The Premise Is the Problem: Exchangeability Failure in Self-Monitored Test-Time Adaptation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2608.30502v1 Announce Type: new Abstract: Machine learning systems are increasingly corrected while they run, and the decision of when to intervene is increasingly delegated to statistical moni...
arXiv:2609.39789v1 Announce Type: cross Abstract: Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although r...
The paper argues that current time‑series forecasting benchmarks are too narrow, rewarding low held‑out error while failing to capture real‑world failure modes such as structured input anomalies, regime shifts, and sensor faults. It proposes scenario‑grounded stress testing, where each test includes historical data, future targets, a semantic scenario, a failure operator, and a difficulty level, to make evaluation interpretable and deployment‑relevant.
arXiv:2610. 12232v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods for time-series forecasting update a deployed model, or a small adapter around it, from incoming ground truth.
arXiv:2607. 18899v1 Announce Type: new Abstract: Forecasting under real-world conditions is inherently non-stationary, as the conditional distribution of future observations evolves over time.
arXiv:2610.03662v1 Announce Type: new Abstract: Deploying a new decision policy creates a cold-start problem for prediction models whose targets depend on the policy's actions: historical observation...