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.
Optimal Algorithms for Learning Partitions with Faulty Oracles
Authors: Adela DePavia, Olga Medrano Martin del Campo, Erasmo Tani
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 31,818 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,942 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 | We design algorithms for this task and prove that they achieve optimal query complexity. |
| Researcher Affiliation | Academia | Adela Frances De Pavia University of Chicago EMAIL Olga Medrano Martín del Campo University of Chicago EMAIL Erasmo Tani University of Chicago EMAIL |
| Pseudocode | Yes | Algorithm 1: Learn(V, α, k, ℓyes) |
| Open Source Code | No | The paper is theoretical and does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experiments or datasets, thus no information about training data availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments or datasets, thus no information about validation splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs rather than empirical experiments, so no experimental setup details are provided. |