Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Connecting Jensen–Shannon and Kullback–Leibler Divergences: A New Bound for Representation Learning
Authors: Reuben Dorent, Polina Golland, William (Sandy) Wells
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 31,160 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,436 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 | Extensive experiments demonstrate that our lower bound is tight when applied to MI estimation. We compared our lower bound to state-of-the-art neural estimators of variational lower bound across a range of established reference scenarios. Our lower bound estimator consistently provides a stable, low-variance estimate of a tight lower bound on MI. We also demonstrate its practical usefulness in the context of the Information Bottleneck framework. |
| Researcher Affiliation | Academia | Reuben Dorent Inria EMAIL Polina Golland MIT EMAIL William Wells III Harvard, MIT EMAIL |
| Pseudocode | Yes | Algorithm 1: Algorithmic implementation of the Ξ function as the inverse of its known inverse Ξ^-1 |
| Open Source Code | Yes | Implementation details2 are available in Appendix E.2. 2https://github.com/Reuben Do/JSDlowerbound |
| Open Datasets | Yes | Table 1: Generalization performance (%) on MNIST dataset. Performance is evaluated by the mean classification accuracy on the MNIST test set after training on the MNIST training set. |
| Dataset Splits | Yes | Table 1: Generalization performance (%) on MNIST dataset. Performance is evaluated by the mean classification accuracy on the MNIST test set after training on the MNIST training set. |
| Hardware Specification | Yes | The computational time analysis is developed on a server with CPU Intel Xeon Platinum 8468 48-Core Processor and an NVIDIA GPU H100 and reported in Table 7. |
| Software Dependencies | No | The paper does not explicitly mention specific software dependencies with version numbers like Python, PyTorch, or CUDA versions. It mentions using 'Adam optimizer' but not with a specific version or framework. |
| Experiment Setup | Yes | Training is performed for 4000 steps using the Adam optimizer and batch size N = 64, matching the architecture and hyperparameters from prior work for comparability. As the function Ξ is strictly increasing, maximizing Ξ (log 2 LCE) is equivalent to minimizing the cross-entropy loss LCE. Therefore, the approximation of Ξ is not used during optimization. Implementation details2 are available in Appendix E.2. |