人工智能 | CCF推荐国际会议信息3条

Posted 2021-04-26 Call4Papers

人工智能

ACT 2021

International Conference on Algorithmic Learning Theory

全文截稿: 2020-09-30

开会时间: 2021-03-16

会议难度: ★★★

CCF分类: C类

会议地点: Paris, France

网址：http://algorithmiclearningtheory.org/alt2021/

The Algorithmic Learning Theory (ALT) 2021 conference will be held in Paris, France on March 16–19, 2021. The conference is dedicated to all theoretical and algorithmic aspects of machine learning. We invite submissions with contributions to new or existing learning problems including, but not limited to:

Design and analysis of learning algorithms.
Statistical and computational learning theory.
Online learning algorithms and theory.
Optimization methods for learning.
Unsupervised, semi-supervised and active learning.
Interactive learning, planning and control, and reinforcement learning.
Privacy-preserving data analysis.
Learning with additional societal considerations: e.g., fairness, economics.
Robustness of learning algorithms to adversarial agents.
Artificial neural networks, including deep learning.
High-dimensional and non-parametric statistics.
Adaptive data analysis and selective inference.
Learning with algebraic or combinatorial structure.
Bayesian methods in learning.
Learning in distributed and streaming settings.
Game theory and learning.
Learning from complex data: e.g., networks, time series.
Theoretical analysis of probabilistic graphical models.

人工智能

ALT 2021

International Conference on Algorithmic Learning Theory

全文截稿: 2020-09-30

开会时间: 2021-03-16

会议难度: ★★★

CCF分类: C类

会议地点: Paris, France

网址：http://algorithmiclearningtheory.org/alt2021/

The Algorithmic Learning Theory (ALT) 2021 conference will be held in Paris, France on March 16–19, 2021. The conference is dedicated to all theoretical and algorithmic aspects of machine learning. We invite submissions with contributions to new or existing learning problems including, but not limited to:

Design and analysis of learning algorithms.
Statistical and computational learning theory.
Online learning algorithms and theory.
Optimization methods for learning.
Unsupervised, semi-supervised and active learning.
Interactive learning, planning and control, and reinforcement learning.
Privacy-preserving data analysis.
Learning with additional societal considerations: e.g., fairness, economics.
Robustness of learning algorithms to adversarial agents.
Artificial neural networks, including deep learning.
High-dimensional and non-parametric statistics.
Adaptive data analysis and selective inference.
Learning with algebraic or combinatorial structure.
Bayesian methods in learning.
Learning in distributed and streaming settings.
Game theory and learning.
Learning from complex data: e.g., networks, time series.
Theoretical analysis of probabilistic graphical models.

人工智能

AISTATS 2021

International Conference on Artificial Intelligence and Statistics

摘要截稿: 2020-10-08

全文截稿: 2020-10-15

开会时间: 2021-04-13

会议难度: ★★★

CCF分类: C类

会议地点: San Diego, California, USA

网址：http://www.aistats.org/aistats2021/

Paper Submission:
Proceedings track: This is the standard AISTATS paper submission track. Papers will be selected via a rigorous double-blind peer-review process. All accepted papers will be presented at the Conference as contributed talks or as posters and will be published in the Proceedings.

Solicited topics include, but are not limited to:

Models and estimation: graphical models, causality, Gaussian processes, approximate inference, kernel methods, nonparametric models, statistical and computational learning theory, manifolds and embedding, sparsity and compressed sensing, ...

Classification, regression, density estimation, unsupervised and semi-supervised learning, clustering, topic models, ...

Structured prediction, relational learning, logic and probability

Reinforcement learning, planning, control

Game theory, no-regret learning, multi-agent systems

Algorithms and architectures for high-performance computation in AI and statistics

Software for and applications of AI and statistics

Deep learning including optimization, generalization and architectures

Trustworthy learning, including learning with privacy and fairness, interpretability, and robustness

Formatting and Supplementary Material
Submissions are limited to 8 pages excluding references using the LaTeX style file we provide below. The number of pages containing citations alone is not limited. You can also submit a single file of additional supplementary material which may be either a pdf file (such as proof details) or a zip file for other formats/more files (such as code or videos). Note that reviewers are under no obligation to examine your supplementary material. If you have only one supplementary pdf file, please upload it as is; otherwise gather everything to the single zip file.

Submissions will be through CMT ( https://cmt3.research.microsoft.com/AISTATS2021/) and will be open a month before the abstract submission deadline.

Formatting information (including LaTeX style files) will be made available. We do not support submission in preparation systems other than LaTeX. Please do not modify the layout given by the style file. If you have questions about the style file or its usage, please contact the publications chair.

Anonymization Requirements
The AISTATS review process is double-blind. Please remove all identifying information from your submission, including author names, affiliations, and any acknowledgments. Self-citations can present a special problem: we recommend leaving in a moderate number of self-citations for published or otherwise well-known work. For unpublished or less-well-known work, or for large numbers of self-citations, it is up to the author's discretion how best to preserve anonymity. Possibilities include leaving out a citation altogether, including it but replacing the citation text with "removed for anonymous submission," or leaving the citation as-is; authors should choose for each citation the treatment which is least likely to reveal authorship.

Previous tech-report or workshop versions of a paper can similarly present a problem for anonymization. We suggest leaving out any identifying information for such versions, but bringing them to the attention of the program committee via the submission page. Reviewers will be instructed that tech reports (including reports on sites such as arXiv) and papers in workshops without archival proceedings do not count as prior publication.

Previous or Concurrent Submissions
Submitted manuscripts should not have been previously published in a journal or in the proceedings of a conference, and should not be under consideration for publication at another conference at any point during the AISTATS review process. It is acceptable to have a substantially extended version of the submitted paper under consideration simultaneously for journal publication, so long as the journal version's planned publication date is in May 2021 or later, the journal submission does not interfere with AISTATS's right to publish the paper, and the situation is clearly described at the time of AISTATS submission. Please describe the situation in the appropriate box on the submission page (and do not include author information in the submission itself, to avoid accidental unblinding).

As mentioned above, reviewers will be instructed that tech reports (including reports on sites such as arXiv) and papers in workshops without archival proceedings do not count as prior publication.

All accepted papers will be presented at the Conference either as contributed talks or as posters, and will be published in the AISTATS Conference Proceedings in the Journal of Machine Learning Research Workshop and Conference Proceedings series. Papers for talks and posters will be treated equally in publication.

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