Report NEP-BIG-2019-04-29

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé issued this report. It is usually issued weekly.

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Other reports in NEP-BIG

The following items were announced in this report:

• Ali Habibnia & Esfandiar Maasoumi, 2019. "Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Papers 1904.11145, arXiv.org.
• Shuaiqiang Liu & Anastasia Borovykh & Lech A. Grzelak & Cornelis W. Oosterlee, 2019. "A neural network-based framework for financial model calibration," Papers 1904.10523, arXiv.org.
• Haoran Wang & Xun Yu Zhou, 2019. "Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework," Papers 1904.11392, arXiv.org, revised May 2019.
• Rogelio A. Mancisidor & Michael Kampffmeyer & Kjersti Aas & Robert Jenssen, 2019. "Deep Generative Models for Reject Inference in Credit Scoring," Papers 1904.11376, arXiv.org, revised Sep 2021.
• Philippe Casgrain & Brian Ning & Sebastian Jaimungal, 2019. "Deep Q-Learning for Nash Equilibria: Nash-DQN," Papers 1904.10554, arXiv.org, revised Oct 2022.
• Luo, Ji & Wu, Guoyuan, 2018. "Developing an Interactive Machine-Learning-based Approach for Sidewalk Digitalization," Institute of Transportation Studies, Working Paper Series qt6ht5185q, Institute of Transportation Studies, UC Davis.
• Hao, Peng & Wang, Chao, 2018. "Evaluating Environmental Impact of Traffic Congestion in Real Time Based on Sparse Mobile Crowd-sourced Data," Institute of Transportation Studies, Working Paper Series qt7q6760rz, Institute of Transportation Studies, UC Davis.
• Shaheen, Susan PhD & Martin, Elliot PhD & Hoffman-Stapleton, Mikaela & Slowik, Peter, 2018. "Understanding How cities can link smart mobility priorities through data," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7303t6sw, Institute of Transportation Studies, UC Berkeley.