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.
Linear Label Ranking with Bounded Noise
Authors: Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis, Christos Tzamos
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 22,963 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,072 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 | The main contributions of this paper are the first efficient algorithms for learning LSFs with bounded noise with respect to Kendall s Tau distance and top-๐disagreement loss. This paper is theoretical and does not have any negative social impact. [N/A] Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [N/A] Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? |
| Researcher Affiliation | Academia | Dimitris Fotakis NTUA EMAIL Alkis Kalavasis NTUA EMAIL Vasilis Kontonis UW Madison EMAIL Christos Tzamos UW Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 Non-proper Learning Algorithm Improper LSF and Algorithm 2 Proper Learning Algorithm Proper LSF |
| Open Source Code | No | The paper states it is theoretical and marks 'N/A' for questions related to code and experimental reproduction, indicating no open-source code for the described methodology is provided. |
| Open Datasets | No | The paper is theoretical and assumes `x` is sampled from a 'd-dimensional standard normal' distribution, but it does not mention the use of any real-world public datasets or provide access information for any dataset used in experiments. All experimental questions are marked N/A. |
| Dataset Splits | No | The paper is theoretical and does not describe any training, validation, or test dataset splits; all experimental questions are marked N/A. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments or provide details on hardware specifications. All experimental questions are marked N/A. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, therefore, it does not specify software dependencies with version numbers. All experimental questions are marked N/A. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and theoretical guarantees rather than experimental setups or hyperparameters. All experimental questions are marked N/A. |