✏️ resolve issue #87

Author: EmanuelSommer

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}
 }
-

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