会议信息

AISTATS 2027: International Conference on Artificial Intelligence and Statistics

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截稿日期:
2026-09-29 已截稿
通知日期:
2027-01-20
会议日期:
2027-05-03
会议地点:
Montreal, Quebec, Canada
届数:
CCF: C   ICORE: A   浏览: 135531   关注: 195   参加: 43
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会伴指数 (CP-I)

87.9 / 100
全站第 100 名 / 共 5,759 个会议 · 前 2%

数学与自然科学 第 1 / 71 人工智能与机器学习 第 12 / 751

学术认可 (35%)
88
投稿选择性 (20%)
79
会议传承 (20%)
92
社区关注 (10%)
79
资料公开度 (15%)
100

用到的输入: 收录等级：CCF C, ICORE A · 录用率：29.5%（有记录的 5 届的均值） · 有据可查的届次：30 · 在会伴关注它的研究者：195 人 · 过去 24 个月打开过本页的研究者：39 人

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 1.2 · 算于 2026-10-22

征稿

AISTATS 2027 (International Conference on Artificial Intelligence and Statistics) is a CCF C / ICORE A conference held in Montreal, Quebec, Canada on 2027-05-03. The paper submission deadline is 2026-09-29. Acceptance notifications are sent on 2027-01-20.

We invite submissions to the 30th International Conference on Artificial Intelligence and Statistics (AISTATS 2027) and welcome paper submissions at the intersection of artificial intelligence, machine learning, statistics, and related areas. Accepted papers will be presented at the conference to be held in person in Montreal, CANADA, on May 3–6, 2027. At least one author of each accepted paper should intend to register and present the work at the conference. Exceptions may be granted in case of travel emergencies or visa issues. AISTATS is an interdisciplinary gathering of researchers from computer science, artificial intelligence, machine learning, statistics, and related areas. Since its inception in 1985, the primary goal of AISTATS has been to broaden research in these fields by promoting the exchange of ideas among them. The conference is committed to diversity in all its forms and encourages submissions from authors of underrepresented groups and geographies in ML/AI. What Is New in 2027 — Please Read Before Submitting AISTATS 2027 introduces several changes to its submission and reviewing policies. They are summarized here and detailed in the corresponding sections below. Use of AI for research and writing is allowed, but must be disclosed. We recognize that research is now often carried out in hybrid human–AI workflows, from proof sketching to code generation and editing. The use of generative AI tools is therefore not banned. However, (i) every submission must include a mandatory AI Use Statement following the ICLR format (see Use of Generative AI by Authors); (ii) authors bear full responsibility for all content; and (iii) submissions whose writing is unclear, unfocused, or padded — as is typical of insufficiently edited AI-generated text — are candidates for rejection or desk rejection. AI review of all submissions. Every submission will receive an AI-generated review targeting factual correctness. This AI review will not be visible to human reviewers while they write their reviews. It will be released to authors at the start of the rebuttal period, and made available to reviewers and area chairs during the discussion phase (see AI Review of Submissions). More details will follow. Submission quotas. No author may appear on more than 12 submissions. Authors who do not have an accepted paper at ICLR, ICML, NeurIPS, or AISTATS may appear on at most 3 submissions (see Submission Quotas). Authorship is frozen at abstract submission. No author may be added (or removed) after the abstract submission deadline, under any circumstances (see Changes of Title/Abstract/Authorship). Confidential reporting (whistleblower) form. Anyone can confidentially report suspected violations of our policies (see Confidential Reporting). Citation style. Initial submissions may use either author–year or numeric citations, provided that the chosen style is used consistently throughout the paper. This flexibility is intended to avoid discouraging authors from citing relevant work because of space constraints. Accepted papers must use author–year citations in the camera-ready version to ensure a consistent and reader-friendly style across the published proceedings. Paper Submission (Proceedings Track) The proceedings track is the standard AISTATS paper submission track. As in 2026, there will be a separate journal track for papers that have been recently published at select top journals to present those works at AISTATS as a poster; the details of this track will be posted separately. Papers will be selected for publication via a rigorous double-blind peer-review process. Acceptance rates tend to be around 25%. All accepted papers will be presented at the Conference as posters, with a subset being presented as talks, and will be published in the AISTATS Conference Proceedings, as part of the Journal of Machine Learning Research Workshop and Conference Proceedings series, by the publisher Proceedings of Machine Learning Research (PMLR). Papers for talks and posters are treated equally in publication. Solicited topics include, but are not limited to: Machine learning methods and algorithms (classification, regression, unsupervised and semi-supervised learning, clustering, logic programming, …) Probabilistic methods (Bayesian methods, approximate inference, density estimation, tractable probabilistic models, probabilistic programming, …) Theory of machine learning and statistics (optimization, computational learning theory, decision theory, online learning and bandits, game theory, frequentist statistics, information theory, …) Deep learning (theory, architectures, generative models, optimization for neural networks, …) Reinforcement learning (theory of RL, offline/online RL, deep RL, multi-agent RL, …) Ethical and trustworthy machine learning (causality, fairness, interpretability, privacy, robustness, safety, …) Applications of machine learning and statistics (including natural language, signal processing, computer vision, physical sciences, social sciences, sustainability and climate, healthcare, …) Submissions are accepted through OpenReview https://openreview.net/group?id=aistats.org/AISTATS/2027/Conference. The site will start accepting submissions on September 15, 2026. All authors must have an up-to-date OpenReview profile (including current affiliation, publication record, and DBLP link where applicable) at the time of abstract submission; this information is used for conflict-of-interest management, reviewer qualification, and submission quotas. Incorrect profile information is grounds for desk rejection.
由 Admin Agent 最后更新于

录用率

平均录用率: 30.9% 11 年间 (2014–2025).

年份提交数录用数录用率(%)
2025186158331.3%
2024198054627.6%
2023168649629.4%
2022168549329.3%
2021152745529.8%
2019111136032.4%
201864521433.2%
201753016831.7%
201653716530.7%
201544212728.7%
201433512035.8%

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Statistics and Computing1.6Springer0960-3174
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Spatial Statistics2.5Elsevier2211-6753
CKnowledge-Based Systems7.2Elsevier0950-7051
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
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CIEEE Transactions on Industrial Informatics11.7IEEE1551-3203
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