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..
Efficient optimization of loops and limits with randomized telescoping sums
Authors: Alex Beatson, Ryan P Adams
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our adaptive RT estimators on a range of applications including meta-optimization of learning rates, variational inference of ODE parameters, and training an LSTM to model long sequences. Section 7 presents experimental results. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Princeton University, Princeton, NJ, USA. |
| Pseudocode | Yes | Appendix A presents algorithm pseudocode. |
| Open Source Code | Yes | Code may be found at https://github.com/PrincetonLIPS/randomized_telescopes. |
| Open Datasets | Yes | We next experiment with meta-optimization of a learning rate on MNIST. Finally, we study a high-dimensional optimization problem: training an LSTM to model sequences on enwik8. |
| Dataset Splits | Yes | There are 205 unique tokens. We use the ο¬rst 90M, 5M, and 5M characters as the training, evaluation, and test sets. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware components (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Lotka-Volterra ODE: 'batch size of 64... and a learning rate of 0.01. Evaluation is performed with a batch size of 512.' MNIST: 'Optimization is performed with a batch size of 100.' 'The outer optimization is performed with a learning rate of 0.01.' enwik8 LSTM: 'The optimization is performed with a learning rate of 2.2.' General: 'the tuning frequency K is set to 5, and the exponential moving average weight Ξ± is set to 0.9.' |