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 Unnormalized Statistical Models via Compositional Optimization
Authors: Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the better performance of our method on different tasks, namely, density estimation, out-of-distribution detection, and real image generation. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2 Department of Computer Science and Engineering, University at Buffalo, New York, USA 3Department of Computer Science and Engineering, Texas A&M University, College Station, USA. Correspondence to: Changyou Chen <EMAIL>, Tianbao Yang <EMAIL>, Lijun Zhang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 MECO Input: time step T, initial points (θ1, u1, v1) sequence {ηt, γt, βt} for time step t = 1 to T do Sampling zt from {x1, , xn} and ezt from q(x) Update estimator ut according to equation (3) Update estimator vt according to equation (4) Update the weight: θt+1 = θt ηtvt end for Choose τ uniformly at random from {1, . . . , T} Return θτ |
| Open Source Code | No | The paper does not provide a direct link to the source code for the methodology, nor does it explicitly state that the code is released or available. |
| Open Datasets | Yes | We choose CIFAR-10 (Krizhevsky, 2009) as the in-distribution data. |
| Dataset Splits | No | The paper mentions using training and testing data but does not explicitly provide details about validation splits or percentages for any of the datasets used. |
| Hardware Specification | Yes | Experiments on MNIST in Section 6.3 are trained on four NVIDIA Tesla V100 GPUs, and the training time is around 2.8 hours. |
| Software Dependencies | No | The paper mentions using `numpy` and `Adam` optimizer, but it does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For our method, we set the parameter γ = 0.1 and β = 0.9. For MCMC training, the number of sampling steps is searched from the set {20, 50, 100} and we use Langevin dynamics (Welling & Teh, 2011) as the sampling approach. For all tasks, we tune the learning rates from {1e 1, 1e 2, 1e 3, 1e 4} and pick the best one. |