Información de la conferencia

AISTATS 2027: International Conference on Artificial Intelligence and Statistics

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Día de Entrega:
2026-09-29 Cerrado
Fecha de Notificación:
2027-01-20
Fecha de conferencia:
2027-05-03
Ubicación:
Montreal, Quebec, Canada
Ediciones:
CCF: C   ICORE: A   Vistas: 136096   Seguidores: 195   Asistentes: 43
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Índice Conference Partner (CP-I)

87,9 / 100
Puesto n.º 102 de 5.761 congresos · 2% superior

N.º 1 de 71 en Matemáticas y ciencias físicas N.º 13 de 752 en Inteligencia artificial y aprendizaje automático

Reconocimiento académico (35%)
88
Selectividad en la revisión (20%)
79
Ediciones celebradas (20%)
92
Atención de la comunidad (10%)
79
Integridad del registro público (15%)
100

Datos utilizados: Categorías: CCF C, ICORE A · Tasa de aceptación: 29.5% (media de 5 ediciones registradas) · Ediciones documentadas: 30 · Investigadores que lo siguen aquí: 195 · Investigadores que abrieron esta página en los últimos 24 meses: 39

Confianza 100 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.2 · Calculado el 2026-10-28

Solicitud de Artículos

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.
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Tasa de aceptación

Tasa de aceptación media: 30.9% en 11 años (2014–2025).

AñoEnviadosAceptadosAceptados(%)
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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