Sampling-based Multi-dimensional Recalibration
Authors: Youngseog Chung, Ian Char, Jeff Schneider
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our method and the quality of the recalibrated samples on a suite of benchmark datasets in multidimensional regression, a real-world dataset in modeling plasma dynamics during nuclear fusion reactions, and on a decision-making application in forecasting demand. |
| Researcher Affiliation | Academia | 1Machine Learning Department; 2Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213. |
| Pseudocode | Yes | Algorithm 1 HDR Recalibration: Training |
| Open Source Code | Yes | Code is available at: https://github.com/YoungseogChung/multi-dimensional-recalibration |
| Open Datasets | Yes | Datasets. The mulan benchmark (Tsoumakas et al., 2011) is a set of prediction tasks with multi-dimensional targets of up to 16 dimensions. |
| Dataset Splits | Yes | On each dataset, we make train-validation-test splits of proportions [65%, 20%, 15%] |
| Hardware Specification | Yes | All of the model training was done with 4 NVIDIA Ge Force RTX 2080 Ti GPUs. All of the evaluation was done on a CPU machine with Intel(R) Xeon(R) Gold 6238 CPU @ 2.10GHz. |
| Software Dependencies | No | The paper mentions software like 'Uncertainty Toolbox' and 'NGBoost' but does not specify version numbers for these or other key software dependencies required for replication. |
| Experiment Setup | Yes | For all of the datasets, the PNN trained has 5 fully connected layers, each with 200 hidden units, and the output parametrizes a diagonal Gaussian with a mean and a log-variance prediction. The Gaussian likelihood loss was used for training, with a learning rate of 0.001 and no weight decay was used. Training was halted early if the validation loss did not improve for more than 100 epochs, for a maximum of 1000 epochs. |