Principled Parallel Mean-Field Inference for Discrete Random Fields

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Countless signal processing applications include the reconstruction of signals from few indirect linear measurements. The design of effective measurement operators is typically constrained by the underlying hardware and physics, posing a challenging and of ...

Principled Parallel Mean-Field Inference for Discrete Random Fields

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Understanding generalization and robustness in modern deep learning

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

Stochastic adaptive resampling for the estimation of discrete choice models

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Universal and adaptive methods for robust stochastic optimization

The statistical complexity of early-stopped mirror descent

Regularization Techniques for Low-Resource Machine Translation

DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications

Positive Definite Completions and Continuous Graphical Models

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Space Oddity? A Statistical Formulation of Conjunction Assessment

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

On the Double Descent of Random Features Models Trained with SGD

Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery