From d4754c76b8eee82478b897ec7b29cd20dec0e1bb Mon Sep 17 00:00:00 2001 From: Emanuel Sommer Date: Mon, 6 Jul 2026 01:12:02 +0200 Subject: [PATCH 1/7] =?UTF-8?q?=F0=9F=93=9D=20Resolve=20issue=2085?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- bde/bde.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bde/bde.py b/bde/bde.py index a9a2dba..f9832b3 100644 --- a/bde/bde.py +++ b/bde/bde.py @@ -843,7 +843,7 @@ def __init__( seed : int, default=0 Shared PRNG seed for member initialisation and sampling. loss : BaseLoss | None - Custom training loss; defaults to :class:`bde.loss.GaussianNLL`. + Custom training loss; defaults to :class:`bde.loss.CategoricalCrossEntropy`. activation : str, default='relu' Activation function applied to each hidden layer. epochs : int, default=20 From f2284d477d2408a137fe38c22be3537732b8b6a4 Mon Sep 17 00:00:00 2001 From: Emanuel Sommer Date: Mon, 6 Jul 2026 01:14:38 +0200 Subject: [PATCH 2/7] =?UTF-8?q?=E2=9C=8F=EF=B8=8F=20resolve=20issue=2084?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 0327b7b..dfdc59f 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -97,7 +97,7 @@ Classification follows analogously using `BdeClassifier`. # Regression benchmark -We provide a small benchmark of `bde` on the `airfoil` [@Dua_2019] and the `bikesharing` [@misc_bike_sharing_dataset_275] datasets. We report mean predictive performance (RMSE), UQ metrics (NLL in the distributional and mean regression formulation), reported as mean ± standard deviation over 5 independent runs. The results show competitive out-of-the-box performance of BDE especially in UQ with its native distributional regression capability. +We provide a small benchmark of `bde` on the `airfoil` [@Dua_2019] and the `bikesharing` [@misc_bike_sharing_dataset_275] datasets. We report mean predictive performance (RMSE), UQ metrics (NLL in the distributional and mean regression formulation), reported as mean ± standard deviation over 5 independent model seeds and fixed train/test splits. The results show competitive out-of-the-box performance of BDE especially in UQ with its native distributional regression capability. | `airfoil` | RMSE | NLL (distr. regr.) | NLL (mean regr.) | | ------------- | ------------------- | -------------------- | -------------------- | From 9dc0c498152c69ec44fb38b0d8b74f87bb942eb3 Mon Sep 17 00:00:00 2001 From: Emanuel Sommer Date: Mon, 6 Jul 2026 01:39:14 +0200 Subject: [PATCH 3/7] =?UTF-8?q?=E2=9E=96=20resolve=20issue=20#86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 3 + pixi.lock | 863 ++++++++++++++++++++----------------------------- pyproject.toml | 6 +- 3 files changed, 359 insertions(+), 513 deletions(-) diff --git a/README.md b/README.md index 52b6124..1200da0 100644 --- a/README.md +++ b/README.md @@ -63,6 +63,9 @@ pixi run python -m examples.example Pixi ensures the correct JAX, CUDA (when needed), and scikit-learn versions are selected automatically. See `pixi.lock` for channel and platform details. +For contributor tooling, use the `dev` Pixi environment. It combines the +documentation, linting, and test environments. +as IPython. 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"ipython>=8.0", ] classifiers = [ @@ -119,11 +118,14 @@ sphinx-prompt = "*" build-doc = { cmd = "make html", cwd = "doc" } clean-doc = { cmd = "rm -rf _build", cwd = "doc" } +[tool.pixi.feature.dev.dependencies] +ipython = ">=8.0" + [tool.pixi.environments] doc = ["doc"] lint = ["lint"] test = ["test"] -dev = ["doc", "lint", "test"] +dev = ["doc", "lint", "test", "dev"] [tool.black] line-length = 88 From 15768bdb103ac2be893d68ad13d5d383fa4570ba Mon Sep 17 00:00:00 2001 From: Emanuel Sommer Date: Mon, 6 Jul 2026 01:47:49 +0200 Subject: [PATCH 4/7] =?UTF-8?q?=F0=9F=93=9D=20add=20contributing=20guide?= =?UTF-8?q?=20(issue=20#83)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- CONTRIBUTING.md | 43 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..fece09b --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,43 @@ +# Contributing to bde + +Thanks for your interest in improving `bde`. We welcome bug reports, +documentation fixes, examples, tests, and focused code contributions. + +Please use GitHub as the default place for project communication: + +- Issues: https://github.com/scikit-learn-contrib/bde/issues +- Pull requests: https://github.com/scikit-learn-contrib/bde/pulls + +## Contributing changes + +For small fixes, open a pull request directly. For larger changes or API changes, +please open an issue first so the approach can be discussed. + +Recommended workflow: + +1. Fork the repository. +2. Create a branch for your change. +3. Install the development environment as outlined in the README and do not forget to also install the `pre-commit` hooks. +4. Add tests or documentation when relevant. +5. Run the relevant checks (lint,tests). +6. Open a pull request on GitHub and link any related issue. + +## Reporting issues + +Please report bugs, feature requests, and documentation problems through GitHub +Issues. Before opening a new issue, check whether a similar issue already exists. + +For bug reports, include: + +- A minimal reproducible example. +- The full traceback or error message. +- Your operating system, Python version, and relevant package versions. +- The expected behavior and the behavior you observed. + +## Seeking support + +Please use GitHub Issues for usage questions and support requests. This keeps +answers visible to other users and helps the project maintain a searchable record. + +Direct email to the maintainers should be reserved for rare cases that are not +appropriate for a public GitHub issue, such as sensitive or private matters. From 1c3e8a56a7444c79a027feed0c8c2114b2fad408 Mon Sep 17 00:00:00 2001 From: Emanuel Sommer Date: Mon, 6 Jul 2026 16:33:14 +0200 Subject: [PATCH 5/7] =?UTF-8?q?=E2=9C=8F=EF=B8=8F=20resolve=20issue=20#87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- paper/paper.bib | 41 ++++++++++++++++++++++++++++++++++++++++- paper/paper.md | 3 ++- 2 files changed, 42 insertions(+), 2 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 122046d..ea62792 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -5,6 +5,14 @@ @inproceedings{sommer2025mile year={2025}, } +@inproceedings{sommer2026smile, + title = {{Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?}}, + author = {Emanuel Sommer and Kangning Diao and Jakob Robnik and Uroš Seljak and David R{\"u}gamer}, + booktitle={Forty-third International Conference on Machine Learning}, + year={2026}, + publisher={PMLR}, +} + @inproceedings{sommer2024connecting, title={Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?}, author={Sommer, Emanuel and Wimmer, Lisa and Papamarkou, Theodore and Bothmann, Ludwig and Bischl, Bernd and R{\"u}gamer, David}, @@ -21,6 +29,38 @@ @InProceedings{papamarkou2024position publisher = {PMLR} } +@article{pymc2023, + title = {{PyMC}: A Modern and Comprehensive Probabilistic Programming Framework in {P}ython}, + author = {Oriol Abril-Pla and Virgile Andreani and Colin Carroll and Larry Dong and Christopher J. Fonnesbeck and Maxim Kochurov and Ravin Kumar and Junpeng Lao and Christian C. Luhmann and Osvaldo A. Martin and Michael Osthege and Ricardo Vieira and Thomas Wiecki and Robert Zinkov }, + journal = {{PeerJ} Computer Science}, + volume = {9}, + number = {e1516}, + doi = {10.7717/peerj-cs.1516}, + year = {2023} +} + +@article{phan2019numpyro, + title={Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro}, + author={Phan, Du and Pradhan, Neeraj and Jankowiak, Martin}, + journal={arXiv preprint arXiv:1912.11554}, + year={2019} +} +@inproceedings{daxberger2021laplace, + title = {Laplace {{Redux}} {\textendash} {{Effortless Bayesian Deep Learning}}}, + booktitle = {35th {{Conference}} on {{Neural Information Processing Systems}} ({{NeurIPS}} 2021)}, + author = {Daxberger, Erik and Kristiadi, Agustinus and Immer, Alexander and Eschenhagen, Runa and Bauer, Matthias and Hennig, Philipp}, + year = {2021}, + keywords = {approximate\_inference,bnn,gaussian\_posterior,laplace,last\_layer,uncertainty\_quantification} +} + +@inproceedings{ + duffield2025scalable, + title={Scalable Bayesian Learning with posteriors}, + author={Samuel Duffield and Kaelan Donatella and Johnathan Chiu and Phoebe Klett and Daniel Simpson}, + booktitle={The Thirteenth International Conference on Learning Representations}, + year={2025} +} + @misc{cabezas2024blackjax, title={{BlackJAX: Composable Bayesian inference in JAX}}, author={Alberto Cabezas and Adrien Corenflos and Junpeng Lao and Rémi Louf}, @@ -92,4 +132,3 @@ @misc{misc_bike_sharing_dataset_275 year = {2013}, howpublished = {UCI Machine Learning Repository} } - diff --git a/paper/paper.md b/paper/paper.md index dfdc59f..e81f7ac 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -47,10 +47,11 @@ The workflow of `bde` implements the two-stage BDE inference process of MILE. Fi # Software design Because optimization and sampling across ensemble members are independent, `bde` exploits JAX’s parallelization and just-in-time compilation to scale efficiently across CPUs, GPUs, and TPUs. Given new test data, the package approximates the posterior predictive, enabling point predictions, credible intervals, coverage estimates, and other uncertainty metrics through a unified interface. +Further, this work currently targets the well-studied and empirically validated full-batch, fully connected-network setting for tabular data also studied in MILE [@sommer2025mile]. This makes it particularly suitable for the chosen scikit-learn framework. Stochastic-gradient variants such as SMILE [@sommer2026smile] require different algorithmic and software design choices, yet will be considered for future extensions. # State of the field -Reliable uncertainty quantification (UQ) is increasingly viewed as a critical component of modern machine learning systems, and BDL provides a principled framework for achieving it [@papamarkou2024position]. While several libraries support optimization-based approaches such as variational inference or classical Bayesian modeling, accessible tools for sampling-based inference in Bayesian neural networks remain scarce. Existing probabilistic programming frameworks offer MCMC but require substantial manual configuration to achieve competitive performance on neural network models. +Reliable uncertainty quantification (UQ) is increasingly viewed as a critical component of modern machine learning systems, and BDL provides a principled framework for achieving it [@papamarkou2024position]. While several libraries support optimization-based approaches such as variational inference [@duffield2025scalable], Laplace approximations [@daxberger2021laplace], or classical Bayesian modeling [@pymc2023], accessible tools for sampling-based inference in Bayesian neural networks remain scarce. Existing probabilistic programming and inference frameworks such as NumPyro [@phan2019numpyro] and `blackjax` [@cabezas2024blackjax] offer MCMC building blocks but require substantial manual configuration to achieve competitive Bayesian neural-network workflows. # Statement of need From 33b6354b330d75ee6e0e5a144906cc8a58754e80 Mon Sep 17 00:00:00 2001 From: Emanuel Sommer Date: Mon, 6 Jul 2026 16:45:25 +0200 Subject: [PATCH 6/7] =?UTF-8?q?=E2=9E=95=20reintroduce=20iPython=20for=20p?= =?UTF-8?q?rogress=20bars?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 1 - pixi.lock | 651 +++++++++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 6 +- 3 files changed, 653 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 1200da0..6db6d08 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,6 @@ selected automatically. See `pixi.lock` for channel and platform details. For contributor tooling, use the `dev` Pixi environment. It combines the documentation, linting, and test environments. -as IPython. 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executing>=1.2.0 + - asttokens>=2.1.0 + - pure-eval + - pytest ; extra == 'tests' + - typeguard ; extra == 'tests' + - pygments ; extra == 'tests' + - littleutils ; extra == 'tests' + - cython ; extra == 'tests' - pypi: https://files.pythonhosted.org/packages/f3/fa/abe89019d8d8815c8781e90d697dec52523fb8ebe308bf11664e8de1877e/websockets-16.0-cp311-cp311-macosx_10_9_x86_64.whl name: websockets version: '16.0' sha256: 417b28978cdccab24f46400586d128366313e8a96312e4b9362a4af504f3bbad requires_python: '>=3.10' +- pypi: https://files.pythonhosted.org/packages/f4/7e/a72dd26f3b0f4f2bf1dd8923c85f7ceb43172af56d63c7383eb62b332364/pygments-2.20.0-py3-none-any.whl + name: pygments + version: 2.20.0 + sha256: 81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176 + requires_dist: + - colorama>=0.4.6 ; extra == 'windows-terminal' + requires_python: '>=3.9' - pypi: https://files.pythonhosted.org/packages/f5/f0/0cfadd537c5470378b1b32bd859cf2824972174b51b873c9d95cfd7475a5/ml_dtypes-0.5.4-cp312-cp312-win_amd64.whl name: ml-dtypes version: 0.5.4 diff --git a/pyproject.toml b/pyproject.toml index df62481..0f95f28 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,6 +22,7 @@ dependencies = [ "optax>=0.2.1", "blackjax==1.2.2", "tqdm>=4.67.1", + "ipython>=8.0", ] classifiers = [ @@ -118,14 +119,11 @@ sphinx-prompt = "*" build-doc = { cmd = "make html", cwd = "doc" } clean-doc = { cmd = "rm -rf _build", cwd = "doc" } -[tool.pixi.feature.dev.dependencies] -ipython = ">=8.0" - [tool.pixi.environments] doc = ["doc"] lint = ["lint"] test = ["test"] -dev = ["doc", "lint", "test", "dev"] +dev = ["doc", "lint", "test"] [tool.black] line-length = 88 From b21e669d7a8cc1762d51273b4093972dbcbdcf97 Mon Sep 17 00:00:00 2001 From: Emanuel Sommer Date: Mon, 6 Jul 2026 16:52:52 +0200 Subject: [PATCH 7/7] =?UTF-8?q?=F0=9F=90=9B=20add=20root=20to=20rmse=20los?= =?UTF-8?q?s?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- bde/loss/loss.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bde/loss/loss.py b/bde/loss/loss.py index 59dd020..93b0a9e 100644 --- a/bde/loss/loss.py +++ b/bde/loss/loss.py @@ -26,8 +26,8 @@ def name(self) -> str: def __call__(self, preds: ArrayLike, y_true: ArrayLike): mu = preds[..., 0:1] - rmse = (mu - y_true) ** 2 - return jnp.mean(rmse) + squared_error = (mu - y_true) ** 2 + return jnp.sqrt(jnp.mean(squared_error)) class GaussianNLL(BaseLoss):