Generalization Analysis for Controllable Learning
Authors: Yi-Fan Zhang, Xiao Zhang, Min-Ling Zhang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | we first establish a unified framework for controllable learning. Then, we develop a novel vector-contraction inequality and derive a tight generalization bound for general controllable learning classes, which is independent of the number of task targets except for logarithmic factors and represents the current best-in-class theoretical result. Furthermore, we derive generalization bounds for two typical controllable learning methods: embedding-based and hypernetwork-based methods. We also upper bound the Rademacher complexities of commonly used control and prediction functions, which serve as modular theoretical components for deriving generalization bounds for specific controllable learning methods in practical applications such as recommender systems. Our theoretical results without strong assumptions provide general theoretical guarantees for controllable learning methods and offer new insights into understanding controllability in machine learning. |
| Researcher Affiliation | Academia | 1School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China 2Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China 3Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China 4Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE 5School of Computer Science and Engineering, Southeast University, Nanjing 210096, China. |
| Pseudocode | No | The paper primarily presents mathematical derivations, theorems, lemmas, and proofs. There are no explicitly labeled pseudocode or algorithm blocks describing a method in a structured, code-like format. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or include links to any code repositories for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on generalization analysis, not on empirical experiments. Consequently, it does not utilize or provide access information for specific datasets. |
| Dataset Splits | No | This paper presents a theoretical framework and generalization analysis, without conducting empirical experiments. Therefore, there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setups that would require specific hardware specifications for running computations. |
| Software Dependencies | No | As a theoretical paper, it does not describe an implementation or experiments requiring specific software libraries or tools with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical contributions such as generalization bounds and a unified framework. It does not include details on experimental setups, hyperparameters, or training configurations. |