Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Agnostic Sample Compression Schemes for Regression
Authors: Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
ICML 2024 | Venue PDF | 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. |