Distributional Rank Aggregation, and an Axiomatic Analysis
Authors: Adarsh Prasad, Harsh Pareek, Pradeep Ravikumar
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments to demonstrate this fact... Figure 1 shows the probability of success against the weight value w for both experiments. |
| Researcher Affiliation | Academia | Department of Computer Science, The University of Texas, Austin, TX 78712, USA |
| Pseudocode | No | The paper describes algorithms and procedures in text, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | No | The paper generates data using "a mixture of two Mallows models" for its experiments, rather than using a pre-existing publicly available dataset with a specific access link or citation. |
| Dataset Splits | No | The paper describes the generation of data for experiments but does not specify training, validation, or test splits. It focuses on simulating distributions and aggregating them. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers, such as programming languages, libraries, or frameworks used for implementation or experimentation. |
| Experiment Setup | Yes | In Experiment 1, we fix centers Z1 = {D, E, A, B, C} and Z2 = {B, C, D, E, A}, while in Experiment 2 we fix centers Z1 = {A, B, C, D, E} and Z2 = {B, C, D, E, A}. Then, for φ = 0.8, we vary w from 0.51 to 1.0. |