14 citations · 14 across the 3 of their papers we have counts for
3 papers
DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting
Ozan Ozyegen, Juyoung Wang, Mucahit Cevik
Despite the high performance of neural network-based time series forecasting methods, the inherent challenge in explaining their predictions has limited their applicability in cert…
A Deep Reinforcement Learning Framework For Column Generation
Cheng Chi, Amine Mohamed Aboussalah, Elias B. Khalil +2
Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an extremely large number of variables (columns). CG is the workhorse for tackling large-sca…
On the Impact of Deep Learning-based Time-series Forecasts on Multistage Stochastic Programming Policies
Juyoung Wang, Mucahit Cevik, Merve Bodur
Multistage stochastic programming provides a modeling framework for sequential decision-making problems that involve uncertainty. One typically overlooked aspect of this methodolog…