What is recorded here?
Reuses batched conjugate-gradient work to estimate solves, trace derivatives and log determinants using kernel matrix-matrix multiplication, paired with preconditioning and a PyTorch framework.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Finite iterative solves, stochastic probes and partial Lanczos quadrature retain numerical/sampling error; a low-rank preconditioner is not necessarily a low-rank approximation to the GP model. One solver call still contains many iterations, not one matrix multiply. Gardner/Pleiss equal contributors; CG/Lanczos/trace-estimation foundations inherited.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Jacob R. Gardner; Geoff Pleiss; David Bindel; Kilian Q. Weinberger; Andrew Gordon Wilson
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
GPyTorch black-box matrix-matrix Gaussian-process inference
Reuses batched conjugate-gradient work to estimate solves, trace derivatives and log determinants using kernel matrix-matrix multiplication, paired with preconditioning and a PyTorch framework.
Scientific scope
Finite iterative solves, stochastic probes and partial Lanczos quadrature retain numerical/sampling error; a low-rank preconditioner is not necessarily a low-rank approximation to the GP model. One solver call still contains many iterations, not one matrix multiply. Gardner/Pleiss equal contributors; CG/Lanczos/trace-estimation foundations inherited.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Jacob R. Gardner; Geoff Pleiss; David Bindel; Kilian Q. Weinberger; Andrew Gordon Wilson
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.