Efficient preconditioned stochastic gradient descent for estimation in latent variable models
Authors: Charlotte Baey, Maud Delattre, Estelle Kuhn, Jean-Benoist Leger, Sarah Lemler
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate through relevant simulations the performance of the proposed methodology in a nonlinear mixed effects model and in a stochastic block model. |
| Researcher Affiliation | Academia | 1Univ. Lille, CNRS, UMR 8524 Laboratoire Paul Painlev e, F-59000 Lille, France 2Universit e Paris-Saclay, INRAE, Ma IAGE, 78350, Jouy-en-Josas, France. 3Universit e de technologie de Compi egne, CNRS, Heudiasyc, Compi egne, France 4Universit e Paris-Saclay, Centrale Sup elec, Math ematiques et Informatique pour la Complexit e et les Syst emes, 91190, Gif-sur-Yvette, France. |
| Pseudocode | Yes | Algorithm 1 Fisher-SGD in the independent case |
| Open Source Code | Yes | The code is available in the Git repository https: //github.com/baeyc/fisher-sgd-nlme. [...] The code is available in the Git repository https://gitlab.com/jbleger/sbm_with_ fisher-sgd. |
| Open Datasets | Yes | We applied our algorithm to a real dataset from a study on coucal growth rates (Goymann et al., 2016). |
| Dataset Splits | No | The paper uses simulated datasets for evaluation and does not describe explicit train/validation/test splits of a larger dataset in the traditional machine learning sense for model training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions the "R package saemix" and "Python module parametrization cookbook" but does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The authors propose to use Kpre-heating = 1000 and γ0 = 10 4. [...] Concerning the choice of α, to ensure Pk γk = + and Pk γ2 k < + , the authors propose to use α = 2/3. [...] The tuning parameters of the algorithm were set as follows: Kpre heating = 2000, K = 10000, Cheating = 100, α = 2/3, λ0 = 10 4, and the algorithm was initialized at a random value for θ. |