“The answerto thatquestion ison the nextslide”YahoodatasetSomeoneis at least5 minuteslateOptimizationproblem 2Bandits withcostlyrewardobservationsDirectmethodSongsPlot comparingperformance oftheir algorithm toanotheralgorithmGraph upand tothe rightQuadrantdiagramOptimizationproblem 1TargetpolicyKillianmakesa jokeAggressivequestionA committeemember islate to the Aexam“Dataefficiency”Inversepropensityscoring/weightingIntimidatingequationThere is a slidewhere youunderstandabsolutelynothingGraphdown andto therightInverseReinforcementlearningAV/ZoomissuesRewardmodelRegretboundReinforcementlearningBalancedestimatorMVAL“The answerto thatquestion ison the nextslide”YahoodatasetSomeoneis at least5 minuteslateOptimizationproblem 2Bandits withcostlyrewardobservationsDirectmethodSongsPlot comparingperformance oftheir algorithm toanotheralgorithmGraph upand tothe rightQuadrantdiagramOptimizationproblem 1TargetpolicyKillianmakesa jokeAggressivequestionA committeemember islate to the Aexam“Dataefficiency”Inversepropensityscoring/weightingIntimidatingequationThere is a slidewhere youunderstandabsolutelynothingGraphdown andto therightInverseReinforcementlearningAV/ZoomissuesRewardmodelRegretboundReinforcementlearningBalancedestimatorMVAL

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