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 Tagging with Markov Chain Optimization
Authors: Nir Rosenfeld, Amir Globerson
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,172 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,168 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the effectiveness of our method, we perform experiments on three tagging datasets, and show that the greedy algorithm outperforms other baselines. |
| Researcher Affiliation | Academia | Nir Rosenfeld School of Computer Science and Engineering Hebrew University of Jerusalem EMAIL Amir Globerson The Blavatnik School of Computer Science Tel Aviv University EMAIL |
| Pseudocode | Yes | Algorithm 1 SIMPLEGREEDYTAGOPT(Q, Q+, π, k) See supp. for efficient implementation |
| Open Source Code | No | The paper mentions "See supp. for efficient implementation" for Algorithm 1 but does not explicitly state that source code is released or provide a link to a repository. |
| Open Datasets | Yes | collected from Last.fm, Delicious, and Movielens by the Het Rec 2011 workshop [3]. |
| Dataset Splits | No | The paper describes generating problem instances and evaluating performance but does not specify a distinct validation set or its split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (CPU, GPU models, etc.) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies or their version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | Transition probabilities from tags to items were set to be proportional to the item weights... The initial distribution was set to be uniform over the set of candidate tags, and the transition probability from items to was set to ϵ = 0.1. |