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..
Probabilistic Line Searches for Stochastic Optimization
Authors: Maren Mahsereci, Philipp Hennig
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments were performed on the well-worn problems of training a 2-layer neural net with logistic nonlinearity on the MNIST and CIFAR-10 datasets. [...] Fig. 4, top, shows test errors after 10 epochs as a function of the initial learning rate α0 (error bars based on 20 random re-starts). |
| Researcher Affiliation | Academia | Maren Mahsereci and Philipp Hennig Max Planck Institute for Intelligent Systems Spemannstraße 38, 72076 T ubingen, Germany [mmahsereci|phennig]@tue.mpg.de |
| Pseudocode | No | The paper describes the algorithm in prose and mathematical formulations but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | Our matlab implementation will be made available at time of publication of this article. |
| Open Datasets | Yes | Our experiments were performed on the well-worn problems of training a 2-layer neural net with logistic nonlinearity on the MNIST and CIFAR-10 datasets. [...] http://yann.lecun.com/exdb/mnist/ and http://www.cs.toronto.edu/ kriz/cifar.html. |
| Dataset Splits | No | The paper mentions 'batches of size m = 10' and 'test errors' but does not specify details for a separate validation split, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions 'Our matlab implementation' but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | We then trained networks with vanilla SGD with and without α-decay (using the schedule α(i) = α0/i), and SGD using the probabilistic line search, with α0 ranging across five orders of magnitude, on batches of size m = 10. [...] In our networks, constant learning rates of α = 0.75 and α = 0.08 for MNIST and CIFAR-10, respectively, achieved the lowest test error after the first 103 steps of SGD. |