Constrained Online Decision-Making: A Unified Framework

TL;DR

This paper introduces a unified framework for constrained online decision-making that handles various applications and provides algorithms with strong theoretical guarantees, leveraging new complexity measures.

Contribution

It proposes a general algorithmic framework for constrained decision-making, incorporating upper counterfactual confidence bounds and a generalized eluder dimension for complex environments.

Findings

The framework applies to multiple constrained learning problems.

Algorithms achieve strong theoretical guarantees.

Introduces a generalized eluder dimension for complex density classes.

Abstract

Contextual online decision-making problems with constraints appear in a wide range of real-world applications, such as adaptive experimental design under safety constraints, personalized recommendation with resource limits, and dynamic pricing under fairness requirements. In this paper, we investigate a general formulation of sequential decision-making with stage-wise feasibility constraints, where at each round, the learner must select an action based on observed context while ensuring that a problem-specific feasibility criterion is satisfied. We propose a unified algorithmic framework that captures many existing constrained learning problems, including constrained bandits, active learning with label budgets, online hypothesis testing with Type I error control, and model calibration. Central to our approach is the concept of upper counterfactual confidence bounds, which enables the design of practically efficient online algorithms with strong theoretical guarantees using any offline conditional density estimation oracle. To handle feasibility constraints in complex environments, we introduce a generalized notion of the eluder dimension, extending it from the classical setting based on square loss to a broader class of metric-like probability divergences. This allows us to capture the complexity of various density function classes and characterize the utility regret incurred due to feasibility constraint uncertainty. Our result offers a principled foundation for constrained sequential decision-making in both theory and practice.