Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data
Authors: Xin Zou, Weiwei Liu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments on simulated data to empirically verify the correctness of our theory and the validity of our proposed method. |
| Researcher Affiliation | Academia | School of Computer Science, Institute of Artificial Intelligence, National Engineering Research Center for Multimedia Software, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, China {zouxin2021, liuweiwei863}@gmail.com |
| Pseudocode | No | The paper describes methods in prose and mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a link to a code repository or explicitly state that the source code for their methodology is publicly available. |
| Open Datasets | No | The paper uses 'simulated data' and describes its generation, such as 'We sample mtrain training examples from S to train a linear predictor', but does not provide any concrete access information (link, DOI, specific repository, or formal citation to an established public dataset) for this data. |
| Dataset Splits | No | The paper mentions 'mtrain training examples' and 'mtest examples from T to form the test data' but does not specify validation splits or detailed percentages/counts for training, validation, and test sets. It does not provide the specific dataset split information needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not list any specific software dependencies with their version numbers that would be needed for replication. |
| Experiment Setup | No | The paper describes the setup for generating simulated data, including distributions and parameters like µs, σs,x, σs,y, but it does not provide specific hyperparameter values or detailed training configurations (e.g., learning rates, batch sizes, optimizers, or number of epochs) for the linear predictors or confidence set construction. |