DirectmethodTargetpolicyYahoodatasetAggressivequestionQuadrantdiagramKillianmakesa jokeAV/ZoomissuesOptimizationproblem 1InverseReinforcementlearning“The answerto thatquestion ison the nextslide”Graphdown andto therightIntimidatingequationBalancedestimatorPlot comparingperformance oftheir algorithm toanotheralgorithmOptimizationproblem 2There is a slidewhere youunderstandabsolutelynothingRewardmodelMVALGraph upand tothe rightSomeoneis at least5 minuteslateBandits withcostlyrewardobservationsReinforcementlearningRegretboundSongs“Dataefficiency”Inversepropensityscoring/weightingA committeemember islate to the AexamDirectmethodTargetpolicyYahoodatasetAggressivequestionQuadrantdiagramKillianmakesa jokeAV/ZoomissuesOptimizationproblem 1InverseReinforcementlearning“The answerto thatquestion ison the nextslide”Graphdown andto therightIntimidatingequationBalancedestimatorPlot comparingperformance oftheir algorithm toanotheralgorithmOptimizationproblem 2There is a slidewhere youunderstandabsolutelynothingRewardmodelMVALGraph upand tothe rightSomeoneis at least5 minuteslateBandits withcostlyrewardobservationsReinforcementlearningRegretboundSongs“Dataefficiency”Inversepropensityscoring/weightingA committeemember islate to the Aexam

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