Research map / Learning theory and algorithms / Submodular and game algorithms
Diversity Maximization in the Presence of Outliers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GREEDY's candidate-based outlier identification instead of BASELINE's nearest-neighbor scan over every point | Max-Min diversification with outliers · FCT, Household, KDD99, Mirai | time [sec]: 312.489 -> 2.348; 391.429 -> 6.962; 360.946 -> 2.135; 485.57 -> 31.046 | Helped |
| Use GREEDY's candidate-based outlier identification instead of BASELINE's nearest-neighbor scan over every point | Max-Min diversification with outliers · FCT, Household, KDD99, Mirai | div(S): 51.514 -> 51.514; 38.999 -> 38.999; 80.281 -> 80.281; 113.460 -> 113.460 | No clear effect |
| Use CORESET for solution computation instead of BASELINE | Max-Min diversification with outliers · FCT, Household, KDD99, Mirai | time [sec]: 312.489 -> 0.005; 391.429 -> 0.006; 360.946 -> 0.009; 485.57 -> 0.098 | Helped |
| Use CORESET instead of BASELINE | Max-Min diversification with outliers · FCT, Household, KDD99, Mirai | div(S): 51.514 -> 50.158; 38.999 -> 38.369; 80.281 -> 77.064; 113.460 -> 106.352 | Mixed |
| Use STREAMING instead of BASELINE | Max-Min diversification with outliers · FCT, Household, KDD99, Mirai | time [sec]: 312.489 -> 1.874; 391.429 -> 5.165; 360.946 -> 1.574; 485.57 -> 20.955 | Helped |
| Use STREAMING instead of BASELINE | Max-Min diversification with outliers · FCT, Household, KDD99, Mirai | div(S): 51.514 -> 49.614; 38.999 -> 37.374; 80.281 -> 79.996; 113.460 -> 95.439 | Hurt |
Closest papers by meaning
- Out-Of-Distribution Detection with Diversification (Provably) (NeurIPS 2024 · similarity 0.85)
- Graph-Based Algorithms for Diverse Similarity Search (ICML 2025 · similarity 0.84)
- Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures (ICML 2025 · similarity 0.84)
- Improved Guarantees for Fully Dynamic $k$-Center Clustering with Outliers in General Metric Spaces (NeurIPS 2024 · similarity 0.84)
- DOS: Diverse Outlier Sampling for Out-of-Distribution Detection (ICLR 2024 · similarity 0.84)
- Near-Linear Time Approximation Algorithms for k-means with Outliers (ICML 2024 · similarity 0.84)
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