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..
Conformal Prediction Sets with Limited False Positives
Authors: Adam Fisch, Tal Schuster, Tommi Jaakkola, Dr.Regina Barzilay
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of this approach across a number of classification tasks in natural language processing, computer vision, and computational chemistry. and We now present our empirical results. |
| Researcher Affiliation | Collaboration | 1CSAIL, Massachusetts Institute of Technology. 2Google Research. |
| Pseudocode | Yes | Algorithm 1 Pseudocode for conformal prediction with limited false positives (in expectation case, see Eq. (1)). |
| Open Source Code | Yes | Our code is publicly available at https: //github.com/ajfisch/conformal-fp. |
| Open Datasets | Yes | All datasets and base models used in this paper are publicly available (see 5.1 and Appendix C for details). and We use the Ch EMBL database (Mayr et al., 2018)... We use the MS-COCO dataset (Lin et al., 2014)... We use the Co NLL NER dataset (Tjong Kim Sang and De Meulder, 2003)... |
| Dataset Splits | Yes | For each task we learn all models on a training set, perform model selection on a validation set, and report final results as the average over 1000 random trials on a test set, where in each trial we partition the data into 80% calibration (x1:n) and 20% prediction points (xn+1). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper refers to external models and repositories used (e.g., chemprop3, EfficientDet, PURE, Albert-base) but does not list specific version numbers for its own software dependencies such as Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | We use the official code repository and the following parameters: 1e 5 learning rate, 5e 4 task learning rate, 32 train batch size, and 100 context window. |