Get our free extension to see links to code for papers anywhere online!Free add-on: code for papers everywhere!Free add-on: See code for papers anywhere!

Add to Chrome

Add to Firefox

Add to Edge

Syama Rangapuram

Solving Recurrent MIPs with Semi-supervised Graph Neural Networks

Feb 20, 2023

Konstantinos Benidis, Ugo Rosolia, Syama Rangapuram, George Iosifidis, Georgios Paschos

Figure 1 for Solving Recurrent MIPs with Semi-supervised Graph Neural Networks

Figure 2 for Solving Recurrent MIPs with Semi-supervised Graph Neural Networks

Figure 3 for Solving Recurrent MIPs with Semi-supervised Graph Neural Networks

Figure 4 for Solving Recurrent MIPs with Semi-supervised Graph Neural Networks

We propose an ML-based model that automates and expedites the solution of MIPs by predicting the values of variables. Our approach is motivated by the observation that many problem instances share salient features and solution structures since they differ only in few (time-varying) parameters. Examples include transportation and routing problems where decisions need to be re-optimized whenever commodity volumes or link costs change. Our method is the first to exploit the sequential nature of the instances being solved periodically, and can be trained with ``unlabeled'' instances, when exact solutions are unavailable, in a semi-supervised setting. Also, we provide a principled way of transforming the probabilistic predictions into integral solutions. Using a battery of experiments with representative binary MIPs, we show the gains of our model over other ML-based optimization approaches.

* 23 pages

Via

Access Paper or Ask Questions

GluonTS: Probabilistic Time Series Models in Python

Jun 14, 2019

Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang

Figure 1 for GluonTS: Probabilistic Time Series Models in Python

Figure 2 for GluonTS: Probabilistic Time Series Models in Python

Figure 3 for GluonTS: Probabilistic Time Series Models in Python

Figure 4 for GluonTS: Probabilistic Time Series Models in Python

We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and experimentation with time series models for common tasks such as forecasting or anomaly detection. It provides all necessary components and tools that scientists need for quickly building new models, for efficiently running and analyzing experiments and for evaluating model accuracy.

* ICML Time Series Workshop 2019

Via

Access Paper or Ask Questions

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale

Sep 22, 2017

Matthias Seeger, Syama Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert

Figure 1 for Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale

Figure 2 for Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale

Figure 3 for Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale

Figure 4 for Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale

We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced to linear-time Kalman smoothing, which allows us to operate on several orders of magnitude larger problems than previous related work. In a study on large real-world sales datasets, our method outperforms competing approaches on fast and medium moving items.

Via

Access Paper or Ask Questions