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.
The information-theoretic value of unlabeled data in semi-supervised learning
Authors: Alexander Golovnev, David Pal, Balazs Szorenyi
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,785 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,751 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 | More specifically, we prove a separation by Θ(log n) multiplicative factor for the class of projections over the Boolean hypercube of dimension n. We prove that there is no separation for the class of all functions on domain of any size. |
| Researcher Affiliation | Collaboration | 1Harvard University, Cambridge, MA, USA 2Yahoo Research, New York, NY, USA. |
| Pseudocode | No | The paper focuses on mathematical proofs and theoretical derivations and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and focuses on proofs and bounds; it does not describe any specific software implementation and therefore does not provide open-source code. |
| Open Datasets | No | This theoretical paper does not involve empirical training with datasets. |
| Dataset Splits | No | This theoretical paper does not involve empirical validation with datasets. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not detail any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or training configurations. |