Research map / Learning theory and algorithms / Matrix and tensor methods
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use scalable MCMC instead of exact rejection sampling | 10-NDPP sampling · UK Retail | runtime (seconds): 406 -> 25.4 | Helped |
| Use scalable MCMC instead of exact rejection sampling | 10-NDPP sampling · Recipe | runtime (seconds): 2.1 -> 14.5 | Hurt |
| Use scalable MCMC instead of exact rejection sampling | 10-NDPP sampling · Instacart | runtime (seconds): 93.7 -> 21.0 | Helped |
| Use scalable MCMC instead of exact rejection sampling | 10-NDPP sampling · Million Song | runtime (seconds): 0.13 -> 9.5 | Hurt |
| Use scalable MCMC instead of exact rejection sampling | 10-NDPP sampling · Book | runtime (seconds): 0.46 -> 23.7 | Hurt |
| Use scalable MCMC instead of exact rejection sampling | 50-NDPP sampling · UK Retail | runtime (seconds): ${}^{(*)}$5.11$\times$10$^{12}$ -> 334 | Helped |
| Use scalable MCMC instead of exact rejection sampling | 50-NDPP sampling · Recipe | runtime (seconds): ${}^{(*)}$9.55$\times$10$^{5}$ -> 229 | Helped |
| Use scalable MCMC instead of exact rejection sampling | 50-NDPP sampling · Instacart | runtime (seconds): ${}^{(*)}$9.50$\times$10$^{5}$ -> 242 | Helped |
| Use scalable MCMC instead of exact rejection sampling | 50-NDPP sampling · Million Song | runtime (seconds): ${}^{(*)}$1.45$\times$10$^{12}$ -> 488 | Helped |
| Use scalable MCMC instead of exact rejection sampling | 50-NDPP sampling · Book | runtime (seconds): ${}^{(*)}$4.06$\times$10$^6$ -> 374 | Helped |
| Use scalable MCMC instead of exact Cholesky sampling | Unconstrained NDPP sampling · Million Song | runtime (seconds): 537 -> 281 | Helped |
| Use scalable MCMC instead of exact Cholesky sampling | Unconstrained NDPP sampling · Book | runtime (seconds): 1540 -> 80 | Helped |
Closest papers by meaning
- Scalable Sampling for Nonsymmetric Determinantal Point Processes (ICLR 2022 · similarity 0.96)
- Sampling from a k-DPP without looking at all items (NeurIPS 2020 · similarity 0.92)
- Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes (ICLR 2021 · similarity 0.91)
- State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives (NeurIPS 2026 · similarity 0.89)
- Small coresets via negative dependence: DPPs, linear statistics, and concentration (NeurIPS 2024 · similarity 0.87)
- Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers (ICLR 2024 · similarity 0.87)
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