Reconstructing Hidden Permutations Using the Average-Precision (AP) Correlation Statistic
Authors: Lorenzo De Stefani, Alessandro Epasto, Eli Upfal, Fabio Vandin
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical analysis with extensive experiments showing that unsupervised methods based on our model can precisely identify ground-truth clusters of rankings in real-world data. In particular, when compared to the Kendall s tau based methods, our methods are less affected by noise in low-rank items. We experimentally evaluate our model and algorithms with both synthetic and real-world data. |
| Researcher Affiliation | Academia | Lorenzo De Stefani, Alessandro Epasto, Eli Upfal Brown University Providence, RI 02906, United States {lorenzo, epasto, eli}@cs.brown.edu Fabio Vandin University of Padova Padova, PD 35131, Italy vadinfa@dei.unipd.it |
| Pseudocode | No | The paper describes the 'MAP (β, π) Generative Process' in a step-by-step manner, but it is not formally labeled as 'Pseudocode' or 'Algorithm' and lacks typical pseudocode formatting. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We split each dataset in training and testing sets using 5-fold cross-validation. We applied the following simple classification algorithm. For each class we reconstructed the center permutation using our comparison-based algorithm (in Section 5.2) on the set of permutations belonging to that class in the training set. |
| Dataset Splits | Yes | We split each dataset in training and testing sets using 5-fold cross-validation. |
| Hardware Specification | No | The paper states 'Each run used a single core and less than 4GB of RAM' but does not specify any hardware models (e.g., CPU, GPU) or processing units. |
| Software Dependencies | No | The paper states 'We implemented the algorithms in C++' but does not provide specific version numbers for C++ compilers or any other software libraries used. |
| Experiment Setup | Yes | In this experiment, we generated, using the algorithm defined in Section 4, a set P of i.i.d. permutations of size n = 100 from the MAP (β, π) model with different settings of β and size of |P|. We set α = 0.15 as the jump-back probability in all the walks. We selected t = 200 u.a.r. nodes for each of the eight well-represented categories in the Wikipedia taxonomy. We split each dataset in training and testing sets using 5-fold cross-validation. |