Inversepropensityscoring/weightingOptimizationproblem 1Optimizationproblem 2There is a slidewhere youunderstandabsolutelynothingRewardmodelIntimidatingequationA committeemember islate to the AexamMVAL“The answerto thatquestion ison the nextslide”AggressivequestionReinforcementlearningKillianmakesa jokeYahoodatasetQuadrantdiagramBandits withcostlyrewardobservationsTargetpolicySomeoneis at least5 minuteslateDirectmethod“Dataefficiency”AV/ZoomissuesGraphdown andto therightPlot comparingperformance oftheir algorithm toanotheralgorithmBalancedestimatorRegretboundGraph upand tothe rightSongsInverseReinforcementlearningInversepropensityscoring/weightingOptimizationproblem 1Optimizationproblem 2There is a slidewhere youunderstandabsolutelynothingRewardmodelIntimidatingequationA committeemember islate to the AexamMVAL“The answerto thatquestion ison the nextslide”AggressivequestionReinforcementlearningKillianmakesa jokeYahoodatasetQuadrantdiagramBandits withcostlyrewardobservationsTargetpolicySomeoneis at least5 minuteslateDirectmethod“Dataefficiency”AV/ZoomissuesGraphdown andto therightPlot comparingperformance oftheir algorithm toanotheralgorithmBalancedestimatorRegretboundGraph upand tothe rightSongsInverseReinforcementlearning

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