18 citations · 37 across the 5 of their papers we have counts for
5 papers · 1 filter
Bayesian Active Learning for Censored Regression
Frederik Boe Hüttel, Christoffer Riis, Filipe Rodrigues +1
Bayesian active learning is based on information theoretical approaches that focus on maximising the information that new observations provide to the model parameters. This is comm…
Deep Evidential Learning for Bayesian Quantile Regression
Frederik Boe Hüttel, Filipe Rodrigues, Francisco Câmara Pereira
It is desirable to have accurate uncertainty estimation from a single deterministic forward-pass model, as traditional methods for uncertainty quantification are computationally ex…
Bayesian Active Learning with Fully Bayesian Gaussian Processes
Christoffer Riis, Francisco Antunes, Frederik Boe Hüttel +2
The bias-variance trade-off is a well-known problem in machine learning that only gets more pronounced the less available data there is. In active learning, where labeled data is s…
Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand
Frederik Boe Hüttel, Inon Peled, Filipe Rodrigues +1
Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this…
Modeling Censored Mobility Demand through Quantile Regression Neural Networks
Frederik Boe Hüttel, Inon Peled, Filipe Rodrigues +1
Shared mobility services require accurate demand models for effective service planning. On the one hand, modeling the full probability distribution of demand is advantageous becaus…