NeurIPS-2019 Workshop on

Safety and Robustness in Decision Making

Workshop Summary

Interacting with increasingly sophisticated decision-making systems is becoming more and more a part of our daily life. This creates an immense responsibility for designers of these systems to build them in a way to guarantee safe interaction with their users and good performance, in the presence of noise and changes in the environment, and/or of model misspecification and uncertainty. Any progress in this area will be a huge step forward in using decision-making algorithms in emerging high stakes applications, such as autonomous driving, robotics, power systems, health care, recommendation systems, and finance.

This workshop aims to bring together researchers from academia and industry in order to discuss main challenges, describe recent advances, and highlight future research directions pertaining to develop safe and robust decision-making systems. We aim to highlight new and emerging theoretical and applied research opportunities for the community that arise from the evolving needs for decision-making systems and algorithms that guarantee safe interaction and good performance under a wide range of uncertainties in the environment.

The research challenges we are interested in addressing in this workshop include (but are not limited to):

  • Counterfactual reasoning and off-policy evaluation.
  • Specifying appropriate definitions of safety and robustness, and quantifying/certifying them under model uncertainty.
  • Safe exploration.
  • Trading off robustness/safety with performance.
  • Understanding the connections between risk and robustness, and other related definitions.

Call for Papers


    • Paper Submission Deadline: September 22, 2019
    • Notification of Acceptance: September 30, 2019
    • Workshop: either Friday December 13 or Saturday December 14, 2019


Papers submitted to the workshop should be between 4 to 8 pages long, excluding references and appendix, and in NeurIPS 2019 format (NOT ANONYMIZED). Accepted papers will be presented as posters or contributed oral presentations.

Submissions should be sent as a pdf file by email to

Invited Speakers

Finale Doshi-Velez

Off-policy Evaluation and Counterfactual Inference

(Harvard University)

Dimitar Filev

Autonomous Driving

(Henry Ford Technical Fellow - Ford Motor Company)

Thorsten Joachims

Off-policy Evaluation and Counterfactual Inference

(Cornell University)

Daniel Kuhn

Operations Research


Scott Niekum


(University of Texas at Austin)

Marco Pavone

Autonomous Driving

(Stanford University)


Yinlam Chow

(Google Research)

Mohammad Ghavamzadeh

(Facebook AI Research)

Shie Mannor


Marek Petrik

(University of New Hampshire)

Yisong Yue