Learning from Stochastically Revealed Preference
Authors: John Birge, Xiaocheng Li, Chunlin Sun
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the algorithm performance through numerical experiments. |
| Researcher Affiliation | Academia | John R. Birge The University of Chicago Booth School of Business John.Birge@chicagobooth.edu; Xiaocheng Li Imperial College Business School, Imperial College London xiaocheng.li@imperial.ac.uk; Chunlin Sun Institute for Computational and Mathematical Engineering, Stanford University chunlin@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Posterior Sampling for the Gaussian Setting; Algorithm 2 Simulated annealing algorithm for δ-corruption |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper uses synthetically generated data based on specified distributions (e.g., 'a Unif([1, 2]n) and b Unif([1, n])') and does not refer to any publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not specify explicit training/validation/test dataset splits (e.g., percentages or counts) or refer to standard predefined splits for its numerical experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | Algorithm 1 and 2 mention 'number of iterations K'. Algorithm 2 also specifies 'margin γ', 'initial (temperature) η > 0 and the reduction rate c (0, 1)', and 'interval length τ'. For numerical experiments, it's stated 'we run both Algorithm 1 and Algorithm 2 for K = 1000 iterations'. |