Hiring Under Uncertainty
Authors: Manish Purohit, Sreenivas Gollapudi, Manish Raghavan
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the performance of our algorithms for the HIRING WITH UNCERTAINTY problem in both the sequential and parallel offers setting via simulations. We generate simulated data sets as follows. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Cornell University 2Google Research. Correspondence to: Manish Raghavan <manish@cs.cornell.edu>. |
| Pseudocode | Yes | Procedure 1 Approx Given Tree(T); Procedure 2 Parallel From Sequential(T); Procedure 3 Knapsack Finite Probes(p, v, s) |
| Open Source Code | No | The paper does not provide any statements or links indicating that the source code for their methodology is open-source or publicly available. |
| Open Datasets | No | The paper states: 'We generate simulated data sets as follows.' It describes the generation process but does not refer to a publicly available dataset with concrete access information (link, DOI, formal citation). |
| Dataset Splits | No | The paper describes generating simulated data for experiments but does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory, cloud resources). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The values for n = 100 candidates are chosen uniformly at random from [0, 1]. We consider three models to generate the probabilities: Negative correlation: Higher-value candidates are less likely to accept offers. We sample pi s according to a Beta distribution, with pi Beta(10(1 vi), 10vi). Positive correlation: Higher-value candidates are more likely to accept: pi Beta(10vi, 10(1 vi)). No correlation: pi Uniform[0, 1]. On each of these data sets, we consider the performance of our three algorithms each with k = 20. |