@conference {694,
	title = {Paths and Ambient Spaces in Neural Loss Landscapes},
	booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
	year = {2025},
	month = {03{\textendash}05 May},
	pages = {10{\textendash}18},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {Understanding the structure of neural network loss surfaces, particularly the emergence of low-loss tunnels, is critical for advancing neural network theory and practice. In this paper, we propose a novel approach to directly embed loss tunnels into the loss landscape of neural networks. Exploring the properties of these loss tunnels offers new insights into their length and structure and sheds light on some common misconceptions. We then apply our approach to Bayesian neural networks, where we improve subspace inference by identifying pitfalls and proposing a more natural prior that better guides the sampling procedure.},
	url = {https://proceedings.mlr.press/v258/dold25a.html},
	author = {Dold, Daniel and Kobialka, Julius and Palm, Nicolai and Sommer, Emanuel and R{\"u}gamer, David and D{\"u}rr, Oliver},
	editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}
}
@conference {693,
	title = {Bayesian Semi-structured Subspace Inference},
	booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
	year = {2024},
	month = {02{\textendash}04 May},
	pages = {1819{\textendash}1827},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {Semi-structured regression models enable the joint modeling of interpretable structured and complex unstructured feature effects. The structured model part is inspired by statistical models and can be used to infer the input-output relationship for features of particular importance. The complex unstructured part defines an arbitrary deep neural network and thereby provides enough flexibility to achieve competitive prediction performance. While these models can also account for aleatoric uncertainty, there is still a lack of work on accounting for epistemic uncertainty. In this paper, we address this problem by presenting a Bayesian approximation for semi-structured regression models using subspace inference. To this end, we extend subspace inference for joint posterior sampling from a full parameter space for structured effects and a subspace for unstructured effects. Apart from this hybrid sampling scheme, our method allows for tunable complexity of the subspace and can capture multiple minima in the loss landscape. Numerical experiments validate our approach{\textquoteright}s efficacy in recovering structured effect parameter posteriors in semi-structured models and approaching the full-space posterior distribution of MCMC for increasing subspace dimension. Further, our approach exhibits competitive predictive performance across simulated and real-world datasets.},
	url = {https://proceedings.mlr.press/v238/dold24a.html},
	author = {Dold, Daniel and Ruegamer, David and Sick, Beate and D{\"u}rr, Oliver},
	editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}
}
