Distributed Batch Gaussian Process Optimization. - Erik A. Daxberger; Bryan Kian Hsiang Low (ICML, 2017)

Embedding Models for Episodic Memory. - Yunpu Ma; Volker Tresp; Erik A. Daxberger (CoRR, 2018)

Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection. - Erik A. Daxberger; José Miguel Hernández-Lobato (CoRR, 2019)

Embedding models for episodic knowledge graphs. - Yunpu Ma; Volker Tresp; Erik A. Daxberger (J. Web Semant., 2019)

Mixed-Variable Bayesian Optimization. - Erik A. Daxberger; Anastasia Makarova; Matteo Turchetta; Andreas Krause 001 (IJCAI, 2020)

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. - Austin Tripp; Erik A. Daxberger; José Miguel Hernández-Lobato (NeurIPS, 2020)

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. - Erik A. Daxberger; Eric T. Nalisnick; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2020)

Bayesian Deep Learning via Subnetwork Inference. - Erik A. Daxberger; Eric T. Nalisnick; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (ICML, 2021)

Laplace Redux - Effortless Bayesian Deep Learning. - Erik A. Daxberger; Agustinus Kristiadi; Alexander Immer; Runa Eschenhagen; Matthias Bauer 001; Philipp Hennig (NeurIPS, 2021)

Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. - Runa Eschenhagen; Erik A. Daxberger; Philipp Hennig; Agustinus Kristiadi (CoRR, 2021)

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. - Javier Antorán; David Janz; James Urquhart Allingham; Erik A. Daxberger; Riccardo Barbano; Eric T. Nalisnick; José Miguel Hernández-Lobato (ICML, 2022)

Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts. - Erik A. Daxberger; Floris Weers; Bowen Zhang 002; Tom Gunter; Ruoming Pang; Marcin Eichner; Michael Emmersberger; Yinfei Yang; Alexander Toshev; Xianzhi Du (CoRR, 2023)

Improving Continual Learning by Accurate Gradient Reconstructions of the Past. - Erik A. Daxberger; Siddharth Swaroop; Kazuki Osawa; Rio Yokota; Richard E. Turner; José Miguel Hernández-Lobato; Mohammad Emtiyaz Khan (Trans. Mach. Learn. Res., 2023)

MM-Ego: Towards Building Egocentric Multimodal LLMs. - Hanrong Ye; Haotian Zhang 005; Erik A. Daxberger; Lin Chen 010; Zongyu Lin; Yanghao Li; Bowen Zhang 002; Haoxuan You; Dan Xu 002; Zhe Gan; Jiasen Lu; Yinfei Yang (CoRR, 2024)

MMEgo: Towards Building Egocentric Multimodal LLMs for Video QA. - Hanrong Ye; Haotian Zhang 005; Erik A. Daxberger; Lin Chen 010; Zongyu Lin; Yanghao Li; Bowen Zhang 002; Haoxuan You; Dan Xu 002; Zhe Gan; Jiasen Lu; Yinfei Yang (ICLR, 2025)

MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs. - Erik A. Daxberger; Nina Wenzel; David Griffiths; Haiming Gang; Justin Lazarow; Gefen Kohavi; Kai Kang; Marcin Eichner; Yinfei Yang; Afshin Dehghan; Peter Grasch (CoRR, 2025)