Connectionists: ACML 2022 -- Third Call for Papers [Submission deadline: 23rd June]

[Raj Sharma]

**apologies if you have received multiple copies of this email*
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*ACML 2022*
The 14th Asian Conference on Machine Learning
Hyderabad, India
December 14-16, 2022
https://www.acml-conf.org/2022/
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*CALL FOR PAPERS*

The 14th Asian Conference on Machine Learning (ACML 2022) will take place
between December 14-16, 2022 at Hyderabad, India. 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.
While the main conference paper presentations will remain virtual to
encourage widespread participation in current times, the conference will
also have physical components to allow in-person interaction for those who
can attend.


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 is closed for the
journal track but accepting submissions in the conference track.

   - *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)


Please refer to http://www.acml-conf.org/2022/ for more details.
Instructions for submission and LaTeX templates will be available soon (at
least one month before the first deadline).


*IMPORTANT DATES *
(subject to minor changes in case there are conflicts with timelines of
other major ML conferences)

Conference Track

   - 23 Jun 2022 Submission deadline
   - 11 Aug 2022 Reviews released to authors
   - 18 Aug 2022 Author rebuttal deadline
   - 08 Sep 2022 Acceptance notification
   - 29 Sep 2022 Camera-ready submission deadline



*TOPICS OF INTEREST include but are not limited to:*
General machine learning

   - Active 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
   - 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

   - Attention mechanism and transformers
   - Deep learning theory
   - Generative models
   - Deep reinforcement learning
   - Architectures
   - Other topics in deep learning

Probabilistic Methods

   - Bayesian machine learning
   - Graphical models
   - Variational inference
   - Gaussian processes
   - Monte Carlo methods

Theory

   - Computational learning theory
   - Optimization (convex, non-convex)
   - Bandits
   - Game theory
   - Matrix/Tensor methods
   - Statistical learning theory
   - Other theories

Datasets and Reproducibility

   - ML datasets and benchmarks
   - Implementations, libraries
   - 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
   - Collaborative filtering
   - Computer vision
   - COVID-19 related research
   - Healthcare
   - Human activity recognition
   - Information retrieval
   - Natural language processing
   - Social networks
   - Web search
   - Climate science
   - Social good
   - Other applications


*OAMLS @ ACML*: Besides a program of tutorials and workshops, this year we
will continue the Online Asian Machine Learning School (OAMLS) as part of
ACML (dates to be finalized, likely to be around the ACML conference dates,
held virtually). OAMLS aims to help prepare the next generation of machine
learning researchers and practitioners by providing them with knowledge of
machine learning fundamentals as well as state-of-the-art advances. It
focuses on participants in the Asia-Pacific region; the virtual format,
supported by ever-improving communication technologies, allows affordable
participation from students and practitioners from a large part of the
region, including those from under-represented areas, who may otherwise be
unable to afford travel to a physical international school.
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