KARMA constructs minimal-K Markov transition kernels as surrogates to deliver global explanations for multivariate time series forecasting models and recovers known causal structure on synthetic data.
Explainable and Interpretable Forecasts on Non-Smooth Multivariate Time Series for Responsible Gameplay , url=
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Framework for dataset subset selection via clustering, A/D-optimality, and FAFI with bootstrap intervals to preserve model rankings, showing high Spearman correlation (0.95 with 5 datasets) in TSC but limited gains in recommender systems.
Introduces Bradley-Terry based ranking of recommender algorithms that varies with dataset statistics, includes a consistency metric, and extends to unseen datasets via BT trees and covariate models.
citing papers explorer
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Global Explanations for Multivariate Time Series Forecasting Models via $K$-Order Markov Approximations
KARMA constructs minimal-K Markov transition kernels as surrogates to deliver global explanations for multivariate time series forecasting models and recovers known causal structure on synthetic data.
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Benchmarking on Tasks That Matter: Dataset Selection for Preserving Model Rankings
Framework for dataset subset selection via clustering, A/D-optimality, and FAFI with bootstrap intervals to preserve model rankings, showing high Spearman correlation (0.95 with 5 datasets) in TSC but limited gains in recommender systems.
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Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
Introduces Bradley-Terry based ranking of recommender algorithms that varies with dataset statistics, includes a consistency metric, and extends to unseen datasets via BT trees and covariate models.