Linear Label Ranking with Bounded Noise
Authors: Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis, Christos Tzamos
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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-𝑟disagreement 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 fotakis@cs.ntua.gr Alkis Kalavasis NTUA kalavasisalkis@mail.ntua.gr Vasilis Kontonis UW Madison kontonis@wisc.edu Christos Tzamos UW Madison tzamos@wisc.edu |
| 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. |