Research map / Probabilistic and scientific modeling / Molecules and proteins
Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the amortized sequential FastAR policy instead of per-instance recourse generation | Recourse generation · Adult Income | Time (s): 0.12 -> 0.015 | Helped |
| Encode causal constraints in the transition function | FastAR · German Credit, Adult Income, Credit Default | Constraints: 100.0 | Helped |
| Use sequential one-feature-at-a-time actions | FastAR · German Credit, Adult Income, Credit Default | Sparsity: 1.22; 1.00; 1.008 | Helped |
| Include a data-manifold distance cost | FastAR reward function · Recourse generation | Manifold dist. | Helped |
| Use random actions rather than the learned FastAR policy | Recourse generation · German Credit | Validity: 97.3 -> 23.7 | Hurt |
| Use greedy action selection rather than the learned FastAR policy | Recourse generation · Credit Default | Validity: 99.9 -> 65.1 | Hurt |
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- Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution (NeurIPS 2024 · similarity 0.83)
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