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[2602.21446] ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding
Summary
The paper presents ConformalHDC, a framework that integrates uncertainty quantification into hyperdimensional computing for improved neural decoding, demonstrating its effectiveness on real-world datasets.
Why It Matters
This research addresses critical limitations in hyperdimensional computing by providing a robust method for uncertainty quantification, enhancing the reliability of neural decoding applications. As machine learning increasingly intersects with neuroscience, these advancements could lead to more accurate interpretations of neural data, impacting fields like brain-computer interfaces and cognitive neuroscience.
Key Takeaways
- ConformalHDC combines hyperdimensional computing with conformal prediction for better uncertainty management.
- The framework offers two formulations: set-valued for robust decision boundaries and point-valued for improved accuracy.
- Demonstrated effectiveness on real-world datasets, particularly in decoding neural activity.
- Provides rigorous uncertainty estimates, enhancing the reliability of predictions.
- Positions itself as a foundational tool for neuromorphic computing applications.
Statistics > Machine Learning arXiv:2602.21446 (stat) [Submitted on 24 Feb 2026] Title:ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding Authors:Ziyi Liang, Hamed Poursiami, Zhishun Yang, Keiland Cooper, Akhilesh Jaiswal, Maryam Parsa, Norbert Fortin, Babak Shahbaba View a PDF of the paper titled ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding, by Ziyi Liang and 7 other authors View PDF HTML (experimental) Abstract:Hyperdimensional Computing (HDC) offers a computationally efficient paradigm for neuromorphic learning. Yet, it lacks rigorous uncertainty quantification, leading to open decision boundaries and, consequently, vulnerability to outliers, adversarial perturbations, and out-of-distribution inputs. To address these limitations, we introduce ConformalHDC, a unified framework that combines the statistical guarantees of conformal prediction with the computational efficiency of HDC. For this framework, we propose two complementary variations. First, the set-valued formulation provides finite-sample, distribution-free coverage guarantees. Using carefully designed conformity scores, it forms enclosed decision boundaries that improve robustness to non-conforming inputs. Second, the point-valued formulation leverages the same conformity scores to produce a single prediction when desired, potentially improving accuracy over traditional HDC by accounting for class interactions. We demo...
