Conference on Parsimony and Learning (CPAL)
The Conference on Parsimony and Learning (CPAL) is an annual research conference focused on addressing the parsimonious, low-dimensional structures that prevail in machine learning, signal processing, optimization, and beyond. It covers theories, algorithms, applications, hardware and systems, as well as scientific foundations for learning with parsimony.
Events
CPAL 2027
by CPAL
The Fourth Conference on Parsimony and Learning (CPAL 2027) will be held at Hitotsubashi Hall in Tokyo, Japan. CPAL 2027 invites theoretical, methodological, empirical, and systems contributions that make parsimony central to learning—through sparsity, low rank, symmetry, modularity, compressibility, or structured computation. The conference welcomes work on foundation, generative, multimodal, and agentic models when the underlying parsimony principle is explicit. Submissions are accepted via the Proceedings Track (published in PMLR) or the Recent Spotlight Track (no archival proceedings).