Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
A query-optimal algorithm for finding counterfactuals
Authors: Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 22,227 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,904 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is theoretical in nature: we give an algorithm with strong bounds on its query complexity and all the counterfactuals that it returns are guaranteed to be optimal. We view our main contribution as proving the curse of dimensionality can be strongly evaded for a broad and natural class of models all monotone models. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University 2Department of Computer Science, Massachusetts Institute of Technology. |
| Pseudocode | Yes | Figure 1: Helper algorithm FINDMINIMAL Figure 2: Algorithm for ο¬nding optimal counterfactuals, using FINDMINIMAL as its main subroutine. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing its code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and proofs. It does not conduct experiments on datasets or mention any training data. |
| Dataset Splits | No | The paper is theoretical and focuses on algorithm design and proofs. It does not mention any validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs. It does not provide details about an experimental setup, such as hyperparameters or training settings. |