Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Batch Value-function Approximation with Only Realizability
Authors: Tengyang Xie, Nan Jiang
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,400 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,448 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our algorithm, BVFT, breaks the hardness conjecture (albeit under a stronger notion of exploratory data) via a tournament procedure that reduces the learning problem to pairwise comparison, and solves the latter with the help of a state-action-space partition constructed from the compared functions. We present the algorithm in Section 4 and prove its sample complexity in Sections 5 and 6. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Illinois at Urbana-Champaign, Illinois, USA. Correspondence to: Nan Jiang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Batch Value-Function Tournament (BVFT) |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper assumes access to a "batch dataset D" for theoretical analysis but does not specify a real-world, publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus it does not provide information about dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not present empirical experiments, thus it does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not present empirical experiments, thus it does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not present empirical experiments, thus it does not provide details about experimental setup, such as hyperparameter values or training configurations. |