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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Sample Complexity Bounds for Estimating Probability Divergences under Invariances
Authors: Behrooz Tahmasebi, Stefanie Jegelka
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 26,855 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,590 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments To observe the gain of invariances, we conduct a simple experiment on the following synthetic dataset. |
| Researcher Affiliation | Academia | 1MIT CSAIL 2TU Munich and MIT CSAIL. Correspondence to: Behrooz Tahmasebi <EMAIL>. |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | To observe the gain of invariances, we conduct a simple experiment on the following synthetic dataset. The input space is the six-dimensional flat torus T6 = [0, 1]6, and the group of invariances acts as the circular shifts on the last two coordinates; see Example 3 for more details about this action. We consider a non-invariant distribution µnon and an invariant distribution µinv in this setting as follows. Samples from the non-invariant distribution are generated based on the sum of three i.i.d. random variables X = X1+X2+X3, each chosen uniformly from [0, 1/3]6. Moreover, samples from the invariant distribution such as (X, X5, X6) are generated based on the sum of four i.i.d. random variables, denoted by X = X1 + X2 + X3 + X4, each chosen uniformly from [0, 0.25]4, and for the last two coordinates, X5, X6 are chosen uniformly from [0, 1]. |
| Dataset Splits | No | The paper describes a synthetic dataset and experiments but does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or versions used for the experiments. |
| Experiment Setup | No | Indeed, we didn t optimize the parameter T, and just used a fixed regularization parameter, which leads to having parallel plots in the logarithmic scale. |