IntimidatingequationKillianmakesa jokeAV/ZoomissuesSomeoneis at least5 minuteslateOptimizationproblem 1A committeemember islate to the AexamInversepropensityscoring/weighting“The answerto thatquestion ison the nextslide”Plot comparingperformance oftheir algorithm toanotheralgorithmQuadrantdiagramDirectmethodThere is a slidewhere youunderstandabsolutelynothingInverseReinforcementlearningRewardmodelSongsOptimizationproblem 2Bandits withcostlyrewardobservationsRegretboundTargetpolicyYahoodatasetGraphdown andto theright“Dataefficiency”MVALAggressivequestionGraph upand tothe rightBalancedestimatorReinforcementlearningIntimidatingequationKillianmakesa jokeAV/ZoomissuesSomeoneis at least5 minuteslateOptimizationproblem 1A committeemember islate to the AexamInversepropensityscoring/weighting“The answerto thatquestion ison the nextslide”Plot comparingperformance oftheir algorithm toanotheralgorithmQuadrantdiagramDirectmethodThere is a slidewhere youunderstandabsolutelynothingInverseReinforcementlearningRewardmodelSongsOptimizationproblem 2Bandits withcostlyrewardobservationsRegretboundTargetpolicyYahoodatasetGraphdown andto theright“Dataefficiency”MVALAggressivequestionGraph upand tothe rightBalancedestimatorReinforcementlearning

Aaron A exam bingo - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. Intimidating equation
  2. Killian makes a joke
  3. AV/Zoom issues
  4. Someone is at least 5 minutes late
  5. Optimization problem 1
  6. A committee member is late to the A exam
  7. Inverse propensity scoring/weighting
  8. “The answer to that question is on the next slide”
  9. Plot comparing performance of their algorithm to another algorithm
  10. Quadrant diagram
  11. Direct method
  12. There is a slide where you understand absolutely nothing
  13. Inverse Reinforcement learning
  14. Reward model
  15. Songs
  16. Optimization problem 2
  17. Bandits with costly reward observations
  18. Regret bound
  19. Target policy
  20. Yahoo dataset
  21. Graph down and to the right
  22. “Data efficiency”
  23. MVAL
  24. Aggressive question
  25. Graph up and to the right
  26. Balanced estimator
  27. Reinforcement learning