Bandit Task Assignment with Unknown Processing Time
Authors: Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted small experiments to evaluate the practical performance of the proposed algorithm using a synthetic dataset. We set the parameters for the dataset as follows: A = {A [N] | |A| M} with (N, M) = (4, 2), T = 10000, Cu = 6, Cl = 1, r = (0.5, 0.5, 0.5, 0.5). For the expected processing times, we set c = (1.5, 1.5, 2.0, 2.0) as a problem instance with small or c = (1.5, 1.5, 5.0, 5.0) as a problem instance with large . Each cti and rti are generated so that cti 1 follows binomial distributions over {0, 1, . . . , Cu 1} and rti follows Bernoulli distributions over {0, 1}. For comparison, we have added the results of applying UCB-BV1 by Ding et al. [15] and Comb UCB1 by Kveton et al. [24]. More detailed information on the experimental setup is provided in the supplementary material. Figures 2 and 3 show the empirical means of regret computed by 100 repetitions of independent experiments. We depict 2-standard deviations of the empirical regret by the shaded areas. These results imply that the proposed algorithm performs better experimentally than other algorithms. |
| Researcher Affiliation | Collaboration | Shinji Ito NEC Corporation, RIKEN AIP i-shinji@nec.com Daisuke Hatano RIKEN AIP daisuke.hatano@riken.jp Hanna Sumita Tokyo Institute of Technology sumita@c.titech.ac.jp Kei Takemura NEC Corporation kei_takemura@nec.com Takuro Fukunaga Chuo University fukunaga.07s@g.chuo-u.ac.jp Naonori Kakimura Keio University kakimura@math.keio.ac.jp Ken-ichi Kawarabayashi National Institute of Informatics, The University of Tokyo k_keniti@nii.ac.jp |
| Pseudocode | Yes | Algorithm 1 Phased-update UCB algorithm for bandit task assignment |
| Open Source Code | Yes | Data and source code to reproduce the experimental results in this paper are included in the supplementary materials. |
| Open Datasets | No | We conducted small experiments to evaluate the practical performance of the proposed algorithm using a synthetic dataset. We set the parameters for the dataset as follows: A = {A [N] | |A| M} with (N, M) = (4, 2), T = 10000, Cu = 6, Cl = 1, r = (0.5, 0.5, 0.5, 0.5). For the expected processing times, we set c = (1.5, 1.5, 2.0, 2.0) as a problem instance with small or c = (1.5, 1.5, 5.0, 5.0) as a problem instance with large . Each cti and rti are generated so that cti 1 follows binomial distributions over {0, 1, . . . , Cu 1} and rti follows Bernoulli distributions over {0, 1}. |
| Dataset Splits | No | The paper describes using a synthetic dataset generated with specified parameters (N, M, T, Cu, Cl, r, c) and conducting simulations over a time horizon T, but does not explicitly mention train, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions that 'More detailed information on the experimental setup is provided in the supplementary material' and 'Data and source code to reproduce the experimental results in this paper are included in the supplementary materials,' but does not explicitly list specific software dependencies with version numbers within the provided text. |
| Experiment Setup | Yes | We set the parameters for the dataset as follows: A = {A [N] | |A| M} with (N, M) = (4, 2), T = 10000, Cu = 6, Cl = 1, r = (0.5, 0.5, 0.5, 0.5). For the expected processing times, we set c = (1.5, 1.5, 2.0, 2.0) as a problem instance with small or c = (1.5, 1.5, 5.0, 5.0) as a problem instance with large . Each cti and rti are generated so that cti 1 follows binomial distributions over {0, 1, . . . , Cu 1} and rti follows Bernoulli distributions over {0, 1}. For comparison, we have added the results of applying UCB-BV1 by Ding et al. [15] and Comb UCB1 by Kveton et al. [24]. More detailed information on the experimental setup is provided in the supplementary material. |