Rubric

Find Rubric
Keep in mind that 29 students have already been assessed using this rubric. Changing it will affect their evaluations.
midterm
midterm
Criteria Ratings Pts
Problem 1, question 1
Step 1: Derive the log-likelihood formula exactly.
threshold: pts
5 to >4.0 pts
Full Marks
Derive the log-likelihood formula exactly.
4 to >0.0 pts
partial
Some errors on the log-likelihood formula, such as, writing log(y) instead of \sum_{i=1}^n \log(y_i)
0 pts
No Marks
- Left blank. - Or, show not enough arguments to get points.
pts
5 pts
--
Problem 1, question 1
Step 2: Take the derivative of log-likelihood and set it to 0.
threshold: pts
5 to >4.0 pts
Full Marks
Take the derivative of log-likelihood and set it to 0.
4 to >0.0 pts
formula of derivative/final theta not exact
- The formula of the derivative is not exact - Or, forget factor 1/n in the final answer
0 pts
No Marks
- Left blank, - Or, the wrong formula for the derivative of log-likelihood,
pts
5 pts
--
Problem 1, question 2
Step 1: Using Box-Muller, sample 2 standard normal distributed X_1, X_2.
threshold: pts
2 to >0.0 pts
Full Marks
- Mention Box-Muller, - Or, other methods, such as Inverse Transform Sampling, Rejection Sampling
0 pts
No Marks
- Left blank. - Or, show not enough arguments to get points.
pts
2 pts
--
Problem 1, question 2
Step 2: Set Y = exp(\Theta + X_1)
threshold: pts
3 to >0.0 pts
Full Marks
- Set Y = exp(\Theta + X_1) (for box-muller), - Or equivalent results for other methods.
0 pts
No Marks
- Provide no details about how to apply a general sampling method to this special case, for example, provide no specific transformation in Inverse Transform method.
pts
3 pts
--
Problem 1, question 2
Step 3: Prove Y has pdf f(y), and show complexity is O(N).
threshold: pts
5 to >4.0 pts
Full Marks
Prove Y has pdf f(y), and show complexity is O(N).
4 to >0.0 pts
Forget to show complexity
- Forget to show the complexity, - Or errors in calculation.
0 pts
No Marks
No proof that the method along with the formula proposed at step 2 sample exactly the given pdf f(y).
pts
5 pts
--
Problem 2
Step 1: Compute E[Z_i]
threshold: pts
5 to >0.0 pts
Full Marks
Compute E[Z_i]
0 pts
No Marks
- Provide no calculation to the expectation of Z_i.
pts
5 pts
--
Problem 2
Step2: Set X_i = aZ_i + b, where a = 2, b = -1/2.
threshold: pts
5 to >4.0 pts
Full Marks
Set X_i = aZ_i + b, where a = 2, b = -1/2.
4 to >0.0 pts
Minor error
- Get wrong a,b, - Or other errors in calculation.
0 pts
No Marks
- Show no steps to find a,b,
pts
5 pts
--
Problem 2
Step3: Using Hoeffding inequality to get c.
threshold: pts
10 to >5.0 pts
Full Marks
- Using Hoeffding inequality to get c correctly, - Or show a right track but not correct results.
5 to >0.0 pts
Partial
- Not be able to show how to apply Hoeffding to get c, - - Or show a right track but not enough results.
0 pts
No Marks
- Left blank, - Or show no mention of Hoeffding inequality or other methods to find c.
pts
10 pts
--
Problem 3, question 1
Step 1: Show log(p(y, f, f_S)/(p(f|f_S)q(f_S))) = logp(y|f) + log p(f_S)/q(f_S)
threshold: pts
5 to >4.0 pts
Full Marks
Show log(p(y, f, f_S)/(p(f|f_S)q(f_S))) = logp(y|f) + log p(f_S)/q(f_S).
4 to >0.0 pts
Minor errors
- errors in calculation.
0 pts
No Marks
- Left blank, - Or show no steps of how to get log(p(y|f)) from the given formula.
pts
5 pts
--
Problem 3, question 1
Step 2: Show final ELBO
threshold: pts
5 to >4.0 pts
Full Marks
Show final ELBO.
4 to >0.0 pts
Minor error
- errors in calculation, so the final result might be not correct.
0 pts
No Marks
- Left blank. - Or, show not enough arguments to get points.
pts
5 pts
--
Problem 3, question 2
Step 1: Show E_{p(f|f_S)}[log p(y|f)] = const + E_{p(f|f_S)}[-1/(2\sigma^2)||y-f||^2]
threshold: pts
4 to >3.0 pts
Full Marks
Show E_{p(f|f_S)}[log p(y|f)] = const + E_{p(f|f_S)}[-1/(2\sigma^2)||y-f||^2]
3 to >0.0 pts
Miror errors
errors in the calculation so the final result might be not correct.
0 pts
No Marks
- Left blank. - Or, show not enough arguments to get points.
pts
4 pts
--
Problem 3, question 2
Step 2: Show the final closed form.
threshold: pts
6 to >4.0 pts
Full Marks
Show E_{p(f|f_S)}[-1/(2\sigma^2)||y-f||^2] = const x (||y-\mu_f||^2 + tr(\Sigma_f)), which is a closed form.
4 to >0.0 pts
Right track but some errors
Errors in calculation so the final result might be not correct.
0 pts
No Marks
- Left blank. - Or, show not enough arguments to get points.
pts
6 pts
--