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.
A Subquadratic Time Algorithm for Robust Sparse Mean Estimation
Authors: Ankit Pensia
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 35,727 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,684 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 contribution is an algorithm for robust sparse mean estimation which runs in subquadratic time using poly(k, log d, 1/ϵ) samples, with similar results for robust sparse PCA. Our results build on algorithmic advances in detecting weak correlations, a generalized version of the light-bulb problem by Valiant (Valiant, 2015). |
| Researcher Affiliation | Industry | 1IBM Research. Correspondence to: Ankit Pensia <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Algorithmic Blueprint [...] Algorithm 2 RANDOMLYCHECKCOORDINATES [...] Algorithm 3 Subroutine to Identify Corrupted Coordinates [...] Algorithm 4 Quadratic Scores [...] Algorithm 5 Randomized Filtering [...] Algorithm 6 Main Subroutine (Expanded version of Algorithm 3) [...] Algorithm 7 Main Algorithm [...] Algorithm 8 PCA Filter [...] Algorithm 9 Robust Sparse PCA Algorithm |
| Open Source Code | No | The paper does not provide any statements about releasing code or links to a code repository. |
| Open Datasets | No | The paper is theoretical, focusing on algorithm design and analysis. It does not use or refer to any publicly available datasets for training or experimentation. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and analyzes algorithm complexity. It does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and describes algorithms; it does not provide implementation details or software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments or their setup, thus no hyperparameters or training settings are mentioned. |