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[2601.16174] Beyond Predictive Uncertainty: Reliable Representation Learning with Structural Constraints
Summary
This paper introduces a framework for reliable representation learning in machine learning, emphasizing the importance of representation-level uncertainty and structural constraints.
Why It Matters
The study challenges traditional views on uncertainty in machine learning by proposing that reliability should be a core property of learned representations. This approach can enhance the stability and robustness of machine learning models, making them more effective in real-world applications where noise and variability are common.
Key Takeaways
- Reliability in representation learning is crucial and should be prioritized.
- The proposed framework incorporates uncertainty-aware regularization.
- Structural constraints help reduce spurious variability in representations.
- The approach is model-agnostic, applicable across various architectures.
- Enhancing representation reliability can improve model performance in noisy environments.
Statistics > Machine Learning arXiv:2601.16174 (stat) [Submitted on 22 Jan 2026 (v1), last revised 19 Feb 2026 (this version, v3)] Title:Beyond Predictive Uncertainty: Reliable Representation Learning with Structural Constraints Authors:Yiyao Yang View a PDF of the paper titled Beyond Predictive Uncertainty: Reliable Representation Learning with Structural Constraints, by Yiyao Yang View PDF Abstract:Uncertainty estimation in machine learning has traditionally focused on the prediction stage, aiming to quantify confidence in model outputs while treating learned representations as deterministic and reliable by default. In this work, we challenge this implicit assumption and argue that reliability should be regarded as a first-class property of learned representations themselves. We propose a principled framework for reliable representation learning that explicitly models representation-level uncertainty and leverages structural constraints as inductive biases to regularize the space of feasible representations. Our approach introduces uncertainty-aware regularization directly in the representation space, encouraging representations that are not only predictive but also stable, well-calibrated, and robust to noise and structural perturbations. Structural constraints, such as sparsity, relational structure, or feature-group dependencies, are incorporated to define meaningful geometry and reduce spurious variability in learned representations, without assuming fully correct or...