Research map / Learning theory and algorithms / Sample complexity
Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Reduce robust Bayesian-network learning to robust mean estimation and use a sparse-input mean estimator | Fixed-structure Bayesian networks · Fixed-structure binary Bayesian networks | running time: Previous: \Omega(N d^2/{\epsilon}); proposed: \widetilde O(N d) | Helped |
| Use robust mean estimation on sparse expanded samples | Robust Bayesian-network learning · Fixed-structure binary Bayesian networks | total variation distance: O( {\epsilon}\sqrt{\log(1/{\epsilon})}/ \sqrt{\alpha c}) | Helped |
| Use a sparse-input robust mean estimation algorithm | Robust mean estimation · Robust mean estimation with sparse input | running time: \widetilde O(\log R \cdot (\mathrm{nnz}(S) + N + d)) | Helped |
Closest papers by meaning
- Robust Learning of Fixed-Structure Bayesian Networks (NeurIPS 2018 · similarity 0.97)
- Distributionally Robust Skeleton Learning of Discrete Bayesian Networks (NeurIPS 2023 · similarity 0.90)
- Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity (NeurIPS 2017 · similarity 0.89)
- Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization (CVPR 2021 · similarity 0.87)
- Extendable and Iterative Structure Learning Strategy for Bayesian Networks (ICLR 2025 · similarity 0.87)
- Adaptive Robust Learning using Latent Bernoulli Variables (ICML 2024 · similarity 0.86)
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