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..
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning
Authors: Alberto Maria Metelli, Alessio Russo, Marcello Restelli
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide numerical simulations on both synthetic examples and contextual bandits, in comparison with off-policy evaluation and learning baselines. |
| Researcher Affiliation | Academia | Alberto Maria Metelli DEIB, Politecnico di Milano EMAIL Alessio Russo DEIB, Politecnico di Milano EMAIL Marcello Restelli DEIB, Politecnico di Milano EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is provided at https://github.com/albertometelli/subgaussian-is. |
| Open Datasets | Yes | We consider 11 UCI [13] multi-class classification datasets (see Table 9 in Appendix B.1.2). |
| Dataset Splits | Yes | Each dataset is split into a training set Dtrain and an evaluation Deval with proportions 30% and 70%. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python version, library versions) that would be needed for reproducibility. |
| Experiment Setup | Yes | Specifically, we consider a Gaussian behavioral policy Q NpµQ,σ2 Qq and a Gaussian target policy P NpµP ,σ2 P q. We generate n i.i.d. samples from Q and we estimate the expectation of function fpyq 100cosp2πyq under P. We select µQ 0, µP 0.5, σ2 Q 1 and σ2 P 1.9 [...] The behavioral policy is obtained as: πbpa|xq αb 1 αb K if a Cpxq and πbpa|xq 1 αb K otherwise, where αb Pr0,1s. The target policy πe is obtained as the behavioral one by training another classifier on Dtrain and using αe Pr0,1s. |