Publications

(2023). Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo. arXiv.

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(2023). Hodge-Aware Contrastive Learning. arXiv.

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(2023). A Primer on Bayesian Neural Networks: Review and Debates. arXiv.

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(2023). Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. AABI.

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(2023). Understanding Pathologies of Deep Heteroskedastic Regression. arXiv.

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(2023). Improving Neural Additive Models with Bayesian Principles. AABI.

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(2023). Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. AABI.

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(2023). Incorporating Unlabelled Data into Bayesian Neural Networks. arXiv.

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(2022). Sparse MoEs meet Efficient Ensembles. TMLR.

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(2022). Quantum Bayesian Neural Networks. AABI.

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(2022). Probing as Quantifying Inductive Bias. ACL.

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

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

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(2022). PAC-Bayesian Meta-Learning: From Theory to Practice. arXiv.

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(2022). On Interpretable Reranking-Based Dependency Parsing Systems. SwissText.

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

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(2022). Neural Variational Gradient Descent. AABI.

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(2022). Meta-learning richer priors for VAEs. AABI.

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(2022). Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. NeurIPS.

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

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(2022). Data augmentation in Bayesian neural networks and the cold posterior effect. UAI.

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(2022). Bayesian Neural Network Priors Revisited. 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. ACM CHIL.

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(2021). Sparse Gaussian Processes on Discrete Domains. IEEE Access.

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

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

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

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(2021). PCA Subspaces Are Not Always Optimal for Bayesian Learning. NeurIPS workshop on Distribution Shifts.

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

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(2021). On Stein Variational Neural Network Ensembles. ICML workshop on Uncertainty and Robustness in Deep Learning.

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

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

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

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

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

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

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

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

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

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

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

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

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

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(2018). Supervised learning on synthetic data for reverse engineering gene regulatory networks from experimental time-series. bioRxiv.

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

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

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