Research map / Learning theory and algorithms / Sample complexity
The Generalized Lasso with Nonlinear Observations and Generative Priors
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
- Compressed Sensing using Generative Models (ICML 2017 · similarity 0.92)
- Signal Recovery with Non-Expansive Generative Network Priors (NeurIPS 2022 · similarity 0.89)
- Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors (ICML 2020 · similarity 0.89)
- Efficient Algorithms for Non-gaussian Single Index Models with Generative Priors (AAAI 2024 · similarity 0.87)
- The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements (NeurIPS 2025 · similarity 0.86)
- Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements (ICML 2025 · similarity 0.86)
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.
