Research map / Reinforcement learning / Visual and representation RL
Active Learning with Logged Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use multiple importance sampling instead of standard importance sampling (ablation) | DBAL with warm start · UCI/LIBSVM and synthetic datasets across four logging policies | AUC | Helped |
| Add the debiasing query strategy to multiple-importance-sampling DBAL (ablation) | DBALwm · Uniform, Uncertainty, and Certainty policies | AUC | Helped |
| Add the debiasing query strategy to multiple-importance-sampling DBAL (ablation) | DBALwm · Identical policy | AUC | No clear effect |
| Add warm start, multiple importance sampling, and debiasing to passive learning | Passive · All datasets and logging policies | AUC | Mixed |
Closest papers by meaning
- Learning Active Learning from Data (NeurIPS 2017 · similarity 0.88)
- Re-Active Learning: Active Learning with Relabeling (AAAI 2016 · similarity 0.86)
- A POMDP Formulation of Proactive Learning (AAAI 2016 · similarity 0.86)
- Asking the Right Questions to the Right Users: Active Learning with Imperfect Oracles (AAAI 2020 · similarity 0.86)
- Universal Rates for Active Learning (NeurIPS 2024 · similarity 0.85)
- Uncertainty Herding: One Active Learning Method for All Label Budgets (ICLR 2025 · similarity 0.85)
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