Personalizing Many Decisions with High-Dimensional Covariates
Authors: Nima Hamidi, Mohsen Bayati, Kapil Gupta
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also show via simulations that REAL-Bandit algorithm outperforms existing algorithms that do not leverage the low-rank structure of the problem. (From Abstract); Figure 1 shows average cumulative regret (with 1 SE error bars) for all algorithms across these 10 runs. |
| Researcher Affiliation | Collaboration | Department of Statistics, Stanford University, hamidi@stanford.edu Graduate School of Business, Stanford University, bayati@stanford.edu Airbnb, kapil.gupta@airbnb.com |
| Pseudocode | Yes | Algorithm 1 Row-enhancement procedure; Algorithm 2 REAL-Bandit algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | Taking k = 201, d = 200, and r = 3, we generated matrix B as UV where rows of U R201 3 and V R200 3 are drawn independently and uniformly from the unit sphere in R3. Noise variance is 1 and features are i.i.d. N(0, Id). |
| Dataset Splits | No | The paper describes synthetic data generation and simulation for a time horizon, but it does not specify any explicit training/validation/test dataset splits with percentages, sample counts, or references to predefined splits typically associated with reproducibility of data partitioning. |
| Hardware Specification | No | The paper describes the simulation setup and parameters but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper refers to other algorithms (OLS-Bandit, LASSO-Bandit, OFUL, Thompson sampling) and theoretical concepts, but it does not list specific software dependencies with version numbers (e.g., libraries, frameworks, or programming language versions) used for its implementation or simulations. |
| Experiment Setup | Yes | Taking k = 201, d = 200, and r = 3, we generated matrix B as UV where rows of U R201 3 and V R200 3 are drawn independently and uniformly from the unit sphere in R3. Noise variance is 1 and features are i.i.d. N(0, Id). We gave Thompson sampling the true prior mean and variance of the arm parameters, and the true noise variance. Similarly, OFUL had access to the true noise variance. Other parameters of OLS-Bandit, LASSO-Bandit, and OFUL are selected as in [6]. We generated 10 data sets and executed all algorithms for a time horizon of length T = 40, 000. |