Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Meta-learning with Stochastic Linear Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase regularization to λ=100 from λ=1 | AVG-OFUL · Synthetic data | cumulative reward | Helped |
| Increase regularization to λ=100 from λ=1 | RR-OFUL · Synthetic data | cumulative reward | Hurt |
| Use AVG-OFUL instead of ITL | BIAS-OFUL meta-learning · LastFM | cumulative regret | Helped |
| Use RR-OFUL instead of ITL | BIAS-OFUL meta-learning · LastFM | cumulative regret | Helped |
| Use AVG-OFUL instead of ITL | BIAS-OFUL meta-learning · MovieLens | cumulative regret | No clear effect |
| Use RR-OFUL instead of ITL | BIAS-OFUL meta-learning · MovieLens | cumulative regret | Hurt |
| Estimate the bias by pooling all available samples with global ridge regression rather than averaging separate task estimates | BIAS-OFUL meta-learning · Similar tasks with shared induced basis | transfer regret | Helped |
| Use global ridge regression to estimate the bias when tasks are misaligned | BIAS-OFUL meta-learning · MovieLens | cumulative regret | Hurt |
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