Exposure Bias as Epistemic Underidentification in Recursive Forecasting

2026-06-11 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new analysis reframes exposure bias in recursive multi-step forecasting, typically viewed as a distribution shift, as an "epistemic underidentification problem." This occurs under partial observability or state truncation, where one-step Bayes supervision fails to identify the deployed recursive predictor when it queries self-generated induced states. The authors formalize this using induced states $Z$ and provenance variables $P$, deriving an induced-state error decomposition that includes teacher-forcing/rollout mismatch, representation--class approximation, and provenance information gaps. Empirically, the study demonstrates that rollout enters a distinct induced-state regime and that closed-loop gains arise from both local adaptation and altering induced states visited during rollout. Furthermore, provenance-aware correction, using a simple binary provenance encoding, can conditionally enhance performance, ultimately recasting exposure bias as reasoning under self-induced epistemic uncertainty.

Key takeaway

For Machine Learning Engineers developing recursive forecasting models, you should recognize that exposure bias extends beyond simple distribution shift. Your models may face epistemic underidentification when generating future states. Consider incorporating provenance variables into your model design to address information gaps and improve performance, especially when dealing with partial observability. Analyzing the distinct induced-state regimes your model enters during rollout can also guide targeted corrective actions.

Key insights

Exposure bias in recursive forecasting is an epistemic underidentification problem, not just distribution shift.

Principles

• Recursive rollout creates distinct induced-state regimes.
• Provenance information gaps contribute to induced-state error.
• Closed-loop gains involve adapting induced states.

Method

The paper formalizes induced states $Z$ and provenance variables $P$ to decompose induced-state error into mismatch, approximation, and provenance information gaps.

In practice

• Consider provenance variables for recursive forecasting.
• Analyze induced states during model rollout.

Topics

• Recursive Forecasting
• Exposure Bias
• Epistemic Uncertainty
• Induced States
• Provenance Variables
• Machine Learning

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.