4 citations · 12 across the 6 of their papers we have counts for
8 papers
Evaluating Dynamical Fidelity through Predictive Structure in Physical Representations
Oskar Bohn Lassen, Joao Paulo de Souza Boger, Simon Driscoll +4
Machine-learning models for physical systems are currently evaluated primarily through errors between predicted and reference states and, increasingly, through tests of physical co…
Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM
Oskar Bohn Lassen, Serio Angelo Maria Agriesti, Filipe Rodrigues +2
Climate policy analysis requires models that capture multi-gas climate effects, but such models are too slow to embed in reinforcement learning loops at scale. In collaboration wit…
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…
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…
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…
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
Carolin Schmidt, Daniele Gammelli, Francisco Camara Pereira +1
Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel reques…
Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand
Frederik Boe Hüttel, Filipe Rodrigues, Francisco Câmara Pereira
Electric vehicle charging demand models, with charging records as input, will inherently be biased toward the supply of available chargers. These models often fail to account for d…