Online Matching with Controllable Rewards and Arrival Probabilities
Authors: Yuya Hikima, Yasunori Akagi, Naoki Marumo, Hideaki Kim
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In simulations on real data from crowdsourcing and ridesharing platforms, we show that the proposed algorithm can find solutions with high total rewards in practical times. |
| Researcher Affiliation | Industry | 1NTT Human Informatics Labratories, NTT Corporation, 2NTT Communication Science Laboratories, NTT Corporation |
| Pseudocode | Yes | Algorithm 1 Adaptive PDHG for (CP ) |
| Open Source Code | Yes | 1Codes/details of our experiments and the proof of Lemma 2 can be found in https://github.com/Yuya-Hikima/IJCAI2022-Online-Matching-with-Controllable-Rewards-and-Arrival-Probabilities. |
| Open Datasets | Yes | We used an open crowdsourcing dataset [Buckley et al., 2010]. We used ride data of yellow taxis in Manhattan in New York4. (Footnote 4 links to: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page) |
| Dataset Splits | No | The paper mentions running simulations on real data but does not provide specific details on training, validation, or testing splits (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | Yes | Experiments were run on a computer with Xeon Platinum 8168 (4 x 2.7GHz), 1TB of memory, Cent OS 7.6. |
| Software Dependencies | No | The paper states, "The program codes were implemented in Python 3.6.3." However, it only specifies the programming language and its version, without listing any versioned libraries, frameworks, or self-contained solvers. |
| Experiment Setup | Yes | Input: z0, λ0, τ0, σ0, α0, η, and c (Algorithm 1). BO-A first evaluates five random points of x. Then, after running the Bayesian optimization for 1000 seconds, it outputs the solution with the highest objective value among the evaluated points. The set to search for x is [0, 1]V T in crowd-sourcing platform experiments and [0, 50]V T in ride-sharing platform experiments. |