Research map / Label-efficient and robust learning / Domain adaptation and generalization
Active Multi-Task Representation Learning
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 |
|---|---|---|---|
| Use iterative target-aware source-task sampling instead of uniform sampling | Multi-task representation learning · MNIST-C, linear representation | target-task performance | Helped |
| Use iterative target-aware source-task sampling instead of uniform sampling | Multi-task representation learning · MNIST-C, CNN representation | target-task performance | Helped |
| Allocate source samples according to estimated task relevance | Active Task Relevance Sampling · MNIST-C, source-task allocation | sample allocation to target-related tasks | Helped |
| Sample according to known task relevance rather than uniformly | Linear representation learning · Theoretical sample-complexity comparison | source sample complexity: s^* ... compared with M | Helped |
| Estimate task relevance adaptively rather than requiring it to be known | Active Task Relevance Sampling · Linear representation learning | source sample complexity: an additional low order term square σ ε^{-1} | Helped |
Closest papers by meaning
- Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks (ICML 2024 · similarity 0.88)
- Learning Task-Agnostic Representations through Multi-Teacher Distillation (NeurIPS 2025 · similarity 0.87)
- Distribution Matching for Multi-Task Learning of Classification Tasks: A Large-Scale Study on Faces & Beyond (AAAI 2024 · similarity 0.85)
- Joint-Task Regularization for Partially Labeled Multi-Task Learning (CVPR 2024 · similarity 0.85)
- Towards Principled Task Grouping for Multi-Task Learning (NeurIPS 2026 · similarity 0.85)
- Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning (NeurIPS 2025 · 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.