SongsBandits withcostlyrewardobservationsReinforcementlearningQuadrantdiagramYahoodatasetGraph upand tothe rightOptimizationproblem 2Killianmakesa jokeAggressivequestionOptimizationproblem 1RegretboundIntimidatingequationInversepropensityscoring/weightingA committeemember islate to the AexamTargetpolicyMVAL“The answerto thatquestion ison the nextslide”AV/ZoomissuesInverseReinforcementlearningRewardmodelPlot comparingperformance oftheir algorithm toanotheralgorithm“Dataefficiency”Graphdown andto therightThere is a slidewhere youunderstandabsolutelynothingBalancedestimatorDirectmethodSomeoneis at least5 minuteslateSongsBandits withcostlyrewardobservationsReinforcementlearningQuadrantdiagramYahoodatasetGraph upand tothe rightOptimizationproblem 2Killianmakesa jokeAggressivequestionOptimizationproblem 1RegretboundIntimidatingequationInversepropensityscoring/weightingA committeemember islate to the AexamTargetpolicyMVAL“The answerto thatquestion ison the nextslide”AV/ZoomissuesInverseReinforcementlearningRewardmodelPlot comparingperformance oftheir algorithm toanotheralgorithm“Dataefficiency”Graphdown andto therightThere is a slidewhere youunderstandabsolutelynothingBalancedestimatorDirectmethodSomeoneis at least5 minuteslate

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