会议信息

UAI 2026: Conference on Uncertainty in Artificial Intelligence

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UAI
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搜索与榜单列表完全无需凭证；本页的完整详情需要一把免费 API 密钥。详见开发者接入页。
截稿日期:
2026-02-25
通知日期:
2026-06-01
会议日期:
2026-08-17
会议地点:
Amsterdam, the Netherlands
届数:
42
CCF: B   ICORE: A   QUALIS: A1   浏览: 287819   关注: 159   参加: 17

会伴指数 (CP-I)

89.8 / 100
全站第 68 名 / 共 5,683 个会议 · 前 2%

人工智能与机器学习 第 7 / 740

学术认可 (35%)
100
投稿选择性 (20%)
79
会议传承 (20%)
100
社区关注 (10%)
71
资料公开度 (15%)
80

用到的输入: 收录等级：CCF B, ICORE A, QUALIS A1 · 录用率：29.5%（有记录的 5 届的均值） · 有据可查的届次：42 · 在会伴关注它的研究者：159 人 · 过去 24 个月打开过本页的研究者：17 人

公开资料里还缺: 历届信息 (+3.0)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-22

征稿

UAI 2026 (Conference on Uncertainty in Artificial Intelligence) is a CCF B / ICORE A / QUALIS A1 conference held in Amsterdam, the Netherlands on 2026-08-17. The paper submission deadline is 2026-02-25. Acceptance notifications are sent on 2026-06-01.

Subject Areas Below you find a non-exhaustive list of relevant topics for your reference. Algorithms Approximate Inference Bayesian Methods Belief Propagation Exact Inference Kernel Methods Missing Data Handling Monte Carlo Methods Optimization - Combinatorial Optimization - Convex Optimization - Discrete Optimization - Non-Convex Probabilistic Programming Randomized Algorithms Spectral Methods Variational Methods Applications Cognitive Science Computational Biology Computer Vision Crowdsourcing Earth System Science Education Forensic Science Healthcare Natural Language Processing Neuroscience Planning and Control Privacy and Security Robotics Social Good Sustainability and Climate Science Text and Web Data Learning Active Learning Adversarial Learning Causal Learning Classification Clustering Compressed Sensing and Dictionary Learning Deep Learning Density Estimation Dimensionality Reduction Ensemble Learning Feature Selection Hashing and Encoding Multitask and Transfer Learning Online and Anytime Learning Policy Optimization and Policy Learning Ranking Reinforcement Learning and Bandits Relational Learning Representation Learning Semi-Supervised Learning Structure Learning Structured Prediction Unsupervised Learning Models Foundation Models Generative Models Graphical Models Models for Relational Data Neural Networks Probabilistic Circuits Regression Models Spatial, Temporal and Spatio-Temporal Models Topic Models and Latent Variable Models Principles Causality Computational and Statistical Trade-Offs Explainability Fairness Privacy Reliability Robustness (Structured) Sparsity Representation Constraints Dempster-Shafer (Description) Logics Imprecise Probabilities Influence Diagrams Knowledge Representation Languages Theory Computational Complexity Control Theory Decision Theory Game Theory Information Theory Learning Theory Probability Theory Statistical Theory
由 Dou Sun 最后更新于

录用率

平均录用率: 30.3% 23 年间 (1999–2025).

年份提交数录用数录用率(%)
202575023030.7%
202474420026.9%
202377824331.2%
202271223032.3%
202177720526.4%
202051514227.6%
201945011826.2%
201833710430.9%
20172828730.9%
20162758530.9%
20152919934%
20142929432.2%
20112859633.7%
20102608833.8%
20092437631.3%
20082567228.1%
20062136831.9%
20052438635.4%
20042532610.3%
20032292510.9%
20021926634.4%
2000843035.7%
19991507751.3%

最佳论文

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相关期刊

CCF全称影响因子出版商ISSN
BSoftware & Systems Modeling3.2Springer1619-1366
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BInformation Sciences6.0Elsevier0020-0255
BComputer Networks4.7Elsevier1389-1286
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BIEEE Transactions on Intelligent Transportation Systems8.4IEEE1524-9050
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