合成数据论文趋势 2023–2026

隐私保护合成

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 169 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

合成数据论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
202327515754
202411381133
2025879832
2026184141450

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

全部合成数据论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 46 篇，第 1 / 1 页

  1. ICML 2026$\texttt{FHAIM}$: Fully Homomorphic AIM for Private Tabular Synthetic Data Generation
  2. ICML 2026A Statistical Framework for Analyzing Specification Resistance to Learnware-Inversion Risks
  3. ICML 2026An Information-Theoretic Criterion for Efficient Data Synthesis
  4. ICML 2026Differentially Private Synthetic Data via APIs 4: Tabular Data
  5. ICML 2026FedUSD: Unbiased Synthetic Data for Federated Learning
  6. ICML 2026Geometry-Aware Tabular Diffusion
  7. ICML 2026Identifying Learnwares via Reduced Neural Conditional Mean Embedding
  8. ICML 2026Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization
  9. ICML 2026Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation
  10. ICML 2026PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
  11. ICML 2026R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?
  12. ICML 2026Relational In-Context Learning via Synthetic Pre-training with Structural Prior
  13. ICML 2026TexEditor: Structure-Preserving Text-Driven Texture Editing
  14. ICML 2026Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
  15. ICML 2025AutoEval Done Right: Using Synthetic Data for Model Evaluation
  16. ICML 2025How to Synthesize Text Data without Model Collapse?
  17. ICML 2025Idiosyncrasies in Large Language Models
  18. ICML 2025Measuring Diversity in Synthetic Datasets
  19. ICML 2025OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling
  20. ICML 2025Privacy Amplification Through Synthetic Data: Insights from Linear Regression
  21. ICML 2025Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs
  22. ICML 2025The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
  23. ICML 2025Unlocking Post-hoc Dataset Inference with Synthetic Data
  24. ICML 2024Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
  25. ICML 2024Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
  26. ICML 2024Differentially Private Sum-Product Networks
  27. ICML 2024Differentially Private Synthetic Data via Foundation Model APIs 2: Text
  28. ICML 2024Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors
  29. ICML 2024Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent
  30. ICML 2024Privacy-Preserving Embedding via Look-up Table Evaluation with Fully Homomorphic Encryption
  31. ICML 2024Privacy-Preserving Instructions for Aligning Large Language Models
  32. ICML 20232D-Shapley: A Framework for Fragmented Data Valuation
  33. ICML 2023A/B Testing in Network Data with Covariate-Adaptive Randomization
  34. ICML 2023Data Structures for Density Estimation
  35. ICML 2023Data-Derived Weak Universal Consistency
  36. ICML 2023Effectively Using Public Data in Privacy Preserving Machine Learning
  37. ICML 2023Generating Private Synthetic Data with Genetic Algorithms
  38. ICML 2023Gradient-Free Structured Pruning with Unlabeled Data
  39. ICML 2023Label differential privacy and private training data release
  40. ICML 2023On Generalizations of Some Distance Based Classifiers for HDLSS Data
  41. ICML 2023Provable Dynamic Fusion for Low-Quality Multimodal Data
  42. ICML 2023Synthetic data for model selection
  43. ICML 2023Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data
  44. ICML 2023Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
  45. ICML 2023The Value of Out-of-Distribution Data
  46. ICML 2023Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data

在 Claude、Cursor 里直接问这个趋势

接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。

可以直接复制这句话问 AI

用 paperlist 查合成数据在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？

AI 会调用的工具：get_topic_stats {"topic": "synthetic-data"}

两分钟接入 Paperlist MCP（Claude Code、Cursor、Codex、ChatGPT）

常见问题

2023–2026 年四大会议一共有多少篇合成数据论文？
共 169 篇，按年份分别是 2023 年 54 篇、2024 年 33 篇、2025 年 32 篇、2026 年 50 篇。
NeurIPS 2026 有多少篇合成数据论文？
NeurIPS 2026 有 18 篇合成数据论文，上一届是 8 篇。
ICLR 2026 有多少篇合成数据论文？
ICLR 2026 有 4 篇合成数据论文，上一届是 7 篇。
ICML 2026 有多少篇合成数据论文？
ICML 2026 有 14 篇合成数据论文，上一届是 9 篇。
CVPR 2026 有多少篇合成数据论文？
CVPR 2026 有 14 篇合成数据论文，上一届是 8 篇。
哪个会议的合成数据论文最多？
NeurIPS，2023–2026 年共 64 篇。

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