ALT 2017 : Algorithmic Learning Theory (ALT)

  in Conferences   Posted on August 21, 2017

Conference Information

Submission Deadline Friday 02 Jun 2017
Conference & Submission Link http://www.comp.nus.edu.sg/~fstephan/alt/alt2017/
Conference Dates Oct 14, 2017 - Oct 17, 2017
Conference Address Kyoto, Japan
Proceedings indexed by
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Google Scholar H5-index: 15
CORE 2017 Rating: B
Guide2Research Overall Ranking: 325
Category Rankings
Computational Theory and Mathematics 37

Conference Call for Papers

The 28th International Conference on Algorithmic Learning Theory (ALT 2017) will be held in Kyoto, Japan, on October 15-17, 2017. The conference is dedicated to the theoretical foundations of machine learning. The conference will be co-located with the 20th International Conference on Discovery Science (DS 2017).

Topics of Interest: We invite submissions with theoretical and algorithmic contributions to new or already existing learning problems including but not limited to:

Comparison of the strength of learning models and the design and evaluation of novel algorithms for learning problems in established learning-theoretic settings such as
Statistical learning theory
Supervised learning and regression
Statistical learning theory
On-line learning
Inductive inference
Query models
Unsupervised learning
Clustering
Semi-supervised and active learning
Stochastic optimization
High dimensional and non-parametric inference
Exploration-exploitation tradeoff, bandit theory
Reinforcement learning, planning, control
Learning with additional constraints, e.g., communication, time or memory budget, or privacy
Analysis of the theoretical properties of existing algorithms such as
Boosting
Kernel-based methods, SVM
Bayesian methods
Graph- and/or manifold-based methods
Methods for latent-variable estimation and/or clustering
Decision tree methods
Information-based methods, MDL
Neural networks
Analyses could include generalization, speed of convergence, computational complexity, or sample complexity.
Definition and analysis of new learning models. Models might identify and formalize classes of learning problems inadequately addressed by existing theory or capture salient properties of important concrete applications.
We are also interested in papers that include viewpoints that are new to the ALT community. We welcome experimental and algorithmic papers provided they are relevant to the focus of the conference by elucidating theoretical results, or by pointing out interesting and not well understood behavior that could stimulate theoretical analysis.

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