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..
Deep Learning Games
Authors: Dale Schuurmans, Martin A. Zinkevich
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To investigate the utility of these methods for supervised learning, we conducted experiments on synthetic data and on the MNIST data set [20]. |
| Researcher Affiliation | Collaboration | Dale Schuurmans Google EMAIL Martin Zinkevich Google EMAIL Work performed at Google Brain while on a sabbatical leave from the University of Alberta. |
| Pseudocode | Yes | Algorithm 1 Main Loop, Algorithm 2 Regret Matching (RM), Algorithm 3 Exp. Weighted Average (EWA), Algorithm 4 Projected SGD |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. |
| Open Datasets | Yes | We conducted experiments on synthetic data and on the MNIST data set [20]. ... The third experiment was conducted on MNIST, which is an n = 10 class problem over m = 784 dimensional inputs with T = 60, 000 training examples, evidently not linearly separable. |
| Dataset Splits | No | The paper mentions 'training loss' and 'test loss' and refers to 60,000 training examples for MNIST, but it does not explicitly define a separate 'validation' split or its size, nor does it refer to a standard three-way split that includes validation. |
| Hardware Specification | No | The paper mentions a "Tensorflow implementation" but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Tensorflow implementation" but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | For this experiment, we used mini-batches of size 100. ... Here we chose the L1 constraint bound to be β = 10 and the initialization scale as σ = 100. For the nonlinear activation functions we used a smooth approximation of the standard Re LU gate fv(x) = τ log(1 + ex/τ) with τ = 0.5. ...RM was run with β = 30 and initialization scales (σ1, σ2, σ3) = (50, 200, 50). ...where RM was run with (β1, β2, β3, β4) = (30, 30, 30, 10) and initialization scales σ = 500. |