MINTS: Minimalist Thompson Sampling

2026-06-01 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

MINTS, or Minimalist Thompson Sampling, introduces a minimalist Bayesian framework for sequential decision-making under uncertainty. This approach places a prior solely on the optimum's location, while eliminating nuisance parameters through profile likelihood, resulting in a generalized posterior that naturally accommodates complex structural constraints. For multi-armed bandits with mean constraints, MINTS achieves near-optimal non-asymptotic regret guarantees and sharp almost-sure asymptotic regret characterizations. It specifically attains the classical Lai--Robbins constant in unstructured settings and automatically adapts to unimodal structure, achieving a sharp constant determined only by the immediate neighbors of the optimal arm.

Key takeaway

For AI scientists designing sequential decision-making systems with complex structural constraints, MINTS offers a principled and efficient Bayesian alternative. You should evaluate MINTS for its ability to provide near-optimal non-asymptotic regret guarantees and sharp asymptotic regret characterizations, particularly when adapting to problem structures like unimodality, potentially simplifying model development and improving performance in constrained environments.

Key insights

MINTS simplifies Bayesian decision-making by focusing priors and profiling nuisance parameters to handle structural constraints effectively.

Principles

• Simplify Bayesian models by profiling nuisance parameters.
• Focus priors on optimum location to accommodate constraints.
• Adapt algorithms to problem structure for improved regret.

Method

MINTS employs a minimalist Bayesian framework, placing a prior only on the optimum's location and eliminating nuisance parameters via profile likelihood to yield a generalized posterior.

In practice

• Apply MINTS to multi-armed bandit problems with mean constraints.
• Use MINTS for sequential decision-making requiring structural constraints.
• Evaluate MINTS for near-optimal regret performance.

Topics

• Thompson Sampling
• Multi-armed Bandits
• Bayesian Methods
• Sequential Decision-Making
• Regret Guarantees
• Structural Constraints

Best for: Research Scientist, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.