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arXiv Machine Learning By Cillian Hourican, Eric Dignum, Rick Quax, Debraj Roy

Scalable partial information decomposition for symptom networks via supervised embeddings

Read the original on arXiv Machine Learning →

The paper introduces ePID, an embedding-based approach that scales partial information decomposition (PID) to large symptom networks by compressing non‑focal symptoms into a low‑cardinality discrete embedding. Using a supervised Agglomerative Conditional Information Bottleneck (ACIB) embedding, ePID accurately recovers source‑unique, remainder‑unique, redundant, and synergistic components for each ordered source‑target pair across 83 real‑world datasets, outperforming 12 other embeddings. Applied to PHQ‑9 and the Interpersonal Reactivity Index, ePID reveals distinct patterns of redundancy and synergy that align with each instrument’s construction, demonstrating its ability to separate overlapping from interaction‑dependent information in symptom networks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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