deep Layer Aggregation google
Convolutional networks have had great success in image classification and other areas of computer vision. Recent efforts have designed deeper or wider networks to improve performance; as convolutional blocks are usually stacked together, blocks at different depths represent information at different scales. Recent models have explored `skip’ connections to aggregate information across layers, but heretofore such skip connections have themselves been `shallow’, projecting to a single fusion node. In this paper, we investigate new deep-across-layer architectures to aggregate the information from multiple layers. We propose novel iterative and hierarchical structures for deep layer aggregation. The former can produce deep high resolution representations from a network whose final layers have low resolution, while the latter can effectively combine scale information from all blocks. Results show that the our proposed architectures can make use of network parameters and features more efficiently without dictating convolution module structure. We also show transfer of the learned networks to semantic segmentation tasks and achieve better results than alternative networks with baseline training settings. …

Differentially Private Regression for Discrete-Time Survival Analysis google
In survival analysis, regression models are used to understand the effects of explanatory variables (e.g., age, sex, weight, etc.) to the survival probability. However, for sensitive survival data such as medical data, there are serious concerns about the privacy of individuals in the data set when medical data is used to fit the regression models. The closest work addressing such privacy concerns is the work on Cox regression which linearly projects the original data to a lower dimensional space. However, the weakness of this approach is that there is no formal privacy guarantee for such projection. In this work, we aim to propose solutions for the regression problem in survival analysis with the protection of differential privacy which is a golden standard of privacy protection in data privacy research. To this end, we extend the Output Perturbation and Objective Perturbation approaches which are originally proposed to protect differential privacy for the Empirical Risk Minimization (ERM) problems. In addition, we also propose a novel sampling approach based on the Markov Chain Monte Carlo (MCMC) method to practically guarantee differential privacy with better accuracy. We show that our proposed approaches achieve good accuracy as compared to the non-private results while guaranteeing differential privacy for individuals in the private data set. …

Advertisements