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..
Slicing Mutual Information Generalization Bounds for Neural Networks
Authors: Kimia Nadjahi, Kristjan Greenewald, Rickard Brรผel Gabrielsson, Justin Solomon
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 31,674 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,377 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 | Finally, we empirically validate our results and achieve the computation of non-vacuous information-theoretic generalization bounds for neural networks, a task that was previously out of reach. |
| Researcher Affiliation | Collaboration | 1MIT 2MIT-IBM Watson AI Lab; IBM Research. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide the code to reproduce the experiments2. 2Code is available here: https://github.com/ kimiandj/slicing_mi_generalization. |
| Open Datasets | Yes | We train fully-connected NNs to classify MNIST and CIFAR-10 datasets... We classify the Iris dataset (Fisher, 1936). |
| Dataset Splits | No | The paper mentions 'a random subset of MNIST with n = 1000 samples' for training and 'a test dataset of 10 000 samples' for evaluation, as well as 'We compute the test error on 20n/80 observations.' for logistic regression, indicating train/test splits. However, it does not explicitly define a separate validation split or its size/percentage. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer (Kingma & Ba, 2017) with default parameters (on PyTorch)' but does not specify the version of PyTorch or other software dependencies. |
| Experiment Setup | Yes | The network is trained for 200 epochs and a batch size of 64, using the Adam optimizer (Kingma & Ba, 2017) with default parameters... To train our NNs, we run Adam (Kingma & Ba, 2017) with default parameters for 30 epochs and batch size of 64 or 128... We use Adam with a learning rate of 0.1 as optimizer, for 200 epochs and batch size of 64... For each ฮ, we train for 20 epochs using the Adam optimizer with a batch size of 256, learning rate ฮท = 0.01 for w1 and ฮท/10 for w2, and other parameters set to their default values (Kingma & Ba, 2017). During training, we clamp the norm of each layer s weight matrix at the end of each iteration to satisfy the condition in Theorem B.2. |