Efficient Online Conformal Selection with Limited Feedback

2026-05-14 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

This work introduces an approach to conformal selection, a problem where an agent must choose the smallest possible subset of options to guarantee that at least one "success" is identified with a pre-specified target probability $\phi$. The research addresses the challenge of minimizing resource cost (efficiency) in such selections, particularly under limited feedback scenarios. It demonstrates that the Adaptive Conformal Inference (ACI) update rule, when applied to the correct control parameter or dual variable, achieves both adversarial validity and stochastic efficiency. This ensures the success target is met on average for any input sequence, including under distribution shifts, and yields sublinear efficiency regret for i.i.d. inputs against a stochastic benchmark. The approach is shown to work under canonical bandit and semi-bandit feedback models using a unifying algorithmic technique and a Lyapunov function-based analytic framework, handling more complex settings with less feedback than previous methods.

Key takeaway

For research scientists developing online learning systems with limited feedback, this work suggests that applying the Adaptive Conformal Inference (ACI) update rule can provide robust guarantees. You should consider ACI for tasks requiring minimal option selection while maintaining a target success probability, especially when facing distribution shifts or sparse feedback, as it offers both validity and efficiency.

Key insights

The ACI update rule ensures valid and efficient conformal selection even with limited bandit feedback.

Principles

• Adversarial validity ensures success target is met on average.
• Stochastic efficiency achieves sublinear regret for i.i.d. inputs.

Method

The Adaptive Conformal Inference (ACI) update rule is applied to a control parameter or dual variable, analyzed via Lyapunov functions, to achieve validity and efficiency.

In practice

• Apply ACI for minimal subset selection.
• Use ACI in bandit/semi-bandit feedback scenarios.

Topics

• Conformal Selection
• Adaptive Conformal Inference
• Bandit Feedback
• Online Learning
• Uncertainty Quantification

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.