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[2602.20652] DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Summary
The paper presents DANCE, a novel algorithm for conformal prediction in machine learning that enhances uncertainty quantification by utilizing adaptive neighborhood methods.
Why It Matters
As machine learning models grow in complexity, accurate uncertainty quantification becomes crucial for reliable predictions. DANCE addresses inefficiencies in existing conformal prediction methods by offering a more adaptive approach, potentially improving model performance across various applications.
Key Takeaways
- DANCE combines two novel nonconformity scores for improved prediction sets.
- The algorithm adapts to the data's embedded representation for better efficiency.
- DANCE outperforms existing conformal prediction methods in terms of set size and robustness.
Statistics > Machine Learning arXiv:2602.20652 (stat) [Submitted on 24 Feb 2026] Title:DANCE: Doubly Adaptive Neighborhood Conformal Estimation Authors:Brandon R. Feng, Brian J. Reich, Daniel Beaglehole, Xihaier Luo, David Keetae Park, Shinjae Yoo, Zhechao Huang, Xueyu Mao, Olcay Boz, Jungeum Kim View a PDF of the paper titled DANCE: Doubly Adaptive Neighborhood Conformal Estimation, by Brandon R. Feng and 9 other authors View PDF HTML (experimental) Abstract:The recent developments of complex deep learning models have led to unprecedented ability to accurately predict across multiple data representation types. Conformal prediction for uncertainty quantification of these models has risen in popularity, providing adaptive, statistically-valid prediction sets. For classification tasks, conformal methods have typically focused on utilizing logit scores. For pre-trained models, however, this can result in inefficient, overly conservative set sizes when not calibrated towards the target task. We propose DANCE, a doubly locally adaptive nearest-neighbor based conformal algorithm combining two novel nonconformity scores directly using the data's embedded representation. DANCE first fits a task-adaptive kernel regression model from the embedding layer before using the learned kernel space to produce the final prediction sets for uncertainty quantification. We test against state-of-the-art local, task-adapted and zero-shot conformal baselines, demonstrating DANCE's superior blend o...
