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].
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model
Authors: Ying Jin, Zhaoran Wang, Junwei Lu
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper aims to solve this problem from two major perspectives: (1) the theoretical gap between the computational and statistical rates for recovering various combinatorial structures in Ising model; and (2) polynomial-time algorithms to detect these structures efficiently. When considering the computational budgets, we employ the computational oracle model developed and explored in (Kearns, 1998), (Feldman et al., 2013), (Wang et al., 2015), etc. |
| Researcher Affiliation | Academia | 1Department of Statistics, Stanford University, Stanford, CA, USA 2Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USA 3Department of Biostatistics, Harvard University, Boston, MA, USA. |
| Pseudocode | No | The paper includes mathematical definitions and query functions, but it does not contain structured pseudocode or algorithm blocks explicitly labeled as such. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. There are no links to repositories or explicit statements about code release. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments, datasets, or training. Therefore, it does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments or data splits for training, validation, or testing. Therefore, it does not provide specific dataset split information. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments; therefore, it does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments or software implementations with specific version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experimental setup details, such as hyperparameters or training configurations. |