Beyond IID: data-driven decision-making in heterogeneous environments
Authors: Omar Besbes, Will Ma, Omar Mouchtaki
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study data-driven decision-making and depart from the classical identically and independently distributed (i.i.d.) assumption. We present a new framework in which historical samples are generated from unknown and different distributions... We quantify the asymptotic worst-case regret... Our work shows that the type of achievable performance varies considerably... We demonstrate the versatility of our framework by comparing achievable guarantees... En route, we establish a new connection between data-driven decision-making and distributionally robust optimization. |
| Researcher Affiliation | Academia | Omar Besbes Will Ma Omar Mouchtaki Decision, Risk, and Operations Division Columbia University {ob2105,wm2428,om2316}@gsb.columbia.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. Policies like SAA are described in textual form. |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper does not mention using any specific public datasets for training, as it focuses on theoretical analysis and deriving performance bounds. |
| Dataset Splits | No | The paper does not provide details on dataset splits (train/validation/test), as it is a theoretical paper focusing on asymptotic analysis. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments, as the research is theoretical and does not involve computational experiments that require such specifications. |
| Software Dependencies | No | The paper does not list specific software components with version numbers. This is consistent with its theoretical nature, which does not involve empirical implementation details. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameters or training configurations, as it is a theoretical work and does not describe empirical experiments. |