Someoneis at least5 minuteslateTargetpolicyInverseReinforcementlearningRegretboundGraphdown andto theright“The answerto thatquestion ison the nextslide”IntimidatingequationBalancedestimatorA committeemember islate to the AexamReinforcementlearningKillianmakesa jokeBandits withcostlyrewardobservationsInversepropensityscoring/weightingPlot comparingperformance oftheir algorithm toanotheralgorithmDirectmethodMVALRewardmodelAV/ZoomissuesOptimizationproblem 1Quadrantdiagram“Dataefficiency”YahoodatasetSongsThere is a slidewhere youunderstandabsolutelynothingOptimizationproblem 2Graph upand tothe rightAggressivequestionSomeoneis at least5 minuteslateTargetpolicyInverseReinforcementlearningRegretboundGraphdown andto theright“The answerto thatquestion ison the nextslide”IntimidatingequationBalancedestimatorA committeemember islate to the AexamReinforcementlearningKillianmakesa jokeBandits withcostlyrewardobservationsInversepropensityscoring/weightingPlot comparingperformance oftheir algorithm toanotheralgorithmDirectmethodMVALRewardmodelAV/ZoomissuesOptimizationproblem 1Quadrantdiagram“Dataefficiency”YahoodatasetSongsThere is a slidewhere youunderstandabsolutelynothingOptimizationproblem 2Graph upand tothe rightAggressivequestion

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