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 [1].
Posterior Asymptotics for Boosted Hierarchical Dirichlet Process Mixtures
Authors: Marta Catalano, Pierpaolo De Blasi, Antonio Lijoi, Igor Pruenster
JMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Here we establish theoretical guarantees for recovering the true data generating process when the data are modeled as mixtures over the HDP or a generalization of the HDP... This paper provides a two-fold contribution in this direction: (1) we generalize Schwartz s theory (Schwartz, 1965), which is pivotal to frequentist asymptotics in the exchangeable case, to the partially exchangeable setup; (2) we derive posterior contraction rates for multivariate mixtures with respect to the b HDP, which includes the HDP as a special case, and establish that they crucially depend on the relation between the sample sizes corresponding to di๏ฌerent groups. |
| Researcher Affiliation | Academia | Marta Catalano EMAIL University of Warwick Coventry, UK Pierpaolo De Blasi EMAIL University of Torino and Collegio Carlo Alberto Torino, Italy Antonio Lijoi EMAIL Bocconi University and BIDSA Milano, Italy Igor Pr unster EMAIL Bocconi University and BIDSA Milano, Italy |
| Pseudocode | No | The paper contains mathematical derivations, lemmas, and theorems. There are no sections explicitly labeled 'Pseudocode' or 'Algorithm', nor are there any structured code-like procedures presented. |
| Open Source Code | No | The paper does not contain any explicit statements about the release of source code for the methodology described, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper discusses 'data generating process' and abstract scenarios like 'data clustered by blood types or by logfiles' for theoretical exposition. It does not mention the use of any specific, publicly available datasets for experiments, nor does it provide any links, DOIs, or citations for data access. |
| Dataset Splits | No | The paper is theoretical and does not describe any experiments using datasets. Therefore, there is no mention of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is purely theoretical, focusing on mathematical derivations and proofs. It does not describe any computational experiments or their execution environment, and therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational experiments. Therefore, no specific software dependencies or their version numbers are mentioned. |
| Experiment Setup | No | The paper is entirely theoretical, presenting mathematical models and derivations without conducting any empirical experiments. Consequently, there are no details provided regarding experimental setup, hyperparameters, or training configurations. |