Research map / Learning theory and algorithms / Prediction and decision losses
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| tree-based sampling replaces linear-in-N dual sampling for repeated DPP sampling | DPP sampler · Movie recommendation (MovieLens, N=26744, D=30) | time per sample: 42.2 ms to 3.2 ms | Helped |
| personalized tree-based sampling replaces personalized dual sampling | personalized DPP sampler · Movie recommendation (MovieLens, N=26744, D=30) | time per sample: 52.2 ms to 5.8 ms | Helped |
| approximate sampling (epsilon=0.4) on top of personalized tree sampling (ablation) | personalized tree sampler · Random subset of 5000 movies | sampling speed relative to exact: about 1.5x faster | Helped |
| approximate sampling via uniform short-circuit (epsilon=0.1) (ablation) | personalized tree sampler · Synthetic, N=5000, k=5, n=5 (Figure 3) | runtime (s): Approx. significantly faster than Exact under all conditions | Helped |
| tree-based sampling vs dual sampling on synthetic data with large N | DPP sampler · N=10^5, D=10, k=10 (Figure 2) | sampling speedup: over 300x speedup reported in abstract | Helped |
Closest papers by meaning
- Maximizing Induced Cardinality Under a Determinantal Point Process (NeurIPS 2018 · similarity 0.90)
- Sampling from a k-DPP without looking at all items (NeurIPS 2020 · similarity 0.90)
- Low-Rank Factorization of Determinantal Point Processes (AAAI 2017 · similarity 0.89)
- Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity (NeurIPS 2018 · similarity 0.89)
- State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives (NeurIPS 2026 · similarity 0.88)
- Small coresets via negative dependence: DPPs, linear statistics, and concentration (NeurIPS 2024 · similarity 0.84)
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