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09/14/2023
Hodge-Aware Contrastive Learning
Simplicial complexes prove effective in modeling data with multiway depe...
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06/06/2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Selecting hyperparameters in deep learning greatly impacts its effective...
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05/26/2023
Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference
Deep neural networks (DNNs) have found successful applications in many f...
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04/17/2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
The linearized-Laplace approximation (LLA) has been shown to be effectiv...
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10/13/2022
On the Identifiability and Estimation of Causal Location-Scale Noise Models
We study the class of location-scale or heteroscedastic noise models (LS...
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02/22/2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
Data augmentation is commonly applied to improve performance of deep lea...
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10/15/2021
Probing as Quantifying the Inductive Bias of Pre-trained Representations
Pre-trained contextual representations have led to dramatic performance ...
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10/08/2021
Pathologies in priors and inference for Bayesian transformers
In recent years, the transformer has established itself as a workhorse i...
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06/28/2021
Laplace Redux – Effortless Bayesian Deep Learning
Bayesian formulations of deep learning have been shown to have compellin...
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04/11/2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Marginal-likelihood based model-selection, even though promising, is rar...
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08/19/2020
Improving predictions of Bayesian neural networks via local linearization
In this paper we argue that in Bayesian deep learning, the frequently ut...
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07/21/2020
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks
In this thesis, we disentangle the generalized Gauss-Newton and approxim...
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04/29/2020
Continual Deep Learning by Functional Regularisation of Memorable Past
Continually learning new skills is important for intelligent systems, ye...
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06/17/2019
Variational Inference with Numerical Derivatives: variance reduction through coupling
The Black Box Variational Inference (Ranganath et al. (2014)) algorithm ...
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06/05/2019
Approximate Inference Turns Deep Networks into Gaussian Processes
Deep neural networks (DNN) and Gaussian processes (GP) are two powerful ...
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11/28/2017
