Loading
Anchors' computational inefficiency limits its applicability. MAnchors, a memorization-based framework, accelerates Anchors while preserving explanations. This addresses a bottleneck in local model-agnostic explanation techniques, making them more deployable in real-world applications. The next battleground is whether accelerated explanations can be trusted in high-stakes decision-making.
“arXiv:2502.11068v3 Announce Type: replace-cross Abstract: Anchors is a popular local model-agnostic explanation technique whose applicability is limited by its computational inefficiency. To address this limitation, we propose a memorizatio…”
Read the source →STATUS
ACTIVE
CATEGORY
Models
EVIDENCE
Not yet assessed
ENTITY
MAnchors, Anchors, arXiv:2502.11068v3
DECISION
Automated · no editorial override
LAST OBSERVED
Jul 26, 2026