arXiv (All)
open access
ProCerDCL: Query-Efficient Black-Box Probabilistic Verification of Deep Learning Compiler Optimization
We present \tool, a query-efficient framework for black-box probabilistic verification. Given a fixed, sampleable input distribution and a binary property oracle, \tool assesses the property's violation probability $p$ against a threshold $θ$. Under independent sampling and stateless oracle behavior
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- AdaProb: Efficient Machine Unlearning via Adaptive Probability#128319
- CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction#141559
- Leveraging Code Embeddings to Identify and Address Blind Spots in Benchmark Creation#438627
- Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion#1342601
- Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion#1318905
Record · ID 1467823
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive