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..
Random Function Priors for Correlation Modeling
Authors: Aonan Zhang, John Paisley
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirical results on three text datasets: a 5K subset of New York Times, 20Newsgroups, and Neur IPS. Their basic statistics are shown in Table 2. In Table 1, we compare three Bayesian nonparametric models: hierarchical Dirichlet process (HDP) (Teh et al., 2005), discrete infinite logistic normal (DILN) (Paisley et al., 2012b), and our population random measure embedding (PRME) using 4-layer MLP with batch normalization. As Table 1 shows, PRME consistently perform better than HDP and DILN. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering & Data Science Institute, Columbia University, New York, USA. |
| Pseudocode | Yes | Algorithm 1 Feature paintboxes model. Algorithm 2 Stochastic inference algorithm |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We show empirical results on three text datasets: a 5K subset of New York Times, 20Newsgroups, and Neur IPS. Their basic statistics are shown in Table 2. |
| Dataset Splits | No | The paper mentions 'For each test document Xn, we do a 90%/10% split into training words Xn,T R and testing words Xn,T S.' This refers to a split within each test document for perplexity calculation, not a global train/validation/test split for the datasets used in experiments. No explicit validation split information is provided for the main datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma & Ba, 2014)' as an optimizer and various neural network architectures (MLP, Res Net) but does not provide specific version numbers for software dependencies such as deep learning frameworks or programming languages. |
| Experiment Setup | Yes | All gradient updates are done via Adam (Kingma & Ba, 2014) with learning rate 10^-4. We tune γ0 and fix the truncation level K = 100 and set the a = 1, b = 1, α = 1, β = 5 for fair comparisons. Let ρ(t) (t0 + t) κ be the step size with some constant t0 and κ (0.5, 1]. For the larger one million New York Times dataset, we show topic paintboxes learned with stochastic PRME in Figure 4. We set t0 = 100, κ = 0.75 and use a 6-layer MLP. In Figure 5(c), we compare run times for updating local parameters ([Z, C] for PRME) and global parameters ([θ, ℓ, V, g, f] for PRME) with batch size 500. |