Learning Rate Free Sampling in Constrained Domains
Authors: Louis Sharrock, Lester Mackey, Christopher Nemeth
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our algorithms on a range of numerical examples, including sampling from targets on the simplex, sampling with fairness constraints, and constrained sampling problems in postselection inference. Our results indicate that our algorithms achieve competitive performance with existing constrained sampling methods, without the need to tune any hyperparameters. |
| Researcher Affiliation | Collaboration | Louis Sharrock Department of Mathematics and Statistics Lancaster University, UK l.sharrock@lancaster.ac.uk Lester Mackey Microsoft Research New England Cambridge, MA lmackey@microsoft.com Christopher Nemeth Department of Mathematics and Statistics Lancaster University, UK c.nemeth@lancaster.ac.uk |
| Pseudocode | Yes | Algorithm 1 MSVGD; Algorithm 2 Coin MSVGD; Algorithm 3 Coin MIED; Algorithm 4 Mirrored LAWGD; Algorithm 5 Mirrored KSDD; Algorithm 6 Coin MLAWGD; Algorithm 7 Coin MKSDD; Algorithm 8 Adaptive Coin MSVGD |
| Open Source Code | Yes | Code to reproduce all of the numerical results can be found at https://github.com/louissharrock/constrained-coin-sampling. |
| Open Datasets | Yes | We use the Adult Income dataset [50]. ... We next consider a post-selection inference problem involving the HIV-1 drug resistance dataset studied in [8, 75]. |
| Dataset Splits | No | The paper mentions a "train-test split of 80% / 20%" but does not explicitly provide information on a validation split. |
| Hardware Specification | Yes | We perform all experiments using a Mac Book Pro 16" (2021) laptop with Apple M1 Pro chip and 16GB of RAM. |
| Software Dependencies | No | The paper states: "We implement all methods using Python 3, Py Torch, and Tensor Flow." However, it only provides a version number for Python, not for PyTorch or TensorFlow, which are crucial software dependencies. |
| Experiment Setup | Yes | We employ the IMQ kernel and the entropic mirror map [7]; and use N = 50 particles, T = 500 iterations. ... We run each algorithm for T = 1000 iterations. For Coin MSVGD, MSVGD, and SVMD, we use N = 50 particles, and generate Ntotal samples by aggregating the particles from Ntotal/N independent runs. ... We run all algorithms for T = 2000 iterations, and using N = 50 particles. |