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..
Improving Optimization for Models With Continuous Symmetry Breaking
Authors: Robert Bamler, Stephan Mandt
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed Goldstone-GD optimization algorithm on the three example models introduced in Section 3.2. We compare Goldstone-GD to standard GD, to Ada Grad (Duchi et al., 2011), and to Adam (Kingma & Ba, 2014). |
| Researcher Affiliation | Industry | Robert Bamler 1 Stephan Mandt 1 1Disney Research, Glendale, CA, USA. Correspondence to: Robert Bamler <EMAIL>, Stephan Mandt <EMAIL>. |
| Pseudocode | Yes | Algorithm 1: Goldstone Gradient Descent (Goldstone-GD) |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We fit the sparse dynamic Bernoulli factorization model defined in Eqs. 6-9 in Section 3.2 to the Movielens 20M data set2 (Harper & Konstan, 2016). We fit the model to digitized books from the years 1800 to 2008 in the Google Books corpus3 (Michel et al., 2011) |
| Dataset Splits | Yes | We split randomly across all bins into 50% training, 20% validation, and 30% test set. |
| Hardware Specification | No | The paper mentions 'embedding dimension to d = 100 due to hardware constraints' but does not provide specific details about the hardware used for the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions optimizers like 'Ada Grad' and 'Adam' but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We use T = 30 time steps and a coupling strength of λ = 10. We train the model with standard GD (baseline) and with Goldstone-GD with k1 = 50 and k2 = 10. We find fastest convergence for the baseline method if we clip the gradients to an interval [ g, g] and use a decreasing learning rate ρs = ρ0( s/(s+ s))0.7 despite the noise-free gradient. Here, s is the training iteration. We optimize the hyperparameters for fastest convergence in the baseline and find g = 0.01, ρ0 = 1, and s = 100. |