Representation Preserving Multiclass Agnostic to Realizable Reduction
Authors: Steve Hanneke, Qinglin Meng, Amirreza Shaeiri
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is a theory that demonstrates a simple and elegant agnostic to realizable reduction for this framework. This resolves an open problem raised by the recent work of (Hopkins et al., 2022). Notably, our result is the first representation preserving multiclass agnostic to realizable reduction, in contrast with the compression based approach of the work of (David et al., 2016). Furthermore, our main theorem is stated in an abstract framework, called Unified PAC Learning , which encompasses a range of frameworks, including multiclass PAC learning, list PAC learning, and multilabel PAC learning. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Purdue University, West Lafayette, IN, USA. |
| Pseudocode | Yes | Algorithm 1 Agnostic to Realizable Reduction Input: Concept Class C, Hypothesis class H, Realizable PAC-Learner A, Labeled Sample S. 1: Divide S into two parts V and T. 2: Run A over all subsets of V to get: HV := {A(S ) | S V } 3: Return the hypothesis in HV with lowest empirical error over T. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets. It references conceptual machine learning tasks like "image object recognition task" but does not use them for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation using specific datasets, thus there are no mentions of dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or hardware used for such experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithms, without mentioning any specific software or programming dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any practical experimental setup, hyperparameters, or training configurations. |