@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{YLM23,
  title={Scalable Probabilistic Routes},
  author={Yang, Suwei and Liang, Victor and Meel, Kuldeep S.},
  abstract={Inference and prediction of routes have become of interest over the past 
  decade owing to a dramatic increase in package delivery and ride-sharing services. 
  Given the underlying combinatorial structure and the incorporation of probabilities,
  route prediction involves techniques from both formal methods and machine learning. 
  One promising approach for predicting routes is using decision diagrams that are 
  augmented with probability values. However, the effectiveness of this approach depends 
  on the size of the compiled decision diagrams. The scalability of the approach is limited 
  owing to its empirical runtime and space complexity. In this work, our contributions are 
  two-fold: first, we introduce a relaxed encoding that uses a linear number of variables 
  with respect to the number of vertices in a road network graph to significantly reduce the 
  size of resultant decision diagrams. Secondly, instead of a stepwise sampling procedure, 
  we propose a single pass sampling-based route prediction. In our evaluations arising from 
  a real-world road network, we demonstrate that the resulting system achieves around twice 
  the quality of suggested routes while being an order of magnitude faster compared to state-of-the-art.},
  year={2023},
  month=jun,
  booktitle=LPAR,
  bib2html_dl_pdf={../Papers/lpar23-ylm.pdf},
  bib2html_pubtype={Refereed Conference},
  bib2html_rescat={Sampling},
}