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Creators/Authors contains: "Perrault, Andrew"

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  1. ; ; (, IEEE)
    Free, publicly-accessible full text available April 6, 2026
  2. ; ; ; ; (, epiDAMIK (ACM SIGKDD))
  3. ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; (, Conference on Uncertainty in Artificial Intelligence (UAI))
    Spatio-temporal incident prediction is a central issue in law enforcement, with applications in fighting crimes like poaching, human trafficking, illegal fishing, burglaries and smuggling. However, state of the art approaches fail to account for evasion in response to predictive models, a common form of which is spatial shift in incident occurrence. We present a general approach for incident forecasting that is robust to spatial shifts. We propose two techniques for solving the resulting robust optimization problem: first, a constraint generation method guaranteed to yield an optimal solution, and second, a more scalable gradientbased approach. We then apply these techniques to both discrete-time and continuoustime robust incident forecasting. We evaluate our algorithms on two different real-world datasets, demonstrating that our approach is significantly 
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    Accepted Manuscript Full Text Available