Towards Efficient Contrastive PAC Learning
Authors: Jie Shen
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We first show that the problem of contrastive PAC learning of linear representations is intractable to solve in general. We then show that it can be relaxed to a semi-definite program when the distance between contrastive samples is measured by the ℓ2-norm. We then establish generalization guarantees based on Rademacher complexity, and connect it to PAC guarantees under certain contrastive large-margin conditions. To the best of our knowledge, this is the first efficient PAC learning algorithm for contrastive learning. |
| Researcher Affiliation | Academia | Jie Shen EMAIL Department of Computer Science Stevens Institute of Technology |
| Pseudocode | No | The paper describes algorithms and methods using mathematical formulations and textual descriptions, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide a link to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets. It refers to a generic 'data distribution D' and 'contrastive samples' but does not specify any publicly available datasets used for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with specific datasets, therefore, it does not provide details on dataset splits (training/test/validation). |
| Hardware Specification | No | The paper is theoretical and does not report any experimental results that would require specific hardware for computation. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not detail any experimental implementation, thus no software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not conduct practical experiments. Therefore, it does not provide details on experimental setup such as hyperparameters or training configurations. |