ReTimeCausal is a new EM-based alternating optimization method for causal discovery from irregularly sampled time series that claims consistency guarantees under high missingness.
Regression shrinkage and selection via the lasso
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
Refined probabilistic and smooth l0 pruning techniques approximate minimum description length for neural networks, achieving high compression with minimal accuracy loss and empirically verifying better sample efficiency and generalization on image and text tasks.
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Causal Discovery for Irregularly Time Series with Consistency Guarantees
ReTimeCausal is a new EM-based alternating optimization method for causal discovery from irregularly sampled time series that claims consistency guarantees under high missingness.
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Efficient compression of neural networks and datasets
Refined probabilistic and smooth l0 pruning techniques approximate minimum description length for neural networks, achieving high compression with minimal accuracy loss and empirically verifying better sample efficiency and generalization on image and text tasks.