Papers › Functional Gradient Flows for Constrained Sampling

Functional Gradient Flows for Constrained Sampling

30 Oct 2024arXiv:2410.23170archive 2025-07-28

Shiyue Zhang, Longlin Yu, Ziheng Cheng, Cheng Zhang

Recently, through a unified gradient flow perspective of Markov chain Monte Carlo (MCMC) and variational inference (VI), particle-based variational inference methods (ParVIs) have been proposed that tend to combine the best of both worlds. While typical ParVIs such as Stein Variational Gradient Descent (SVGD) approximate the gradient flow within a reproducing kernel Hilbert space (RKHS), many attempts have been made recently to replace RKHS with more expressive function spaces, such as neural networks. While successful, these methods are mainly designed for sampling from unconstrained domains. In this paper, we offer a general solution to constrained sampling by introducing a boundary condition for the gradient flow which would confine the particles within the specific domain. This allows us to propose a new functional gradient ParVI method for constrained sampling, called constrained functional gradient flow (CFG), with provable continuous-time convergence in total variation (TV). We also present novel numerical strategies to handle the boundary integral term arising from the domain constraints. Our theory and experiments demonstrate the effectiveness of the proposed framework.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2410.23170")

Code

Syntology Ran 10 of 12 code samples harvested from 2 repositories linked to this paper; 2 have no recorded run. Of those that ran: 1 ran · honoured contract; 5 ran · our draft was wrong; 4 ran with no contract checked.

By repository: official repository: 9 samples from 1 repository, 7 ran; community: 3 samples from 1 repository, 3 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

12 samples harvested; 10 ran; 1 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
5ran · our draft was wrong
4ran
2unverified

Licence: 9 of the 12 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

BNNClassifier ShiyueZhang66/Constrained-Functional-Gradient-Flow/Monotonic BNN/CFG_bnn/runners.py official repository ran no licence file found · pointer only · 12fbdc03d5e1e46f · report
F_net ShiyueZhang66/Constrained-Functional-Gradient-Flow/Monotonic BNN/CFG_bnn/runners.py official repository ran fingerprinted no licence file found · pointer only · ff0707ff82829c52 · report
Z_net ShiyueZhang66/Constrained-Functional-Gradient-Flow/Monotonic BNN/CFG_bnn/runners.py official repository ran fingerprinted no licence file found · pointer only · 3f24bf5f55d8ce60 · report
generate_normalize_numerical_mat ShiyueZhang66/Constrained-Functional-Gradient-Flow/Monotonic BNN/CFG_bnn/runners.py official repository ran · honoured contract fingerprinted no licence file found · pointer only · d451d2d77a159994 · report
generate_one_hot_mat ShiyueZhang66/Constrained-Functional-Gradient-Flow/Monotonic BNN/CFG_bnn/runners.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · 8579b1f0c4075070 · report
load_data ShiyueZhang66/Constrained-Functional-Gradient-Flow/Monotonic BNN/CFG_bnn/runners.py official repository ran no licence file found · pointer only · a97ff05389d00e15 · report
normalize_data_ours ShiyueZhang66/Constrained-Functional-Gradient-Flow/Monotonic BNN/CFG_bnn/runners.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · 7e1954f30c7a9e83 · report
ConstrainedGWG ShiyueZhang66/Constrained-Functional-Gradient-Flow/Monotonic BNN/CFG_bnn/runners.py official repository unverified no licence file found · pointer only · 953cf1ae00d88ff9 · report
RUNNER ShiyueZhang66/Constrained-Functional-Gradient-Flow/Monotonic BNN/CFG_bnn/runners.py official repository unverified no licence file found · pointer only · ff25ad1c88331c77 · report
get_skimming_mask Extraltodeus/Skimmed_CFG/skimmed_CFG.py community ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · b7525eaf099c0049 · report
interpolated_scales Extraltodeus/Skimmed_CFG/skimmed_CFG.py community ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · 0f0e888ded533663 · report
skimmed_CFG Extraltodeus/Skimmed_CFG/skimmed_CFG.py community ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · 97625a658e3fa3aa · report

Tasks

Variational Inference

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

Variational Inference

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections