Notice: 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. Full accuracy metrics and methodology are described in [1].
A Unified Approach to Online Matching with Conflict-Aware Constraints
Authors: Pan Xu, Yexuan Shi, Hao Cheng, John Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Yongxin Tong, Leonidas Tsepenekas2221-2228
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we propose two LP-based heuristics and test them against two natural baselines on both real and synthetic datasets. Our LP-based heuristics experimentally dominate the baseline algorithms, aligning with our theoretical predictions and supporting our unified approach. ... In this section, we present our experimental results. We test SAMP against several natural heuristic baselines on both the Meetup dataset (Liu et al. 2012) and synthetic datasets. |
| Researcher Affiliation | Academia | 1University of Maryland, College Park, USA 2BDBC, SKLSDE Lab and IRC, Beihang University, China 1EMAIL, 2EMAIL |
| Pseudocode | Yes | Algorithm 1: An LP-Based Sampling Algorithm (SAMP(α)) |
| Open Source Code | No | The paper does not contain any statements or links indicating that the source code for the proposed methodology is publicly available. |
| Open Datasets | Yes | We use the Meetup dataset from (Liu et al. 2012) collected between Oct. 2011 to Jan. 2012. ...Liu, X.; He, Q.; Tian, Y.; Lee, W.-C.; Mc Pherson, J.; and Han, J. 2012. Event-based social networks: Linking the online and offline social worlds. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1032 1040. |
| Dataset Splits | No | The paper describes how data is sampled for experiments (e.g., "randomly sample 50 events (U) and 100 users (V)" from the Meetup dataset), and how synthetic datasets are generated, but it does not specify explicit train/validation/test splits or mention cross-validation techniques. |
| Hardware Specification | Yes | We solve all LPs via the Glop Linear Solver6 on commodity hardware: an Intel Core i7-7700 (2.80 GHz) machine with 16GB of main memory. |
| Software Dependencies | No | The paper mentions using the "Glop Linear Solver6" but does not specify its version number or any other software components with their versions required for reproducibility. |
| Experiment Setup | Yes | In our experimental setup, we randomly sample 50 events (U) and 100 users (V) from the extracted data. ... We set capacity cu = Dcu and choose cv { 1/3 Dcv , Dcv} ... We set Nv = 2cv and Av = 2Nv. ... Set T = 1000 and for each time t [T], we generate a random vector {pv,t} such that each pv,t takes a uniform value from [0, 1] and P v pv,t = 1. (for Meetup dataset) ... In our experimental setup, we have three parameters (CU,T, p), where T is the number of online rounds. We generate a set of instances by varying one single parameter over a range and fixing all other parameter on the default value. (for Synthetic datasets) |