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..
Learning Mixtures of Gaussians with Censored Data
Authors: Wai Ming Tai, Bryon Aragam
ICML 2023 | Venue PDF | LLM Run Details
| 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. |