Research map / Optimization / Optimizers
Gradient-Based Algorithms for Machine Teaching
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Select teaching examples by MaxGrad rather than bbIMT | MTurk learners · Butterflies | accuracy: 76.09(18.05) -> 80.33(19.76) | Helped |
| Select teaching examples by MaxGrad rather than EXPLAIN | MTurk learners · Chinese Characters | accuracy: 65.44 -> 81.89(12.93) | Helped |
| Select difficult, high-gradient examples rather than examples from the whole training set | Simulated learners · Butterflies | test accuracy | Helped |
| Select difficult, high-gradient examples rather than examples from the whole training set | Simulated learners · Chinese Characters | test accuracy | Helped |
Closest papers by meaning
- Machine Teaching of Active Sequential Learners (NeurIPS 2019 · similarity 0.88)
- Iterative Machine Teaching (ICML 2017 · similarity 0.88)
- Learning to Teach with Dynamic Loss Functions (NeurIPS 2018 · similarity 0.88)
- Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity (ICLR 2018 · similarity 0.88)
- High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling (ICML 2024 · similarity 0.85)
- A Survey of Learning Criteria Going beyond the Usual Risk (Abstract Reprint) (AAAI 2024 · similarity 0.84)
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