Open Problems

Uncertainty Quantification & Conformal Prediction

Evaluating Uncertainty Quantification and Conformal Prediction Methods Beyond Binary Outcomes

Scope to testOpen
Possible candidate · 2/5 runs4 papers report this50% from 2025+

Generated automatically from the limitations stated in 4 papers (AISTATS, COLT, ICML), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Existing uncertainty quantification and conformal prediction formulations in this family have been developed and tested exclusively on binary outcome spaces ($y \in \{0, 1\}$). As a result, it is unknown whether their theoretical properties, coverage guarantees, and set efficiencies persist or degrade when applied to multiclass and continuous regression targets. Practitioners in non-binary domains are currently blocked from deploying these methods with verified reliability.

Why it matters

Clarifies the operational limits and empirical validity of these uncertainty quantification methods across non-binary settings, enabling their principled deployment on multiclass and continuous problems.

Ways to approach it

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  1. 1

    Empirical robustness benchmarking: Adapt the binary uncertainty quantification methods to standard multiclass and regression benchmarks, measuring empirical coverage error, marginal/conditional validity, and prediction set/interval size across varying label cardinalities and noise distributions.

  2. 2

    Reduction and decomposition strategies: Evaluate one-vs-all, hierarchical grouping, and quantile-based reductions to bridge binary formulations to multiclass and continuous targets, measuring computational overhead and loss in prediction efficiency relative to native baselines.

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Why it might fail

Direct extensions might perform identically to existing standard multiclass conformal methods, yielding no surprising failure modes or distinctive trade-offs in a pure robustness study.

Evidence

Each paper's own statement of the limitation, verbatim.

Nearest existing work

Related open problems

Uncertainty Quantification & Conformal Prediction

Scope to testOpen

Principled Multiclass Extensions of Binary-Only Theoretical Guarantees in Prediction Methodology

A large body of theory and tooling — covering uncertainty quantification, conformal methods, and attribution-style analyses — is derived and validated only for binary or single-label settings, with multiclass, multi-label, and open-set cases deferred to "future work" or handled via ad hoc one-versus-rest surrogates whose guarantees and sharpness are not established. As a result, practitioners applying these techniques to realistic multi-class problems either inherit unquantified approximation error or cannot use the methods at all (e.g., when only discrete predictions rather than continuous outputs are available). Each new paper re-derives binary results and re-defers the extension, so the field lacks a general recipe for lifting binary guarantees to K-class settings with provable validity and measured sharpness.

Strong candidate · 4/5 runs14 papers report this30% from 2025+
Generated automatically, not curated by hand. Automated prior-work checks catch about a third of existing work, so treat this problem as a lead to investigate.