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CALL FOR PAPERS
The 15th Asian Conference on Machine Learning (ACML 2023) will take place between November 11-14, 2023 in İstanbul, Turkey. The conference aims to provide a leading international forum for researchers in machine learning and related fields to share their new ideas, progress and achievements. The conference calls for high-quality, original research papers in the theory and practice of machine learning. The conference also solicits proposals focusing on frontier research, new ideas and paradigms in machine learning. We encourage submissions from all parts of the world, not only confined to the Asia-Pacific region. The conference runs two publication tracks, authors may submit either to: Conference Track: (16-page limit with references) for which the proceedings will be published as a volume of Proceedings of Machine Learning Research Workshop and Conference Proceedings (PMLR). Journal Track: (20-page limit with references) for which accepted papers will appear in a special issue of the Springer Machine Learning Journal (MLJ). Please refer to http://www.acml-conf.org/2023/ for more details. Instructions for submission and LaTeX templates will be available soon. IMPORTANT DATES (subject to minor changes in case there are conflicts with timelines of other major ML conferences) Conference Track 23 June 2023 Submission deadline 11 August 2023 Reviews released to authors 18 August 2023 Author rebuttal deadline 08 September 2023 Acceptance notification 29 September 2023 Camera-ready submission deadline Journal Track 26 May 2023 Submission deadline 07 July 2023 1st round review results (accept, minor revision, or reject) 11 August 2023 Revised manuscript submission deadline (for minor revision papers) 08 September 2023 Acceptance notification 29 September 2023 Camera-ready submission deadline TOPICS Topics of interest include but are not limited to: General machine learning Active learning Bayesian machine learning Dimensionality reduction Feature selection Graphical models Imitation Learning Latent variable models Learning for big data Learning from noisy supervision Learning in graphs Multi-objective learning Multiple instance learning Multi-task learning Neuro-symbolic learning Online learning Optimization Reinforcement learning Relational learning Semi-supervised learning Sparse learning Structured output learning Supervised learning Transfer learning Unsupervised learning Other machine learning methodologies Deep learning Architectures Attention mechanism and transformers Deep learning theory Deep reinforcement learning Generative models Supervised learning Other topics in deep learning Theory Bandits Computational learning theory Game theory Matrix/tensor methods Optimization Statistical learning theory Other theories Datasets and reproducibility Implementations, libraries ML datasets and benchmarks Other topics in reproducible ML research Trustworthy machine learning Accountability, explainability, transparency Causality Fairness Privacy Robustness Other topics in trustworthy ML Applications Bioinformatics Biomedical informatics Climate science Collaborative filtering Computer vision COVID-19 related research Healthcare Human activity recognition Information retrieval Natural language processing Social good Social networks Web search Other applications | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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