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LearningStrategyDecisions

Explore, Then Exploit

Do I know where the gains are likely to be, or am I already optimizing one path?

Situation

A problem has many possible places to intervene and no obvious starting point.

The idea

Early experiments should sometimes maximize diversity of learning rather than incremental performance. Once a promising region appears, deeper optimization becomes more useful.

Useful when

New productsambiguous technical problemsoptimizationexperimentation

Watch for

Exploration can become endless experimentation. Shift modes once enough structure appears.

Where this came from

Applied machine learning, Research

Read the reasoning

Lessons From Six Years of Applied Machine Learning in IndustryPrimary post · Jun 4, 2024