Signature transforms approximate path-dependent nonlinear rewards as linear functionals, enabling the DisSigUCB algorithm with a high-probability regret bound of order O(sqrt((d+m)KT)).
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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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Signature Approach for Contextual Bandits with Nonlinear and Path-dependent Rewards
Signature transforms approximate path-dependent nonlinear rewards as linear functionals, enabling the DisSigUCB algorithm with a high-probability regret bound of order O(sqrt((d+m)KT)).
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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.
- Online Goal Recognition using Path Signature and Dynamic Time Warping