Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Kernel Mean Estimation via Spectral Filtering
Authors: Krikamol Muandet, Bharath Sriperumbudur, Bernhard Schölkopf
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The paper includes a dedicated section '5 Empirical studies' which describes experiments on synthetic and real data, presenting results in figures and tables, and discussing performance metrics like risk and negative log-likelihood, thus indicating experimental research. |
| Researcher Affiliation | Academia | Krikamol Muandet MPI-IS, T ubingen EMAIL Bharath Sriperumbudur Dept. of Statistics, PSU EMAIL Bernhard Sch olkopf MPI-IS, T ubingen EMAIL |
| Pseudocode | Yes | Table 1: Update equations for β and corresponding filter functions. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | The datasets were taken from the UCI repository1 and pre-processed by standardizing each feature. 1http://archive.ics.uci.edu/ml/ |
| Dataset Splits | Yes | For these two algorithms, we evaluate the leave-one-out score and select βt at the iteration t that minimizes this score (see, e.g., Figure 3(a)). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | To allow for an analytic calculation of the loss L(β, X, P), we assume that the distribution P is a d-dimensional mixture of Gaussians [1, 8]. Specifically, the data are generated as follows: x P4 i=1 πi N(θi, Σi)+ε, θij U( 10, 10), Σi W(3 Id, 7), ε N(0, 0.2 Id) where U(a, b) and W(Σ0, df) are the uniform distribution and Wishart distribution, respectively. As in [1], we set π = [0.05, 0.3, 0.4, 0.25]. Throughout, we use the Gaussian RBF kernel k(x, x ) = exp( x x 2/2σ2) whose bandwidth parameter is calculated using the median heuristic, i.e., σ2 = median{ xi xj 2}. The parameters (πj, θj, σ2 j ) are initialized by the best one obtained from the K-means algorithm with 50 initializations. Throughout, we set r = 5 and use 25% of each dataset as a test set. |