Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
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2026 2verdicts
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An oracle-optimal decision policy for informative conformal prediction sets is calibrated to ensure finite-sample FCR control and delivers higher power than prior methods on classification tasks.
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Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
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Selecting Informative Conformal Prediction Sets with an Optimized FCR-Controlled Approach
An oracle-optimal decision policy for informative conformal prediction sets is calibrated to ensure finite-sample FCR control and delivers higher power than prior methods on classification tasks.