# Learning Gaussian (Bounded Cov.) on Gaussian Distribution Bounded Covariance pure-DP (train test)

Source: [Learning Gaussian (Bounded Cov.) on Gaussian Distribution Bounded Covariance pure-DP (train test)](<https://www.sota2.com/research/sota/learning-gaussian-bounded-cov-on-gaussian-distribution-bounded-covariance-pure-dp-train-test>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-26T05:21:12.199Z

Evaluation of algorithms for learning the parameters of a Gaussian distribution where the covariance matrix is known to be bounded, specifically using the 'pure-DP (train test)' subset of the dataset, focusing on complexity metrics.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Learning Gaussian (Bounded Cov.) |
| Dataset (including any stated split) | Gaussian Distribution Bounded Covariance pure-DP (train test) |
| Metric | Complexity (Symbolic) |
| Unit | OTHER |
| Direction | Lower is better |
| Results loaded | 0 of 0 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [Complexity (Symbolic)](<https://www.sota2.com/research/sota/learning-gaussian-bounded-cov-on-gaussian-distribution-bounded-covariance-pure-dp-train-test.md?metric=Complexity%20(Symbolic)>): Lower is better; unit: OTHER.

## Recorded results

| Method | Complexity (Symbolic) | Result date | Report paper |
| --- | --- | --- | --- |

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