Generalization Analysis for Contrastive Representation Learning
Authors: Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we establish novel generalization bounds for contrastive learning which do not depend on k, up to logarithmic terms. Our analysis uses structural results on empirical covering numbers and Rademacher complexities to exploit the Lipschitz continuity of loss functions. For self-bounding Lipschitz loss functions, we further improve our results by developing optimistic bounds which imply fast rates in a low noise condition. We apply our results to learning with both linear representation and nonlinear representation by deep neural networks, for both of which we derive Rademacher complexity bounds to get improved generalization bounds. |
| Researcher Affiliation | Academia | 1Department of Mathematics, The University of Hong Kong 2Department of Computer Science and Engineering, Texas A&M University 3Department of Mathematics and Statistics, State University of New York at Albany 4School of Mathematics and Statistics, University of Sydney. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It focuses on theoretical analysis and mathematical proofs. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper defines a theoretical 'dataset S' for its analysis: 'Let (xj, x+ j ) Dsim and (x j1, . . . , x jk) Dneg, j [n] := {1, . . . , n}, where k denotes the number of negative examples. We collect these training examples into a dataset S = n (x1, x+ 1 , x 11, . . . , x 1k), (x2, x+ 2 , x 21, . . . , x 2k), . . . , (xn, x+ n , x n1, . . . , x nk) o.' It does not refer to a specific, publicly available dataset used for empirical training. |
| Dataset Splits | No | The paper is theoretical and focuses on generalization bounds. It defines a theoretical dataset for its analysis but does not describe training, validation, or test splits for empirical experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe running empirical experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe running empirical experiments. Therefore, no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup. Thus, it does not provide details on hyperparameters or training settings. |