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..
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation
Authors: Yuyang Deng, Ilja Kuzborskij, Mehrdad Mahdavi
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the effectiveness of our proposed mixture weights estimation algorithm, we conducted an experiment using MNIST dataset [1] according to the following specifications. |
| Researcher Affiliation | Collaboration | Yuyang Deng Pennsylvania State University EMAIL Ilja Kuzborskij Google Deep Mind EMAIL Mehrdad Mahdavi Pennsylvania State University EMAIL |
| Pseudocode | Yes | Algorithm 1: Mixture Weight Estimation; Algorithm 2: Learning w function by a neural network; Algorithm 3: GD(v, α, K); Algorithm 4: Label efficient nonparametric online regression |
| Open Source Code | No | The paper does not provide any statement about making its source code available or include a link to a code repository for its proposed methods. |
| Open Datasets | Yes | To demonstrate the effectiveness of our proposed mixture weights estimation algorithm, we conducted an experiment using MNIST dataset [1]... |
| Dataset Splits | No | The paper mentions 80 for training and 20 for testing samples for each domain but does not explicitly state a validation split. No specific percentages or counts are provided for a validation set. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments, such as CPU or GPU models, or memory. |
| Software Dependencies | No | The paper mentions implementing experiments with a "two-layer MLP neural network" but does not specify any software libraries (e.g., TensorFlow, PyTorch) or their version numbers. |
| Experiment Setup | No | The paper mentions using a "two-layer MLP neural network" but does not provide specific hyperparameters such as learning rates, batch sizes, or optimizer details used in the experimental setup. |