Datasets:
Machine Unlearning Papers at Top-Tier Venues (2016–2026)
Complete crawl of machine-unlearning papers from 9 top-tier venues: IEEE S&P, USENIX Security, ACM CCS, NDSS (security) and NeurIPS, ICML, ICLR, AAAI, IJCAI (AI/ML), years 2016–2026.
- 501 core papers (+ 14 adjacent-field papers flagged
needs_review) - 454 PDFs under
pdfs/(<year>_<venue>_<slug>.pdf) - Built 2026-08 with a multi-stage pipeline (DBLP + Semantic Scholar + OpenReview) followed by a multi-agent coverage audit against official proceedings; 539 false positives were removed (workshop tracks, student abstracts, ICMLA/ICMLC mismatches, continual-learning noise) and 67 audit-discovered missing papers were added after per-paper verification.
- 2026 is partial: NeurIPS 2026 and CCS 2026 had not taken place at crawl time.
Files
| file | description |
|---|---|
papers.jsonl / papers.csv |
final list; one row per paper |
meta/excluded.jsonl |
removed records with excluded_reason (for recovery/audit) |
meta/triage.json |
human/agent verdict overrides applied during merging |
meta/pdf_report.json, meta/pdf_missing_report.json |
PDF acquisition log; 47 core papers lack PDFs (OpenReview-only, no arXiv preprint) |
meta/wf_result.json |
raw multi-agent audit output |
pdfs/ |
collected PDFs (arXiv preprints or official open-access versions) |
Record fields
title, authors, year, venue, venue_key, field (security/ai),
status (core / needs_review), abstract, doi, arxiv_id,
pdf_url, pdf_candidates, url, citations (Semantic Scholar, 2026-08),
sources (dblp/s2/openreview/manual).
Scope
Core = exact/approximate unlearning, certified data removal,
federated/graph/recommendation unlearning, LLM knowledge unlearning, concept
erasure in generative models/representations, unlearning
evaluation/verification/auditing, attacks on unlearning, right-to-be-forgotten
deletion from trained models. needs_review = adjacent literatures that
remove training-data influence but position themselves elsewhere (backdoor
purification, model repair, deletion-robust optimization).
Excluded: catastrophic forgetting / continual learning, unlearnable examples (availability poisoning), logic-based forgetting (KR), database deletion.
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