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.
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Authors: Ruqi Zhang, Qiang Liu, Xin Tong
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,705 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,024 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement O-Gradient through both Langevin dynamics and Stein variational gradient descent and demonstrate its effectiveness in various experiments, including Bayesian deep neural networks. and We empirically demonstrate the sampling performance of O-Langevin and O-SVGD across different constrained ML problems. |
| Researcher Affiliation | Academia | Ruqi Zhang Department of Computer Science Purdue University EMAIL Qiang Liu Department of Computer Science University of Texas at Austin EMAIL Xin T. Tong Department of Mathematics National University of Singapore EMAIL |
| Pseudocode | Yes | Algorithm 1 O-SVGD and O-Langevin. |
| Open Source Code | Yes | We released the code at https://github.com/ruqizhang/o-gradient. |
| Open Datasets | Yes | We use Adult Income dataset [13] and We apply our methods to image classification on CIFAR10 with Res Net-18. |
| Dataset Splits | No | The paper mentions "training" and "testing" but does not provide specific details on dataset splits (e.g., percentages, sample counts, or explicit reference to standard splits used) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names like PyTorch 1.9 or TensorFlow 2.x). |
| Experiment Setup | No | The paper describes some high-level experimental details such as model architectures (two-layer MLP, ResNet-18) and the number of particles (n=50 for synthetic task), but it does not provide specific hyperparameters like learning rates, batch sizes, number of epochs, or optimizer settings needed for a complete experimental setup. |