×
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

Click here to flash read.

We introduce a boosting algorithm to pre-process data for fairness. Starting
from an initial fair but inaccurate distribution, our approach shifts towards
better data fitting while still ensuring a minimal fairness guarantee. To do
so, it learns the sufficient statistics of an exponential family with
boosting-compliant convergence. Importantly, we are able to theoretically prove
that the learned distribution will have a representation rate and statistical
rate data fairness guarantee. Unlike recent optimization based pre-processing
methods, our approach can be easily adapted for continuous domain features.
Furthermore, when the weak learners are specified to be decision trees, the
sufficient statistics of the learned distribution can be examined to provide
clues on sources of (un)fairness. Empirical results are present to display the
quality of result on real-world data.

Click here to read this post out
ID: 336731; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: Aug. 16, 2023, 7:33 a.m. Changes:
Dictionaries:
Words:
Spaces:
Views: 14
CC:
No creative common's license
Comments: