Publications

(2022). Quantum Bayesian Neural Networks. In AABI.

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(2022). Probing as Quantifying the Inductive Bias of Pre-trained Representations. In ACL.

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(2022). Priors in Bayesian Deep Learning: A Review. In International Statistical Review.

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(2022). Pathologies in priors and inference for Bayesian transformers. In AABI.

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(2022). On Disentanglement in Gaussian Process Variational Autoencoders. In AABI.

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(2022). Neural Variational Gradient Descent. In AABI.

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(2022). Deep Classifiers with Label Noise Modeling and Distance Awareness. In AABI.

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(2022). Bayesian Neural Network Priors Revisited. In ICLR.

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(2021). On the Choice of Priors in Bayesian Deep Learning. PhD thesis.

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(2021). T-DPSOM: An Interpretable Clustering Method for Unsupervised Learning of Patient Health States. In ACM CHIL.

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(2021). Sparse MoEs meet Efficient Ensembles. In arXiv.

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(2021). Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. In ICML.

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(2021). Scalable Gaussian Processes on Discrete Domains. In IEEE Access.

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(2021). Scalable Gaussian Process Variational Autoencoders. In AISTATS.

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(2021). Repulsive Deep Ensembles are Bayesian. In NeurIPS (spotlight).

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(2021). PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. In ICML.

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(2021). On Stein Variational Neural Network Ensembles. In arXiv.

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(2021). Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations. In PLOS Computational Biology.

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(2021). MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis. In PLOS ONE.

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(2021). Factorized Gaussian Process Variational Autoencoders. In AABI.

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(2021). Exact Langevin Dynamics with Stochastic Gradients. In AABI.

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(2021). Data augmentation in Bayesian neural networks and the cold posterior effect. In arXiv.

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(2021). BNNpriors: A library for Bayesian neural network inference with different prior distributions. In Software Impacts.

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(2021). Annealed Stein Variational Gradient Descent. In AABI.

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(2021). A Bayesian Approach to Invariant Deep Neural Networks. In ICML workshop on Uncertainty and Robustness in Deep Learning.

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(2020). Sparse Gaussian Process Variational Autoencoders. In arXiv.

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(2020). GP-VAE: Deep Probabilistic Time Series Imputation. In AISTATS.

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(2020). Conservative Uncertainty Estimation By Fitting Prior Networks. In ICLR.

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(2019). SOM-VAE: Interpretable Discrete Representation Learning on Time Series. In ICLR.

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(2019). META^2: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning. In MLCB.

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(2019). Meta-Learning Mean Functions for Gaussian Processes. In NeurIPS workshop on Bayesian Deep Learning.

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(2019). DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps. In NeurIPS workshop on Machine Learning for Health.

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(2018). On the Connection between Neural Processes and Gaussian Processes with Deep Kernels. In NeurIPS workshop on Bayesian Deep Learning.

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(2018). InspireMe: Learning Sequence Models for Stories. In AAAI.

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