Research map / Learning theory and algorithms / Prediction and decision losses
One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| online 2-neighbourhood local search with stash (Online-2-neighbor) vs Online-LSS and online greedy | online NDPP MAP inference · several real datasets (Figure 1) | solution quality (objective value) | Helped |
| online local search with stash (Online-LSS) vs online greedy | online NDPP MAP inference · several real datasets (Figure 1) | solution quality (objective value) | Helped |
| single-pass online algorithms vs offline greedy using k passes and full memory | online NDPP MAP inference · several real datasets (Figure 1) | solution quality (objective value) | No clear effect |
| partition-based streaming algorithm (Streaming Partition Greedy) vs other methods | streaming NDPP MAP inference · datasets in Section 7 | solution quality (objective value) | Hurt |
| smaller alpha (more swaps allowed) in Online-LSS (ablation) | Online-LSS · varied set sizes k (Appendix D) | solution quality | Mixed |
| random-order stream instead of adversarial order | Online-LSS and Online-2-neighbor · random permutation of datasets (Appendix D.3) | solution quality | No clear effect |
Closest papers by meaning
- Online MAP Inference of Determinantal Point Processes (NeurIPS 2020 · similarity 0.91)
- Faster Greedy MAP Inference for Determinantal Point Processes (ICML 2017 · similarity 0.90)
- Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes (ICLR 2021 · similarity 0.90)
- Lazy and Fast Greedy MAP Inference for Determinantal Point Process (NeurIPS 2022 · similarity 0.90)
- Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes (ICML 2022 · similarity 0.87)
- Convergence of Some Convex Message Passing Algorithms to a Fixed Point (ICML 2024 · similarity 0.85)
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