Someoneis at least5 minuteslateRewardmodelTargetpolicyOptimizationproblem 2Bandits withcostlyrewardobservationsAggressivequestionInverseReinforcementlearningSongsDirectmethodGraphdown andto theright“Dataefficiency”RegretboundA committeemember islate to the AexamBalancedestimatorMVALGraph upand tothe rightAV/ZoomissuesInversepropensityscoring/weightingKillianmakesa jokeOptimizationproblem 1“The answerto thatquestion ison the nextslide”ReinforcementlearningIntimidatingequationYahoodatasetQuadrantdiagramPlot comparingperformance oftheir algorithm toanotheralgorithmThere is a slidewhere youunderstandabsolutelynothingSomeoneis at least5 minuteslateRewardmodelTargetpolicyOptimizationproblem 2Bandits withcostlyrewardobservationsAggressivequestionInverseReinforcementlearningSongsDirectmethodGraphdown andto theright“Dataefficiency”RegretboundA committeemember islate to the AexamBalancedestimatorMVALGraph upand tothe rightAV/ZoomissuesInversepropensityscoring/weightingKillianmakesa jokeOptimizationproblem 1“The answerto thatquestion ison the nextslide”ReinforcementlearningIntimidatingequationYahoodatasetQuadrantdiagramPlot comparingperformance oftheir algorithm toanotheralgorithmThere is a slidewhere youunderstandabsolutelynothing

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