Papers › Learning Robust Statistics for Simulation-based Inference under Model Misspecification

Learning Robust Statistics for Simulation-based Inference under Model Misspecification

25 May 2023NeurIPS 2023 11arXiv:2305.15871archive 2025-07-28

Daolang Huang, Ayush Bharti, Amauri Souza, Luigi Acerbi, Samuel Kaski

Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models. However, such methods are known to yield untrustworthy and misleading inference outcomes under model misspecification, thus hindering their widespread applicability. In this work, we propose the first general approach to handle model misspecification that works across different classes of SBI methods. Leveraging the fact that the choice of statistics determines the degree of misspecification in SBI, we introduce a regularized loss function that penalises those statistics that increase the mismatch between the data and the model. Taking NPE and ABC as use cases, we demonstrate the superior performance of our method on high-dimensional time-series models that are artificially misspecified. We also apply our method to real data from the field of radio propagation where the model is known to be misspecified. We show empirically that the method yields robust inference in misspecified scenarios, whilst still being accurate when the model is well-specified.

PaperPDFConference PDFCodeCode 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="2305.15871")

Code

Syntology Ran 2 of 12 code samples harvested from 3 repositories linked to this paper; 10 have no recorded run. Of those that ran: 2 ran · our draft was wrong.

By repository: official repository: 2 samples from 1 repository, 0 ran; found in paper text by Syntology: 10 samples from 2 repositories, 2 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

huangdaolang/robust-sbi officialmentioned in papermentioned on GitHubpytorchMIT report

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; 2 ran; 0 honoured the contract we drafted; 10 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.

2ran · our draft was wrong
10unverified

Licence: 0 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 3 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.

TurinModel huangdaolang/robust-sbi/simulators/turin.py official repository unverified MIT (permissive) · 3d6cf48c8adb6af6 · report
get_1d_marginal_peaks_from_kde huangdaolang/robust-sbi/utils/analysis_utils.py official repository unverified MIT (permissive) · 3cda7949f2409864 · report
add_spike_and_slab_error danielward27/rnpe/scripts/run_task.py found in paper text by Syntology ran · our draft was wrong MIT (permissive) · 6f99b93048e3da8d · report
rescale_results danielward27/rnpe/scripts/run_task.py found in paper text by Syntology ran · our draft was wrong MIT (permissive) · 90853ad231944023 · report
compute_rbf_mmd mackelab/sbi/sbi/diagnostics/misspecification.py found in paper text by Syntology unverified Apache-2.0 (permissive) · fc0e53b9b168ce47 · report
eval_lc2st mackelab/sbi/sbi/diagnostics/lc2st.py found in paper text by Syntology unverified Apache-2.0 (permissive) · 042c1c4d2e2f3e1c · report
get_default_diag_kwargs mackelab/sbi/sbi/analysis/plotting_classes.py found in paper text by Syntology unverified Apache-2.0 (permissive) · db2fea809ee31e99 · report
get_default_offdiag_kwargs mackelab/sbi/sbi/analysis/plotting_classes.py found in paper text by Syntology unverified Apache-2.0 (permissive) · 9e075659a09f2c50 · report
get_kde mackelab/sbi/sbi/analysis/plot.py found in paper text by Syntology unverified Apache-2.0 (permissive) · f75a8e2562862bbf · report
median_heuristic mackelab/sbi/sbi/diagnostics/misspecification.py found in paper text by Syntology unverified Apache-2.0 (permissive) · 982bf515ea600367 · report
permute_data mackelab/sbi/sbi/diagnostics/lc2st.py found in paper text by Syntology unverified Apache-2.0 (permissive) · 4b99ed3c2e6b6613 · report
rbf_kernel mackelab/sbi/sbi/diagnostics/misspecification.py found in paper text by Syntology unverified Apache-2.0 (permissive) · 62ed8bd9e7daa553 · report

Tasks

Time Series

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

ABC

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