Graphdown andto therightYahoodatasetOptimizationproblem 2Inversepropensityscoring/weightingA committeemember islate to the AexamPlot comparingperformance oftheir algorithm toanotheralgorithmThere is a slidewhere youunderstandabsolutelynothingQuadrantdiagramAggressivequestionAV/ZoomissuesBalancedestimatorTargetpolicyReinforcementlearning“Dataefficiency”Optimizationproblem 1IntimidatingequationKillianmakesa jokeRegretbound“The answerto thatquestion ison the nextslide”InverseReinforcementlearningRewardmodelSongsMVALBandits withcostlyrewardobservationsSomeoneis at least5 minuteslateDirectmethodGraph upand tothe rightGraphdown andto therightYahoodatasetOptimizationproblem 2Inversepropensityscoring/weightingA committeemember islate to the AexamPlot comparingperformance oftheir algorithm toanotheralgorithmThere is a slidewhere youunderstandabsolutelynothingQuadrantdiagramAggressivequestionAV/ZoomissuesBalancedestimatorTargetpolicyReinforcementlearning“Dataefficiency”Optimizationproblem 1IntimidatingequationKillianmakesa jokeRegretbound“The answerto thatquestion ison the nextslide”InverseReinforcementlearningRewardmodelSongsMVALBandits withcostlyrewardobservationsSomeoneis at least5 minuteslateDirectmethodGraph upand tothe right

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