Research map / Learning theory and algorithms / Prediction and decision losses
Active Learning for Non-Parametric Regression Using Purely Random Trees
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| two-stage active sampling (leaf allocation proportional to estimated sqrt(p*variance)) instead of random sampling | Mondrian Tree regressor · simulated heteroskedastic and varying-complexity data, Wine, Claisen (Cl) | MSE | Helped |
| two-stage active sampling instead of random sampling | Breiman Tree regressor · simulations vs real data (Wine, Cl) | MSE | Mixed |
| naive uncertainty sampling (leaf size proportional to variance) instead of random sampling | Mondrian Tree active learning · simulated and real datasets | MSE | Mixed |
Closest papers by meaning
- Online Active Regression (ICML 2022 · similarity 0.87)
- Online Active Linear Regression via Thresholding (AAAI 2017 · similarity 0.87)
- Semi-supervised Active Linear Regression (NeurIPS 2022 · similarity 0.87)
- Active Heteroscedastic Regression (ICML 2017 · similarity 0.87)
- Transductive Active Learning: Theory and Applications (NeurIPS 2024 · similarity 0.85)
- Piecewise Constant and Linear Regression Trees: An Optimal Dynamic Programming Approach (ICML 2024 · similarity 0.85)
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.