Agnostic Sample Compression Schemes for Regression
Authors: Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We obtain the first positive results for bounded sample compression in the agnostic regression setting with the ℓp loss, where p [1, ]. We construct a generic approximate sample compression scheme for real-valued function classes exhibiting exponential size in the fat-shattering dimension but independent of the sample size. Notably, for linear regression, an approximate compression of size linear in the dimension is constructed. Moreover, for ℓ1 and ℓ losses, we can even exhibit an efficient exact sample compression scheme of size linear in the dimension. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Ben-Gurion University, Israel 2Department of Computer Science, Purdue University, USA. |
| Pseudocode | Yes | Algorithm 1 Approximate Agnostic Sample Compression for ℓp Regression, p [1, ] |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments run on specific public datasets with access information. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation with specific dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe a concrete experimental setup with hyperparameters or training configurations. |