Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?

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In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

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Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

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The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drive ...

We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this an ...

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A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?

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Topics in statistical physics of high-dimensional machine learning

On the Generalization of Stochastic Gradient Descent with Momentum

Understanding generalization and robustness in modern deep learning

Predicting the long-term collective behaviour of fish pairs with deep learning

Meta-learning to address diverse Earth observation problems across resolutions

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Robust machine learning for neuroscientific inference

Partial discharge localization in power transformer tanks using machine learning methods

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Incorporating Projective Geometry into Deep Learning

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Boosting likelihood learning with event reweighting

Scalable constrained optimization

Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells

Mapping drivers of tropical forest loss with satellite image time series and machine learning

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Aiming beyond slight increases in accuracy

Multi-agent Learning with Privacy Guarantees