Someone isat least 5-10minutes lateMangaindexInversepropensityscoring/weightingMVALNetflixAggressivequestionGraphdown andto therightPlot comparingperformance oftheir algorithm toanotheralgorithmIntimidatingequation“Dataefficiency”DirectmethodSpotifyQuadrantdiagram“The answerto thatquestion ison the nextslide”InverseReinforcementlearningA committeemember islate to the Aexam“Is thatboundtight?”YahoodatasetAV/ZoomissuesReinforcementlearningBandits withcostlyrewardobservationsGraph upand tothe rightOptimizationproblem 2There is a slidewhere youunderstandabsolutelynothingRegretboundRewardmodelOptimizationproblem 1Someone isat least 5-10minutes lateMangaindexInversepropensityscoring/weightingMVALNetflixAggressivequestionGraphdown andto therightPlot comparingperformance oftheir algorithm toanotheralgorithmIntimidatingequation“Dataefficiency”DirectmethodSpotifyQuadrantdiagram“The answerto thatquestion ison the nextslide”InverseReinforcementlearningA committeemember islate to the Aexam“Is thatboundtight?”YahoodatasetAV/ZoomissuesReinforcementlearningBandits withcostlyrewardobservationsGraph upand tothe rightOptimizationproblem 2There is a slidewhere youunderstandabsolutelynothingRegretboundRewardmodelOptimizationproblem 1

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