Backend






Bandits with Probing: Optimal Coverage and the Role of Feedback
arXiv stat.ML0 views
arXiv:2609.15248v2 Announce Type: replace-cross Abstract: In bandits with probing, a learner selects at most $k$ of $n$ arms per round, earns the largest of their rewards, and competes with the best fixed single arm. For independent arms with i.i.d. rewards in $[0,1]$ and $2\le kSource:https://arxiv.org/abs/2609.15248
PreviousVerdict Instability of OOD Scores under Reference ResamplingNextWe intercepted PostgreSQL’s COMMIT completion and replayed the batch
Related posts

Copilot Fusion Takes Local Models, and the Offline Boundary Is Somewhere Else
0dev.to AI

A CTO Checklist for Evaluating AI Features Before Production Release
0dev.to AI
Humanoid Robot Demo Makes the Case for Keeping the Human Form
0dev.to AI
Supabase anon key vs service role key: what each one opens
0dev.to Security
After the eMudhra CA compromise: audit your own TLS stack in 30 minutes
0dev.to Security
Exposed Building Automation Services: Bavaria and DACH
0dev.to Security
show = false, 2600)"
x-show="show" x-cloak x-transition.opacity.duration.200ms
class="fixed bottom-8 left-1/2 z-[80] -translate-x-1/2 rounded-lg border border-line bg-card px-4 py-2.5 text-sm text-heading shadow-card-hover"
role="status" aria-live="polite">