activity
20232025
most citedDeep Evidential Learning for Bayesian Quantile Regression

4 citations · 8 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM

Oskar Bohn Lassen, Serio Angelo Maria Agriesti, Filipe Rodrigues +1

Climate policy studies require models that capture the combined effects of multiple greenhouse gases on global temperature, but these models are computationally expensive and diffi…

cs.LG20241 cited

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…

cs.LG20234 cited

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…

stat.AP2023

Analyzing the Reporting Error of Public Transport Trips in the Danish National Travel Survey Using Smart Card Data

Georges Sfeir, Filipe Rodrigues, Maya Abou Zeid +1

Household travel surveys have been used for decades to collect individuals and households' travel behavior. However, self-reported surveys are subject to recall bias, as respondent…

cs.LG2023

Railway Network Delay Evolution: A Heterogeneous Graph Neural Network Approach

Zhongcan Li, Ping Huang, Chao Wen +1

Railway operations involve different types of entities (stations, trains, etc.), making the existing graph/network models with homogenous nodes (i.e., the same kind of nodes) incap…