RegretboundGraph upand tothe rightDirectmethod“The answerto thatquestion ison the nextslide”Plot comparingperformance oftheir algorithm toanotheralgorithmSpotifySomeone isat least 5-10minutes lateThere is a slidewhere youunderstandabsolutelynothingYahoodatasetInverseReinforcementlearningGraphdown andto therightInversepropensityscoring/weightingMVALAV/Zoomissues“Is thatboundtight?”Bandits withcostlyrewardobservationsOptimizationproblem 2QuadrantdiagramAggressivequestionRewardmodel“Dataefficiency”ReinforcementlearningIntimidatingequationOptimizationproblem 1NetflixMangaindexA committeemember islate to the AexamRegretboundGraph upand tothe rightDirectmethod“The answerto thatquestion ison the nextslide”Plot comparingperformance oftheir algorithm toanotheralgorithmSpotifySomeone isat least 5-10minutes lateThere is a slidewhere youunderstandabsolutelynothingYahoodatasetInverseReinforcementlearningGraphdown andto therightInversepropensityscoring/weightingMVALAV/Zoomissues“Is thatboundtight?”Bandits withcostlyrewardobservationsOptimizationproblem 2QuadrantdiagramAggressivequestionRewardmodel“Dataefficiency”ReinforcementlearningIntimidatingequationOptimizationproblem 1NetflixMangaindexA committeemember islate to the Aexam

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