Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Reliable Learning of Halfspaces under Gaussian Marginals
Authors: Ilias Diakonikolas, Lisheng Ren, Nikos Zarifis
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 38,591 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,185 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main positive result is a new algorithm for reliable learning of Gaussian halfspaces on Rd with sample and computational complexity d O(log(min{1/α,1/ϵ})) min(2log(1/ϵ)O(log(1/α)), 2poly(1/ϵ)) , where ϵ is the excess error and α is the bias of the optimal halfspace. We complement our upper bound with a Statistical Query lower bound suggesting that the dΩ(log(1/α)) dependence is best possible. |
| Researcher Affiliation | Academia | Ilias Diakonikolas University of Wisconsin-Madison EMAIL Lisheng Ren University of Wisconsin-Madison EMAIL Nikos Zarifis University of Wisconsin-Madison EMAIL |
| Pseudocode | Yes | Algorithm 1: Reliably Learning General Halfspaces with Gaussian Marginals. Algorithm 2: Finding a Direction with High Correlation. Algorithm 3: Reliably Learning General Halfspaces with Gaussian Marginals (detailed version of Algorithm 1). |
| Open Source Code | No | The paper is theoretical and does not mention providing open-source code for the methodology. |
| Open Datasets | No | The paper is theoretical and does not use or provide access information for a publicly available dataset for training. It refers to a theoretical distribution D supported on Rd { 1}. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments or specify training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or hyperparameters. |