PyDCML is a Python library for fast implementation and scalable inference of Bayesian Discrete Choice Models that makes it easy to leverage flexible state-of-the-art modelling techniques from Machi…

Diffusion-aware Censored Demand Models combine a Tobit likelihood with a graph diffusion process in order to model the latent process of transfer of unsatisfied demand between similar products or s…

Baselines and benchmarks for spatio-temporal forecasting problems in transportation.

Amortized-MXL: Scaling Bayesian Inference of Mixed Multinomial Logit Models to Very Large Datasets

A neural network layer that enables training of deep neural networks directly from crowdsourced labels (e.g. from Amazon Mechanical Turk) or, more generally, labels from multiple annotators with di…

CAREL is open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment.

Deep joint mean and quantile regression for spatio-temporal problems

A Bayesian ARD approach for automatically determining an optimal utility function specification in Discrete Choice Models (DCMs) from an exponentially large set of possible specifications in a pure…

A deep learning approach for combining time-series and textual data for taxi demand prediction in event areas

Logistic Regression from Crowds

Multi-Annotator Supervised LDA for regression

Multi-Annotator Supervised LDA for classification

Text Preprocessing in Python