山南水北

Title:  Instance-based Credit Risk Assessment for Investment Decisions in P2P Lending Author(s):  Yanhong Guo, Wenjun Zhou, Chunyu Luo, Chuanren Liu, Hui Xiong Journal: European Journal of Operation Research Year: 2015 URL:  https://www.sciencedirect.com/science/article/abs/pii/S0377221715004610 Abstract Objective:  Effective allocation of personal investors' money across different loans by accurately assessing the credit risk of each loan. Key contributions:  Guo et al. proposed a data-driven investment decision-making framework for the P2P market. They designed an instance-based credit risk assessment model, which has the ability to evaluate the return and risk of each individual loan. Given the estimate of return and risk, they formulated the investment decision in P2P lending as a portfolio optimization problem with boundary constraints. Data Description 2016 loan samples from Lending Club 4128 loan samples from Prosper Features include the borrower's credi...

Specialization Introduction This specialization covers five selected grounding papers in online optimization. In each blog, I will discuss one paper, where I aim to include Brief introduction and summary to the paper Key takeaways of the paper Notice that all the discussion and summary in this specialization are based on the reviewed papers. None of the algorithms or theorems is proposed by myself.  Summary Paper Detail Title:  Projection-free Online Learning Author(s):  Elad Hazan, Satyen Kale URL:  https://icml.cc/2012/papers/292.pdf Abstract The computational bottleneck in applying online learning to massive data sets is usually the projection step. We present efficient online learning algorithms that eschew projections in favor of much more efficient linear optimization steps using the Frank-Wolfe technique. We obtain a range of regret bounds for online convex optimization, with better bounds for specific cases such as stochastic online smooth convex optimization...