SOCP uses self-organizing maps for unsupervised group discovery to enable local calibration in conformal prediction, reducing regional coverage gaps on benchmarks with small set-size increases while preserving validity guarantees.
Kelley Pace and R
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6representative citing papers
The Multi-Block DC class admits polynomial-size DC decompositions for problems that require exponential size under standard DC programming and supplies explicit constructive formulations for deep ReLU networks together with convergent batch and stochastic algorithms.
TLRD distills tri-level rationales (instance features, dataset distributions, neighbor comparisons) from a teacher into student LLMs to close the accuracy gap with tree ensembles on tabular data while generating grounded explanations.
Develops a skew-adaptive split conformal prediction method that learns local skewness via a gauge-derived conformity score and an asinh residual model while preserving marginal validity under exchangeability.
Model evaluation in supervised learning should be treated as a context-dependent, decision-oriented process aligned with operational objectives rather than relying on a small set of aggregate metrics.
citing papers explorer
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Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery
SOCP uses self-organizing maps for unsupervised group discovery to enable local calibration in conformal prediction, reducing regional coverage gaps on benchmarks with small set-size increases while preserving validity guarantees.
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The Multi-Block DC Function Class: Theory, Algorithms, and Applications
The Multi-Block DC class admits polynomial-size DC decompositions for problems that require exponential size under standard DC programming and supplies explicit constructive formulations for deep ReLU networks together with convergent batch and stochastic algorithms.
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TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation
TLRD distills tri-level rationales (instance features, dataset distributions, neighbor comparisons) from a teacher into student LLMs to close the accuracy gap with tree ensembles on tabular data while generating grounded explanations.
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Skew-adaptive conformal prediction
Develops a skew-adaptive split conformal prediction method that learns local skewness via a gauge-derived conformity score and an asinh residual model while preserving marginal validity under exchangeability.
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Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Model evaluation in supervised learning should be treated as a context-dependent, decision-oriented process aligned with operational objectives rather than relying on a small set of aggregate metrics.
- Online Learning-to-Defer with Varying Experts