Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911604488011776 |
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| author | Cho, Dongkyu Li, Xiyue Adhikari, Samrachana Chunara, Rumi |
| author_facet | Cho, Dongkyu Li, Xiyue Adhikari, Samrachana Chunara, Rumi |
| contents | Continual learning aims to update models under distribution shift without forgetting, yet many high-stakes deployments, such as healthcare, also require interpretability. In practice, models that adapt well (e.g., deep networks) are often opaque, while models that are interpretable (e.g., decision trees) are brittle under shift, making it difficult to achieve both properties simultaneously. In response, we propose Tree of Concepts, an interpretable continual learning framework that uses a shallow decision tree to define a fixed, rule-based concept interface and trains a concept bottleneck model to predict these concepts from raw features. Continual updates act on the concept extractor and label head while keeping concept semantics stable over time, yielding explanations that do not drift across sequential updates. On multiple tabular healthcare benchmarks under continual learning protocols, our method achieves a stronger stability-plasticity trade-off than existing baselines, including replay-enhanced variants. Our results suggest that structured concept interfaces can support continual adaptation while preserving a consistent audit interface in non-stationary, high-stakes domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17089 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains Cho, Dongkyu Li, Xiyue Adhikari, Samrachana Chunara, Rumi Machine Learning Continual learning aims to update models under distribution shift without forgetting, yet many high-stakes deployments, such as healthcare, also require interpretability. In practice, models that adapt well (e.g., deep networks) are often opaque, while models that are interpretable (e.g., decision trees) are brittle under shift, making it difficult to achieve both properties simultaneously. In response, we propose Tree of Concepts, an interpretable continual learning framework that uses a shallow decision tree to define a fixed, rule-based concept interface and trains a concept bottleneck model to predict these concepts from raw features. Continual updates act on the concept extractor and label head while keeping concept semantics stable over time, yielding explanations that do not drift across sequential updates. On multiple tabular healthcare benchmarks under continual learning protocols, our method achieves a stronger stability-plasticity trade-off than existing baselines, including replay-enhanced variants. Our results suggest that structured concept interfaces can support continual adaptation while preserving a consistent audit interface in non-stationary, high-stakes domains. |
| title | Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.17089 |