“The answerto thatquestion ison the nextslide”Optimizationproblem 2AV/ZoomissuesGraphdown andto therightRewardmodelMVALInverseReinforcementlearningSomeone isat least 5-10minutes lateIntimidatingequationQuadrantdiagramYahoodataset“Dataefficiency”A committeemember islate to the AexamGraph upand tothe rightOptimizationproblem 1Plot comparingperformance oftheir algorithm toanotheralgorithmReinforcementlearningBandits withcostlyrewardobservationsRegretboundMangaindexSpotifyNetflixAggressivequestionInversepropensityscoring/weightingThere is a slidewhere youunderstandabsolutelynothingDirectmethod“Is thatboundtight?”“The answerto thatquestion ison the nextslide”Optimizationproblem 2AV/ZoomissuesGraphdown andto therightRewardmodelMVALInverseReinforcementlearningSomeone isat least 5-10minutes lateIntimidatingequationQuadrantdiagramYahoodataset“Dataefficiency”A committeemember islate to the AexamGraph upand tothe rightOptimizationproblem 1Plot comparingperformance oftheir algorithm toanotheralgorithmReinforcementlearningBandits withcostlyrewardobservationsRegretboundMangaindexSpotifyNetflixAggressivequestionInversepropensityscoring/weightingThere is a slidewhere youunderstandabsolutelynothingDirectmethod“Is thatboundtight?”

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