Research map / Learning theory and algorithms / Sample complexity
A Computationally Efficient Method for Learning Exponential Family Distributions
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation (NeurIPS 2016 · similarity 0.89)
- Learning Exponential Families from Truncated Samples (NeurIPS 2023 · similarity 0.88)
- Tractable Expected Information Gains for Exponential Family Posteriors (ICML 2026 · similarity 0.87)
- Asymptotics of Alpha-Divergence Variational Inference Algorithms with Exponential Families (NeurIPS 2024 · similarity 0.85)
- Private Statistical Estimation via Truncation (NeurIPS 2025 · similarity 0.85)
- A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation (ICML 2025 · similarity 0.83)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
