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..
Regression with Sensor Data Containing Incomplete Observations
Authors: Takayuki Katsuki, Takayuki Osogami
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the advantages of our algorithm through numerical experiments. and Extensive experiments on synthetic and six real-world regression tasks including a real use case for healthcare demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Industry | 1IBM Research Tokyo, Tokyo, Japan. Correspondence to: Takayuki Katsuki <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 U2 regression based on stochastic gradient method. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the availability of its own source code for the methodology described. |
| Open Datasets | Yes | We use three synthetic tasks, Low Noise, High Noise, and Breathing, collected from the Kaggle dataset (Sen, 2016). and We next apply the proposed method and baselines to five different real-world healthcare tasks from the UCI Machine Learning Repository (Velloso, 2013; Velloso et al., 2013) |
| Dataset Splits | Yes | We conducted 5-fold cross-validation, each with a different randomly sampled training-testing split. For evaluation purposes, we do not include incomplete observations in these test sets. For each fold of the cross-validation, we use a randomly sampled 20% of the training set as a validation set to choose the best hyperparameters for each algorithm. |
| Hardware Specification | Yes | All of the experiments were carried out with a Python and Tensor Flow implementation on workstations having 80 GB of memory, a 4.0 GHz CPU, and an Nvidia Titan X GPU. |
| Software Dependencies | No | The paper mentions using 'Python and Tensor Flow implementation' but does not specify version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | We set the candidates of the hyperparameters, ρ and λ, to {10−3, 10−2, 10−1, 100}. and We used Adam with the hyperparameters recommended in (Kingma & Ba, 2015), and the number of samples in the mini-batches was set to 32. and We used a 6-layer multilayer perceptron with Re LU (Nair & Hinton, 2010) (more specifically, D-100-100-100-1) as f(x) |