Research map / Learning theory and algorithms / Prediction and decision losses
Bayesian Batch Active Learning as Sparse Subset Approximation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use ACS-FW batch construction instead of random selection | neural linear model · yacht | test RMSE: 1.272 -> 1.031 | Helped |
| use ACS-FW instead of naive top-b MAXENT batches | neural linear model · energy | test RMSE: 1.443 -> 0.855 | Helped |
| use ACS-FW instead of naive top-b MAXENT batches | neural linear model · yacht | test RMSE: 0.923 -> 1.031 | Hurt |
| use ACS-FW instead of naive top-b MAXENT batches | neural linear model · year | test RMSE: 13.030 -> 12.194 | Helped |
| use sequential greedy MAXENT-SG instead of ACS-FW batches | neural linear model · boston | test RMSE: 3.799 -> 3.458 | Helped |
| use ACS-FW instead of RANDOM on large classification tasks | ResNet18 neural linear model · cifar10, SVHN, Fashion MNIST | accuracy | Helped |
| use ACS-FW instead of K-Center/BALD/MaxEnt | ResNet18 neural linear model · cifar10, SVHN, Fashion MNIST | accuracy | No clear effect |
| use ACS-FW instead of MAXENT on batch construction runtime | neural linear model · power | total runtime (s): 617.0 -> 179.8 | Helped |
Closest papers by meaning
- Batch Active Learning at Scale (NeurIPS 2021 · similarity 0.93)
- Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing (ICLR 2023 · similarity 0.91)
- BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning (NeurIPS 2019 · similarity 0.90)
- Active Statistical Inference (ICML 2024 · similarity 0.86)
- Active Learning Guided by Efficient Surrogate Learners (AAAI 2024 · similarity 0.85)
- Transductive Active Learning: Theory and Applications (NeurIPS 2024 · similarity 0.85)
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