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Learning in Gaussian Markov random fields

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Learning in Gaussian Markov random fields

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Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Regularization for distributionally robust state estimation and prediction

Regularization for distributionally robust state estimation and prediction

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

The Impact of Changes in Resolution on the Persistent Homology of Images

Bayesian regression explains how human participants handle parameter uncertainty

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks.

An Inertial Parallel and Asynchronous Forward-Backward Iteration for Distributed Convex Optimization

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Distributed State Estimation and Cooperative Path-Following Under Communication Constraints

Regularization for distributionally robust state estimation and prediction

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Bayesian regression explains how human participants handle parameter uncertainty

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks.

Distributed State Estimation and Cooperative Path-Following Under Communication Constraints

Regularization for distributionally robust state estimation and prediction

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

The Impact of Changes in Resolution on the Persistent Homology of Images

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

An Inertial Parallel and Asynchronous Forward-Backward Iteration for Distributed Convex Optimization