Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Statistical Inference with M-Estimators on Adaptively Collected Data
Authors: Kelly Zhang, Lucas Janson, Susan Murphy
NeurIPS 2021 | Venue PDF | 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 EMAIL Lucas Janson Departments of Statistics Harvard University EMAIL Susan A. Murphy Departments of Statistics and Computer Science Harvard University EMAIL |
| 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. |