Laura Israel - Je ne regrette rien: Teaching Machine Learning Models Regret Avoidance

Complex ML systems often involve multiple sequential decision components (models, rules, human decisions) that contribute to the same higher-level goal

Regret in ML systems occurs when decisions turn out to be suboptimal after more information becomes available - this can be incorporated into model training

Sample weights and custom loss functions can be used to penalize decisions that lead to regret conditions, without replacing existing models

The regret parameter should be tuned carefully - if set too high it can lead to underperformance both locally and globally

Implementation demonstrated using XGBoost by modifying the loss function and applying sample weights to regret cases

Key benefits include:

• Simple implementation with existing ML frameworks
• No need for complex model changes
• Can improve global optimization while maintaining local performance

Results showed significant improvements - in one case converting 300,000 more transactions per month worth €20M in additional merchant revenue

Approach works best when:

• System has sequential decision points
• Later information reveals optimal decisions
• Components contribute to shared objectives
• Regret conditions can be clearly defined

Important considerations:

• Regret parameter needs careful tuning
• Should be stable across optimization
• Implementation depends on use case
• May need to adapt for continuous targets

Complex systems can be viewed as game trees with imperfect information games, allowing application of game theory concepts