Publications related to TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Related publications (34)

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In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024

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We consider the problem of comparing several samples of stochastic processes with respect to their second-order structure, and describing the main modes of variation in this second order structure, if present. These tasks can be seen as an Analysis of Vari ...

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IEEE2023

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OXFORD UNIV PRESS2023

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EPFL2023

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Is it possible to detect if the sample paths of a stochastic process almost surely admit a finite expansion with respect to some/any basis? The determination is to be made on the basis of a finite collection of discretely/noisily observed sample paths. We ...

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Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. Because of the non-differentiable nature of common PnP solvers, these methods are supervised via the i ...

Ieee Computer Soc2023

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NATURE PORTFOLIO2023

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EPFL2023

DESI mock challenge Halo and galaxy catalogues with the bias assignment method

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EPFL2023

Small-scale dynamo with finite correlation times

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AMER PHYSICAL SOC2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

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eLIFE SCIENCES PUBL LTD2023

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2023

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Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

EPFL2023

Random Surface Covariance Estimation by Shifted Partial Tracing

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The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even ...

2023

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EPFL2022