AggressivequestionA committeemember islate to the AexamBandits withcostlyrewardobservationsYahoodatasetThere is a slidewhere youunderstandabsolutelynothingGraphdown andto therightAV/ZoomissuesPlot comparingperformance oftheir algorithm toanotheralgorithmSpotifyReinforcementlearningQuadrantdiagramRegretboundMangaindexSomeone isat least 5-10minutes lateOptimizationproblem 2“Is thatboundtight?”NetflixDirectmethodOptimizationproblem 1“The answerto thatquestion ison the nextslide”Graph upand tothe rightInversepropensityscoring/weighting“Dataefficiency”RewardmodelMVALIntimidatingequationInverseReinforcementlearningAggressivequestionA committeemember islate to the AexamBandits withcostlyrewardobservationsYahoodatasetThere is a slidewhere youunderstandabsolutelynothingGraphdown andto therightAV/ZoomissuesPlot comparingperformance oftheir algorithm toanotheralgorithmSpotifyReinforcementlearningQuadrantdiagramRegretboundMangaindexSomeone isat least 5-10minutes lateOptimizationproblem 2“Is thatboundtight?”NetflixDirectmethodOptimizationproblem 1“The answerto thatquestion ison the nextslide”Graph upand tothe rightInversepropensityscoring/weighting“Dataefficiency”RewardmodelMVALIntimidatingequationInverseReinforcementlearning

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