no code implementations • 26 Jul 2022 • Christian Kurniawan, Xiyu Deng, Adhiraj Chakraborty, Assane Gueye, Niangjun Chen, Yorie Nakahira
Although many methods in regular finance can estimate credit scores and default probabilities, these methods are not directly applicable to microfinance due to the following unique characteristics: a) under-explored (developing) areas such as rural Africa do not have sufficient prior loan data for microfinance institutions (MFIs) to establish a credit scoring system; b) microfinance applicants may have difficulty providing sufficient information for MFIs to accurately predict default probabilities; and c) many MFIs use group liability (instead of collateral) to secure repayment.
no code implementations • 28 Mar 2018 • Niangjun Chen, Gautam Goel, Adam Wierman
We demonstrate the generality of the OBD framework by showing how, with different choices of "balance," OBD can improve upon state-of-the-art performance guarantees for both competitive ratio and regret, in particular, OBD is the first algorithm to achieve a dimension-free competitive ratio, $3 + O(1/\alpha)$, for locally polyhedral costs, where $\alpha$ measures the "steepness" of the costs.
no code implementations • 25 Apr 2015 • Niangjun Chen, Anish Agarwal, Adam Wierman, Siddharth Barman, Lachlan L. H. Andrew
Making use of predictions is a crucial, but under-explored, area of online algorithms.