| --- |
| license: other |
| license_name: metadata-compilation |
| license_details: >- |
| Bibliographic metadata compiled from DBLP (CC0), Semantic Scholar (ODC-BY), |
| and OpenReview. PDFs in pdfs/ retain their original publisher/author |
| copyrights and are stored here for personal research use only — do not make |
| this repository public with the PDFs included. |
| language: |
| - en |
| task_categories: |
| - text-classification |
| tags: |
| - machine-unlearning |
| - literature-survey |
| - bibliography |
| pretty_name: Machine Unlearning Papers at Top-Tier Venues (2016-2026) |
| size_categories: |
| - n<1K |
| --- |
| |
| # 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. |
|
|
