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Stochastic Optimization for Performative Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use greedy deploy instead of lazy deploy when performative effects are weak | stochastic optimization for performative prediction · synthetic Gaussian | convergence to performative stability | Helped |
| use lazy deploy instead of greedy deploy when performative effects are strong | stochastic optimization for performative prediction · synthetic Gaussian | convergence to performative stability | Helped |
| use a shorter lazy deployment schedule with smaller α (ablation) | lazy deploy · synthetic Gaussian | initial optimization progress | Helped |
| use a longer lazy deployment schedule with larger α (ablation) | lazy deploy · synthetic Gaussian | long-run progress | Helped |
| use lazy deploy instead of greedy deploy | stochastic optimization for performative prediction · strategic classification, ε=100 | convergence to performative stability | Helped |
| increase α in the lazy deployment schedule (ablation) | lazy deploy · strategic classification, ε=100 | convergence to performative stability | Helped |
| use greedy deploy instead of lazy deploy | stochastic optimization for performative prediction · strategic classification, ε=10^-3 | convergence to performative stability | Helped |
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- The Stability of Online Algorithms in Performative Prediction (NeurIPS 2026 · similarity 0.89)
- Optimal Classification under Performative Distribution Shift (NeurIPS 2024 · similarity 0.89)
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