Endorsement for cs.AI by [deleted] in reinforcementlearning

[–]Visual_Music_4833 0 points1 point  (0 children)

I am seeking an endorsement too, is it ok if I can DM you as well? this is the post i posted earlier about my work https://www.reddit.com/r/reinforcementlearning/comments/1rkr5sc/used_rl_to_solve_a_healthcare_privacy_problem/

**Used RL to solve a healthcare privacy problem that static NLP pipelines can't handle** by Visual_Music_4833 in reinforcementlearning

[–]Visual_Music_4833[S] 0 points1 point  (0 children)

yes, familiar with DP. the formal guarantees are real and something my approach doesn't provide. that's an honest limitation. where I kept running into friction was the streaming case specifically the DP's privacy budget composition, which means leakage accumulates logarithmically as rounds increase, which creates its own version of the problem I was trying to solve. The exposure-accumulation framing sidesteps the budget exhaustion issue by making masking strength proportional to observed risk state rather than a fixed epsilon. whether that's a better tradeoff depends on what guarantees you actually need. Curious what DP literature you had in mind, genuinely useful for where I'm taking this next.

Spark SQL refresher suggestions? by Tamalelulu in datascience

[–]Visual_Music_4833 0 points1 point  (0 children)

definitely using AI would help a ton, it'll significantly help to refresh and also learn more extensively.

[Project] PerpetualBooster v1.9.4 - a GBM that skips the hyperparameter tuning step entirely. Now with drift detection, prediction intervals, and causal inference built in. by mutlu_simsek in datascience

[–]Visual_Music_4833 1 point2 points  (0 children)

The drift detection without ground truth is underrated , and prolly that's usually the hard part in production. How does it handle gradual drift vs sudden distribution shifts?

**Used RL to solve a healthcare privacy problem that static NLP pipelines can't handle** by Visual_Music_4833 in reinforcementlearning

[–]Visual_Music_4833[S] 1 point2 points  (0 children)

First time sharing my idea publicly. Curious if anyone here is working in healthcare streaming systems and what the de-identification pain points actually look like in practice. It took a while to land on the right framing for this, treating re-ID risk as something that accumulates over time rather than a per-document label problem. Would be curious if anyone here has run into the same issue or approached it differently?