Non-stationary Experimental Design under Linear Trends
Authors: David Simchi-Levi, Chonghuan Wang, Zeyu Zheng
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we address the problem of nonstationary experimental design under linear trends by considering two objectives: estimating the dynamic treatment effect and minimizing welfare loss within the experiment. We propose an efficient design that can be customized for optimal estimation error rate, optimal regret rate, or the Pareto optimal trade-off between the two objectives. We establish information-theoretical lower bounds that highlight the inherent challenge in estimating dynamic treatment effects and minimizing welfare loss, and also statistically reveal the fundamental trade-off between them. |
| Researcher Affiliation | Academia | David Simchi-Levi , Chonghuan Wang , Zeyu Zheng Institute for Data, Systems, and Society, Department of Civil and Environmental Engineering, Operations Research Center, MIT Laboratory for Information and Decision Systems, MIT Department of Industrial Engineering and Operations Research, University of California, Berkeley dslevi@mit.edu, chwang9@mit.edu, zyzheng@berkeley.edu |
| Pseudocode | Yes | Algorithm 1: Linear Exploration and OFU (L-EOFU) |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments with datasets, thus it does not provide concrete access information for a publicly available or open dataset for training. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments, therefore it does not specify dataset splits for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, as it focuses on theoretical contributions rather than empirical experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, as it is a theoretical work and does not describe an empirical implementation. |
| Experiment Setup | No | The paper describes a theoretical design and analysis, but it does not include details about an experimental setup, hyperparameters, or training configurations. |