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..
Off-Policy Evaluation for Large Action Spaces via Embeddings
Authors: Yuta Saito, Thorsten Joachims
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to the theoretical analysis, we find that the empirical performance improvement can be substantial, enabling reliable OPE even when existing estimators collapse due to a large number of actions. and 4. Empirical Evaluation We first evaluate MIPS on synthetic data to identify the situations where it enables a more accurate OPE. Second, we validate real-world applicability on data from an online fashion store. |
| Researcher Affiliation | Academia | Yuta Saito 1 Thorsten Joachims 1 1Department of Computer Science, Cornell University, Ithaca, NY, USA. Correspondence to: Yuta Saito <EMAIL>, Thorsten Joachims <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 An Experimental Procedure to Evaluate an OPE Estimator with Real-World Bandit Data (Appendix D.3) |
| Open Source Code | Yes | Our experiment implementation is available at https://github.com/usaito/icml2022-mips. |
| Open Datasets | Yes | We use the Open Bandit Dataset (OBD)6 (Saito et al., 2020), a publicly available logged bandit dataset collected on a large-scale fashion e-commerce platform. |
| Dataset Splits | No | The paper describes generating synthetic data and using bootstrap sampling for real-world data evaluation, and cross-fitting for internal model estimation, but does not specify a fixed training, validation, and test dataset split for the overall OPE evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions software like 'scikit-learn' and 'Categorical Naive Bayes' but does not specify their version numbers. |
| Experiment Setup | Yes | To summarize, we first sample context x and define the expected reward q(x, e) as in Eq. (5). We then sample discrete action a from π0 based on Eq. (6). Given action a, we sample categorical action embedding e based on Eq. (4). Finally, we sample the reward from a normal distribution with mean q(x, e) and standard deviation σ = 2.5. Iterating this procedure n times generates logged data D with n independent copies of (x, a, e, r). and In the main text, we use β = 1, and additional results for other values of β can be found in Appendix D.2. and In the main text, we set ϵ = 0.05, which produces a near-optimal and near-deterministic target policy. |