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 [1].
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. |