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This paper proposes weighted conformal methods for changepoint localization and root cause analysis that reduce confidence set size under corrupted observations by downweighting likely contaminated data, using uncertainty signals and meta-learning.
This paper presents an LLM-based pipeline for analyzing mental health changes from sequentially ordered social media posts, participating in the CLPsych 2026 shared task. It performs post-level assessment and user-level temporal modeling to capture shifts in psychological well-being.
The paper proposes non-parametric estimators KM-ARL and KM-ADD for evaluating changepoint detectors under finite and irregular sequence lengths, drawing an analogy between QCD and survival analysis.