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.
Near-Optimal Confidence Sequences for Bounded Random Variables
Authors: Arun K Kuchibhotla, Qinqing Zheng
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,131 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,496 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct numerical experiments to verify our theoretical claims. Moreover, we apply the Bentkus confidence sequence to the pε, δq mean estimation problem and the best-arm identification problem. |
| Researcher Affiliation | Collaboration | 1 Department of Statistics and Data Science, Carnegie Mellon University. 2Facebook AI Research. |
| Pseudocode | Yes | Algorithm 1: Adaptive Stopping Algorithm; Algorithm 2: Best Arm Identification |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper generates synthetic data for its experiments (e.g., "We generate samples Y1, Y2, . . . , Y20000 i.i.d Bernoullip0.1q" and "The data samples are i.i.d generated as Yi m 1 řm j 1 Uij, where Uij are i.i.d uniformly distributed in r0, 1s"). It does not use or provide access to any publicly available external datasets. |
| Dataset Splits | No | The paper describes sequential data generation and does not specify traditional training, validation, or test splits. It focuses on sequential stopping criteria. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to conduct the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We use δ 0.05 for all the experiments. For A-Bentkus, we fix the spacing parameter η 1.1, the stitching function hpkq pk 1q1.1ζp1.1q, and δ1 2δ{3, δ2 δ{3. |