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Conference on Parsimony and Learning (CPAL)
January 2024, HKU

Subject Areas

Theory & Foundations

  • Theories for sparse coding, structured sparsity, subspace learning, low-dimensional manifolds, and general low-dimensional structures.
  • Dictionary learning and representation learning for low-dimensional structures and their connections to deep learning theory.
  • Equivariance and invariance modeling.
  • Theoretical neuroscience and cognitive science foundation for parsimony, and biologically inspired computational mechanisms.

Optimization & Algorithms

  • Optimization, robustness, and generalization methods for learning compact and structured representations.
  • Interpretable and efficient deep architectures (e.g., based on unrolled optimization).
  • Data-efficient and computation-efficient training and inference.
  • Adaptive and robust learning and inference algorithms.
  • Distributed, networked, or federated learning at scale.
  • Other nonlinear dimension-reduction and representation-learning methods.

Data, Systems & Applications

  • Domain-specific datasets, benchmarks, and evaluation metrics.
  • Parsimonious and structured representation learning from data.
  • Inverse problems that benefit from parsimonious priors.
  • Hardware and system co-design for parsimonious learning algorithms.
  • Parsimonious learning in intelligent systems that integrate perception-action cycles.
  • Applications in science, engineering, medicine, and social sciences.

The above is intended as a high-level overview of CPAL’s focus and by no means exclusive. If you doubt that your paper fits the venue, feel free to contact the program chairs via email at pcs@cpal.cc.