×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.
Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

Stochastic differential equations for limiting description of UCB rule for Gaussian multi-armed bandits. (arXiv:2112.06423v3 [cs.LG] UPDATED)

Click here to flash read.

We consider the upper confidence bound strategy for Gaussian multi-armed
bandits with known control horizon sizes $N$ and build its limiting description
with a system of stochastic differential equations and ordinary differential
equations. Rewards for the arms are assumed to have unknown expected values and
known variances. A set of Monte-Carlo simulations was performed for the case of
close distributions of rewards, when mean rewards differ by the magnitude of
order $N^{-1/2}$, as it yields the highest normalized regret, to verify the
validity of the obtained description. The minimal size of the control horizon
when the normalized regret is not noticeably larger than maximum possible was
estimated.

Click here to read this post out
ID: 126319; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: May 14, 2023, 7:32 a.m. Changes:
/u/anonymous
Dictionaries:
Words:
Spaces:
Views: 8
CC:
No creative common's license
Comments: