eScholarship
metadata only
On Fairness and Interpretability in Matrix and Tensor Methods for Stratified Data
This dissertation develops and analyzes methods for interpretable and fair machine learning that account for stratified data. We investigate two settings in which data may exhibit meaningful differences across groups or sources. First, we propose and analyze Stratified Non-Negative Tensor Factorizat
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Separable Nonnegative Matrix Factorization Using Powered Ratio-of-Norms Regularization#793037
- Separable Nonnegative Matrix Factorization Using Powered Ratio-of-Norms Regularization#789033
- AlphaRAD: Grounded Zero-Shot Classification in Chest Radiology via $α$-Corrected Binary Cross Entropy and Factorized Latent Supervision#822186
- Nonnegative matrix factorizations and related compositional models: Equivalence, identifiability, and an application on the grain-size analysis of sediments#1110153
- Multimodal feature fusion model for breast mass malignant risk stratification.#288776
Record · ID 983522
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive