16 documents matched the search for the 2019-10-21 issue of the NEP report on Computational Economics (nep-cmp), currently edited by Stan Miles.
Go to document
|
111
Residual Switching Network for Portfolio Optimization,
Jifei Wang and Lingjing Wang,
from arXiv.org
(2019)
Branch-Price-and-Cut for the Soft-Clustered Capacitated Arc-Routing Problem,
Stefan Irnich, Timo Hintsch and Lone Kiilerich,
from Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz
(2019)
Keywords: Arc routing, branch-price-and-cut, branch-and-cut, districting
Principled estimation of regression discontinuity designs,
L. Jason Anastasopoulos,
from arXiv.org
(2020)
Predicting Auction Price of Vehicle License Plate with Deep Residual Learning,
Vinci Chow,
from arXiv.org
(2019)
Incorporating Fine-grained Events in Stock Movement Prediction,
Deli Chen, Yanyan Zou, Keiko Harimoto, Ruihan Bao, Xuancheng Ren and Xu Sun,
from arXiv.org
(2019)
Stock price formation: useful insights from a multi-agent reinforcement learning model,
J. Lussange, Sacha Bourgeois-Gironde, S. Palminteri and B. Gutkin,
from arXiv.org
(2019)
Nowcasting and forecasting US recessions: Evidence from the Super Learner,
Benedikt Maas,
from University Library of Munich, Germany
(2019)
Keywords: Machine Learning; Nowcasting; Forecasting; Business cycle analysis
Prediksi Pendapatan Terbesar pada Penjualan Produk Cat dengan Menggunakan Metode Monte Carlo,
Bias Yulisa Geni, Julius Santony and Sumijan Sumijan,
from University Library of Munich, Germany
(2019)
Keywords: Modeling and Simulation, Monte Carlo, Revenue Prediction, Paint Products, Building Stores
The Paradox of Big Data,
Gary Smith,
from Economics Department, Pomona College
(2019)
Keywords: data mining, big data, machine learning
Weighted Monte Carlo with least squares and randomized extended Kaczmarz for option pricing,
Damir Filipovi\'c, Kathrin Glau, Yuji Nakatsukasa and Francesco Statti,
from arXiv.org
(2019)
Does South African Affirmative Action Policy Reduce Poverty? A CGE Analysis,
Hélène Maisonnave, Bernard Decaluwé and Margaret Chitiga,
from HAL
(2019)
Variation and adaptation: learning from success in patient safety-oriented simulation training,
Peter Dieckmann, Mary Patterson, Saadi Lahlou, Jessica Mesman, Patrik Nyström and Ralf Krage,
from London School of Economics and Political Science, LSE Library
(2017)
Smart hedging against carbon leakage,
Christoph Böhringer, Knut Einar Rosendahl and Halvor Briseid Storrøsten,
from Norwegian University of Life Sciences, School of Economics and Business
(2019)
Keywords: Carbon leakage; output-based allocation; consumption tax
Predicting Consumer Default: A Deep Learning Approach,
Stefania Albanesi and Domonkos Vamossy,
from Human Capital and Economic Opportunity Working Group
(2019)
Keywords: consumer default, credit scores, deep learning, macroprudential policy
Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning,
Jérôme Bolte and Edouard Pauwels,
from Toulouse School of Economics (TSE)
(2019)
Keywords: Deep Learning, Automatic differentiation, Backpropagation algorithm,; Nonsmooth stochastic optimization, Defiable sets, o-minimal structures, Stochastic gradient, Clarke subdifferential, First order methods
An Inertial Newton Algorithm for Deep Learning,
Jérôme Bolte, Camille Castera, Edouard Pauwels and Cédric Févotte,
from Toulouse School of Economics (TSE)
(2019)
|