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.
Learning Mixtures of Gaussians with Censored Data
Authors: Wai Ming Tai, Bryon Aragam
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 24,608 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,589 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 | We study the problem of learning mixtures of Gaussians with censored data. Statistical learning with censored data is a classical problem, with numerous practical applications, however, finitesample guarantees for even simple latent variable models such as Gaussian mixtures are missing. Formally, we are given censored data from a mixture of univariate Gaussians i=1 wi N(µi, σ2), i.e. the sample is observed only if it lies inside a set S. The goal is to learn the weights wi and the means µi. We propose an algorithm that takes only 1 εO(k) samples to estimate the weights wi and the means µi within ε error. |
| Researcher Affiliation | Academia | 1Booth School of Business, University of Chicago, Chicago, USA. Correspondence to: Wai Ming Tai <EMAIL>, Bryon Aragam <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Learning mixtures of Gaussians with censored data |
| Open Source Code | No | The paper does not provide any explicit statement about releasing open-source code for their methodology or a link to a repository. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training. Thus, no information about public availability of datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve data splitting for training, validation, or testing. Therefore, no information on dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup or hyperparameter details. |