Explain Multi-Armed Bandit Principles
Overview
This question evaluates understanding of multi-armed bandit principles and contextual bandits, covering algorithmic trade-offs (regret, exploration–exploitation balance, and modeling assumptions) among epsilon-greedy, UCB, and Thompson sampling, along with operational concerns such as delayed or batched rewards, non‑stationarity, offline policy evaluation, and production safety. It is commonly asked in Analytics & Experimentation and machine learning interviews because it probes both conceptual understanding and practical application of online decision-making, testing the ability to reason about algorithm selection, performance trade-offs, and deployment considerations.
Multi-Armed Bandits vs A/B Testing: Algorithms, Trade-offs, and Production Considerations
You are designing online decision-making for a large-scale product (e.g., recommendations, pricing, notifications) where you must learn from user interactions while maximizing outcomes.
- Explain what multi-armed bandit (MAB) algorithms are and when to use them instead of standard A/B tests.
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Compare the following algorithms along three dimensions: regret, exploration–exploitation balance, and assumptions.
- Epsilon-greedy
- Upper Confidence Bound (UCB)
- Thompson sampling (TS)
- Extend the discussion to contextual bandits: what they are, typical algorithms, and when to use them.
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Discuss operational considerations:
- Delayed or batched rewards
- Non-stationarity and drift handling
- Offline policy evaluation (OPE) using logged bandit data
- Safety and guardrails for production deployment
Solution
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