Publications related to Multi-Gradient Descent for Multi-Objective Recommender Systems

Deep Learning Theory Through the Lens of Diagonal Linear Networks

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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EPFL2024

Extensions of Peer Prediction Incentive Mechanisms

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As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

EPFL2024

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

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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 ...

EPFL2024

Explainable Face Verification via Feature-Guided Gradient Backpropagation

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Recent years have witnessed significant advance- ment in face recognition (FR) techniques, with their applications widely spread in people’s lives and security-sensitive areas. There is a growing need for reliable interpretations of decisions of such syste ...

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On the Generalization of Stochastic Gradient Descent with Momentum

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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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Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

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Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

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In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

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Ultrahigh-quality-factor micro- and nanomechanical resonators using dissipation dilution

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Mechanical resonators are widely used in sensors, transducers and optomechanical systems, where mechanical dissipation sets the ultimate limit to performance. Over the past 15 years, the quality factors in strained mechanical resonators have increased by f ...

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Scalable constrained optimization

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Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

EPFL2024

Multi-Gradient Descent for Multi-Objective Recommender Systems

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Extensions of Peer Prediction Incentive Mechanisms

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Topics in statistical physics of high-dimensional machine learning

Explainable Face Verification via Feature-Guided Gradient Backpropagation

On the Generalization of Stochastic Gradient Descent with Momentum

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Ultrahigh-quality-factor micro- and nanomechanical resonators using dissipation dilution

Scalable constrained optimization

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Extensions of Peer Prediction Incentive Mechanisms

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

On the Generalization of Stochastic Gradient Descent with Momentum

Topics in statistical physics of high-dimensional machine learning

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Ultrahigh-quality-factor micro- and nanomechanical resonators using dissipation dilution

Scalable constrained optimization