AggressivequestionKillianmakesa jokeThere is a slidewhere youunderstandabsolutelynothingOptimizationproblem 2Graph upand tothe rightDirectmethodOptimizationproblem 1“The answerto thatquestion ison the nextslide”Graphdown andto therightReinforcementlearningYahoodatasetPlot comparingperformance oftheir algorithm toanotheralgorithmTargetpolicySomeoneis at least5 minuteslateIntimidatingequationAV/ZoomissuesMVALRewardmodelRegretboundA committeemember islate to the Aexam“Dataefficiency”QuadrantdiagramBandits withcostlyrewardobservationsBalancedestimatorInversepropensityscoring/weightingSongsInverseReinforcementlearningAggressivequestionKillianmakesa jokeThere is a slidewhere youunderstandabsolutelynothingOptimizationproblem 2Graph upand tothe rightDirectmethodOptimizationproblem 1“The answerto thatquestion ison the nextslide”Graphdown andto therightReinforcementlearningYahoodatasetPlot comparingperformance oftheir algorithm toanotheralgorithmTargetpolicySomeoneis at least5 minuteslateIntimidatingequationAV/ZoomissuesMVALRewardmodelRegretboundA committeemember islate to the Aexam“Dataefficiency”QuadrantdiagramBandits withcostlyrewardobservationsBalancedestimatorInversepropensityscoring/weightingSongsInverseReinforcementlearning

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