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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing
TL;DR
Tree-Coupled A/B Testing (TCAB) introduces a novel method for efficiently comparing multiple adaptive decision policies by sharing feedback across them, significantly reducing the number of required outcomes. This approach achieves a cost of T plus a small additional term, compared to JT outcomes in traditional methods, enhancing the cost-precision balance in policy evaluation.
✦ Why It Matters
Implement TCAB to reduce resource costs in evaluating multiple decision policies today.
Key Takeaways
- Adopt TCAB for efficient multi-policy evaluation.
- Utilize shared feedback to minimize outcome costs.
- Leverage tree structures for contextual bandit policies.
- Achieve sublinear pseudo-regret in policy comparisons.
How It Works
TCAB connects the histories of multiple policies through a tree structure, allowing for maximally coupled feedback. Each policy retains its individual trajectory while sharing rewards based on matched tree edges, which reduces the total number of required outcomes.