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pro vyhledávání: '"Larocque, Denis"'
A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. In this work, we study the robustness of such methods along two axes. First, sensor failures and GPS occlusions create missing-data challenges and we show tha
Externí odkaz:
http://arxiv.org/abs/2306.01925
Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine. We propose a new method called Covaria
Externí odkaz:
http://arxiv.org/abs/2209.08173
Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection or after any modification to the road network, traffic distribution, or behavioral constraints experienced dur
Externí odkaz:
http://arxiv.org/abs/2208.00659
Like many predictive models, random forests provide point predictions for new observations. Besides the point prediction, it is important to quantify the uncertainty in the prediction. Prediction intervals provide information about the reliability of
Externí odkaz:
http://arxiv.org/abs/2106.08217
Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available. In this paper, we investigate several different easy-to-implement ways that random forests
Externí odkaz:
http://arxiv.org/abs/2103.01355
Autor:
Alakus, Cansu, Larocque, Denis, Jacquemont, Sebastien, Barlaam, Fanny, Martin, Charles-Olivier, Agbogba, Kristian, Lippe, Sarah, Labbe, Aurelie
Investigating the relationships between two sets of variables helps to understand their interactions and can be done with canonical correlation analysis (CCA). However, the correlation between the two sets can sometimes depend on a third set of covar
Externí odkaz:
http://arxiv.org/abs/2011.11555
Survival data with time-varying covariates are common in practice. If relevant, they can improve on the estimation of survival function. However, the traditional survival forests - conditional inference forest, relative risk forest and random surviva
Externí odkaz:
http://arxiv.org/abs/2006.00567
Publikováno v:
Annals of Applied Statistics (2023)
Recent statistical methods fitted on large-scale GPS data can provide accurate estimations of the expected travel time between two points. However, little is known about the distribution of travel time, which is key to decision-making across a number
Externí odkaz:
http://arxiv.org/abs/2004.11292
Scaling adaptive traffic-signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning attempts to address this challenge by distributing control to specialized agents. However, specialization hinders
Externí odkaz:
http://arxiv.org/abs/2003.05738
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