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..
Testably Learning Polynomial Threshold Functions
Authors: Lucas Slot, Stefan Tiegel, Manuel Wiedmer
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is that PTFs can be learned efficiently in the testable model as well. ... Theorem 3 is the first result achieving efficient testable learning for PTFs of any fixed degree d (up to constant error ε > 0). Previously, such a result was not even available for learning degree-2 PTFs with respect to the Gaussian distribution. |
| Researcher Affiliation | Academia | Lucas Slot Department of Computer Science ETH Zurich EMAIL Stefan Tiegel Department of Computer Science ETH Zurich EMAIL Manuel Wiedmer Department of Computer Science ETH Zurich EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It describes theoretical concepts and proofs. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and discusses properties of distributions like the standard Gaussian, but it does not use or provide access information for a public dataset for training purposes. |
| Dataset Splits | No | The paper is theoretical and does not include empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameter values or training configurations. |