ROME: Robust Multi-Modal Density Estimator
Authors: Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora, Arkady Zgonnikov, Jens Kober
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compared our approach to state-of-the-art methods for density estimation as well as ablations of ROME, showing that it not only outperforms established methods but is also more robust to a variety of distributions.Our results demonstrate that ROME can overcome the issues of over-fitting and over-smoothing exhibited by other estimators. |
| Researcher Affiliation | Academia | Anna M esz aros , Julian F. Schumann , Javier Alonso-Mora , Arkady Zgonnikov and Jens Kober Cognitive Robotics, TU Delft, Netherlands {A.Meszaros, J.F.Schumann, J.Alonso Mora, A.Zgonnikov, J.Kober}@tudelft.nl |
| Pseudocode | Yes | Algorithm 1 ROME |
| Open Source Code | Yes | Source code: https://github.com/anna-meszaros/ROME |
| Open Datasets | Yes | A multivariate, 24-dimensional, and highly correlated distribution generated from a subset of the Forking Paths dataset [Liang et al., 2020] (Figure 3). |
| Dataset Splits | Yes | To test for over-fitting, we first sample two different datasets X1 and X2 (N samples each) from p (X1, X2 p). We then use the estimator f to create two queryable distributions bp1 = f(X1) and bp2 = f(X2). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For the hyperparameters pertaining to the clustering within ROME (see Section 3.1), we found empirically that stable results can be obtained using 199 possible clusterings, 100 for DBSCAN (Equation (3)) ε = min RN + α 2 (max(RN\{ }) min RN) | α {0, . . . , 99} o combined with 99 for ξ-clustering (Equation (4)) 100 | β {1, . . . , 99} , as well as using kmin = 5, kmax = 20, and αk = 400 for calculating kc (Equation (2) and Appendix C). |