ICMLA 2021

The aim of the conference is to bring researchers working in the areas of machine learning and applications together. The conference will cover both theoretical and experimental research results. Submission of machine learning papers describing machine learning applications in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, game playing and problem solving is strongly encouraged.

We encourage submissions of high quality research papers on all topics in the general area of machine learning and its applications. Topics of interest include, but are not limited to, the following areas:

• General Machine Learning (e.g., statistical learning, reinforcement learning, supervised learning, unsupervised learning, clustering, hybrid learning, federated learning, online and incremental learning, ranking, feature selection, few-shot learning, evolutionary learning, etc.)
• Deep Learning (neural network models, deep reinforcement learning, etc.)
• Learning Theory (game theory, statistical learning theory, computational learning theory,
  plausible reasoning theory and models, etc.)
• Machine Learning performance and optimization (network architectures search, pruning, quantization, learning low capacity devices, scalability of learning algorithms, system, performance, offloading, distributed and parallel learning, etc.)
• Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods, etc.)
• Trustworthy Machine Learning (security, privacy, adversarial learning, etc.)
• Applications (gaming, problem solving, virtual environments, industry, manufacturing, homeland security, medicine, bioinformatics and system biology, healthcare, neuroscience, economics, business, social good, web, mobile data, time series data,  multimedia data, natural language processing, data mining, information retrieval, knowledge discovery, etc.)

Contributions describing applications of machine learning (ML) techniques to real-world problems, interdisciplinary research involving machine learning, experimental and/or theoretical studies yielding new insights into the design of ML systems, and papers describing development of new analytical frameworks that advance practical machine learning methods are especially encouraged.