Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Doubly Decomposing Nonparametric Tensor Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use nonparametric local-function decomposition (AMNR) instead of flattened tensor GP (TGP) | tensor regression · Synthetic low-rank and full-rank data; Enron epidemic spreading | MSE | Helped |
| Replace linear tensor regression (TLR) with nonparametric AMNR | tensor regression · Epidemic spreading (Enron) | testing MSE | Helped |
| Truncate number of local functions to small M (M=1 or 2) rather than large M (ablation) | AMNR · Full-rank synthetic data | testing MSE | Helped |
| Increase CP rank R beyond a small value (ablation) | AMNR · Synthetic low-rank data sensitivity of R | MSE | No clear effect |
| Model rank-additive local functions (AMNR) on full-rank data where model is misspecified | AMNR · Full-rank synthetic data | MSE | Helped |
Closest papers by meaning
- Nonparametric Decomposition of Sparse Tensors (ICML 2021 · similarity 0.91)
- Efficient Nonparametric Tensor Decomposition for Binary and Count Data (AAAI 2024 · similarity 0.89)
- Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data (ICLR 2024 · similarity 0.88)
- High-Dimensional Tensor Regression With Oracle Properties (ICML 2025 · similarity 0.88)
- Generalized Tensor Decomposition for Understanding Multi-Output Regression under Combinatorial Shifts (NeurIPS 2024 · similarity 0.87)
- Low-Rank Tensor Transitions (LoRT) for Transferable Tensor Regression (ICML 2025 · similarity 0.86)
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