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..
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient
Authors: Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris Papailiopoulos
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify our results empirically by approximating a target network via SUBSETSUM in Experiment 1, and by pruning a sufficiently over-parameterized neural network that implements the structures in Figures 1b and 1c in Experiment 2. In both setups, we benchmark on the MNIST [33] dataset, and all training and pruning is accomplished with cosine annealing learning rate decay [34] on a batch size 64 with momentum 0.9 and weight decay 0.0005. |
| Researcher Affiliation | Academia | Ankit Pensia University of Wisconsin-Madison EMAIL Shashank Rajput University of Wisconsin-Madison EMAIL Alliot Nagle University of Wisconsin-Madison EMAIL Harit Vishwakarma University of Wisconsin-Madison EMAIL Dimitris Papailiopoulos University of Wisconsin-Madison EMAIL |
| Pseudocode | No | The paper describes mathematical proofs and experimental procedures in narrative text, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements or links indicating that the source code for the methodology described in the paper is publicly available. |
| Open Datasets | Yes | In both setups, we benchmark on the MNIST [33] dataset |
| Dataset Splits | No | The paper mentions training on MNIST and achieving a "final test set accuracy", but it does not explicitly provide details about training/validation/test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | Yes | The 397, 000 weights in our target network were approximated with 3, 725, 871 coefficients in 21.5 hours on 36 cores of a c5.18xlarge AWS EC2 instance. |
| Software Dependencies | No | The paper mentions using "Gurobi's MIP solver" and cites its reference manual from 2020, but it does not provide a specific version number (e.g., Gurobi X.Y) for the software dependencies used. |
| Experiment Setup | Yes | ...all training and pruning is accomplished with cosine annealing learning rate decay [34] on a batch size 64 with momentum 0.9 and weight decay 0.0005. |