Statistical Inference with M-Estimators on Adaptively Collected Data
Authors: Kelly Zhang, Lucas Janson, Susan Murphy
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 1 we plot the empirical distributions of the z-statistic for the least-squares estimator both with and without adaptive weighting. We consider a two-armed bandit with At 2 {0, 1}. ... In Figure 4 we plot the empirical coverage probabilities and volumes of 90% confidence regions for (P) := [ 1(P) in both the continuous and binary reward settings. |
| Researcher Affiliation | Academia | Kelly W. Zhang Department of Computer Science Harvard University kellywzhang@seas.harvard.edu Lucas Janson Departments of Statistics Harvard University ljanson@fas.harvard.edu Susan A. Murphy Departments of Statistics and Computer Science Harvard University samurphy@fas.harvard.edu |
| Pseudocode | No | The paper describes algorithms and methods in text and mathematical formulas but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing code or links to a code repository. |
| Open Datasets | No | The paper describes generating its own data for simulations: 'In both simulation settings we collect data using Thompson Sampling with a linear model for the expected reward and normal priors'. It does not use or provide access information for a public or open dataset. |
| Dataset Splits | No | The paper describes generating data for its simulations ('In both simulation settings we collect data using Thompson Sampling') but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not contain any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper describes the methods and models used (e.g., least-squares estimators, maximum likelihood estimators, Thompson Sampling) but does not list any specific software or library names with version numbers. |
| Experiment Setup | Yes | In the continuous reward setting, we use least-squares estimators with a correctly specified model for the expected reward, i.e., M-estimators with m (Rt, Xt, At) = (Rt X> t 1)2. ... In both simulation settings we collect data using Thompson Sampling with a linear model for the expected reward and normal priors ... We constrain the action selection probabilities with clipping at a rate of 0.05. |