vitamin 10

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University of California, Berkeley *

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Course

C102

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Computer Science

Date

Apr 29, 2024

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pdf

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11

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4/27/24, 7:24 PM View Submission | Gradescope https://www.gradescope.com/courses/711377/assignments/4324125/submissions/244847232 1/11
Q1 1 Point Calculate the Hoeffding bound for a variable which is the sample mean of 10 random variables which are each bounded between 3 and 8. In other words, compute a bound for the following probability where is the expected value of . Z P ( Z μ t ) μ Z 10 10 E [ X ] i 25 2 1 t >=0 min e 10 t E [ e ] t ( Z μ ) exp ( 5 4 t 2 ) 4/27/24, 7:24 PM View Submission | Gradescope https://www.gradescope.com/courses/711377/assignments/4324125/submissions/244847232 2/11
Q2 Bandits 2 Points Let be the set of arms in the setting of the bandits problem. For each arm , let the random variable be the random reward received for pulling arm at time and let be the expected reward from pulling arm . Finally, let the random variable be the arm pulled at time (so is the reward received at time ). All expectations (e.g., , ) are taken over the randomness in the payouts as well as the arm pulled at each timestep. Q2.1 Regret 1 Point Select all of the definitions which are true. A a A X i a a i μ = a E[ X ] i a a A i i X i A i i E[ X ] i a E[ X ] i A i X i a A i The regret is . R ( t ) X X i =1 t ( a A max i a i A i ) The regret is . R ( t ) μ X i =1 t ( a A max a i A i ) The regret is . R ( t ) X − E[ X ] i =1 t ( a A max i a i A i ) The regret is . R ( t ) μ − E[ X ] i =1 t ( a A max a i A i ) The regret is R ( t ) t μ a A max a X i =1 t i A i The regret is R ( t ) t μ a A max a E[ X ] i =1 t i A i 4/27/24, 7:24 PM View Submission | Gradescope https://www.gradescope.com/courses/711377/assignments/4324125/submissions/244847232 3/11
Q2.2 Expected Regret and Pseudo-Regret 1 Point Select all of the definitions which are true. 4/27/24, 7:24 PM View Submission | Gradescope https://www.gradescope.com/courses/711377/assignments/4324125/submissions/244847232 4/11
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