probability-statistics / Probability

Bombari's Question on Sign-Quantized Linear Maps

A dimension-independent subgaussian concentration bound for Gaussian vectors under coordinate-wise nonlinear maps, valid for any bounded function under a well-conditioned covariance, which answers a question of Simone Bombari on sign quantization.

10Significance / 100
1Frontier events
0Verification tasks
0Recorded attempts

Temporal state

Current frontier

No reconciled state yet.

Append-only history

Frontier timeline

probability-statisticsMay 26, 2026Significance 10/100Registry: unreviewed

Bombari's Question on Sign-Quantized Linear Maps

Prior state unknownproved

A dimension-independent subgaussian concentration bound for Gaussian vectors under coordinate-wise nonlinear maps, valid for any bounded function under a well-conditioned covariance, which answers a question of Simone Bombari on sign quantization.

SourceReplayReproducedFormal proofStatement auditExternal checkExpert reviewPeer review

Research memory

Claims and attempts

Scoped claims

Source authenticated

A dimension-independent subgaussian concentration bound for Gaussian vectors under coordinate-wise nonlinear maps, valid for any bounded function under a well-conditioned covariance, which answers a question of Simone Bombari on sign quantization.

Recorded attempts

Evidence graph

Connected research record

No public relationships recorded yet.

Bombari's Question on Sign-Quantized Linear Maps — Mathematical Frontier Network