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 [1].
Sequential Kernel Goodness-of-fit Testing
Authors: Zhengyu Zhou, Weiwei Liu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to demonstrate the adaptability of our sequential test across varying difficulty levels of problems while maintaining control over type-I errors. This section describes the experiments that demonstrate our tests capacity to adapt to a problem s unknown difficulty while maintaining type-I error control. |
| Researcher Affiliation | Academia | 1School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China. Correspondence to: Weiwei Liu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Online Newton Step (ONS) strategy for selecting betting fractions, Algorithm 2 KSD-based SKGT, Algorithm 3 KDSD-based SKGT, Algorithm 4 bd-KSD-based SKGT |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper describes how synthetic data was generated for simulations (e.g., "Zt iid q", "samples from a Student s t distribution", "samples from an Ising model"), but it does not provide access information or citations for any public dataset. |
| Dataset Splits | No | The paper conducts statistical goodness-of-fit tests and does not involve training machine learning models with explicit training, validation, and testing data splits. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions statistical methods and algorithms used (e.g., Gaussian kernel, Metropolis algorithm, wild bootstrap), but it does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, or specialized packages). |
| Experiment Setup | Yes | We consider 11 β: β {0.5, 0.55, . . . , 1.0} values, and for each β we repeat the simulation 100 times. We use Gaussian kernel k(x, y) = exp( |x y|2 /2) for all testing procedures. We consider a periodic 10-by-10 lattice, with d = 100 random variables. We focus on the ferromagnetic setting and set θij = 1/T, where T is the temperature of the system. For T0 = 5 and various values of T, we test the hypotheses H0 : T = T0 vs. H1 : T = T0 using data samples drawn from the model under temperature T. We consider different values of v {0, 0.3, 0.6, 0.9}. |