Martin Jaggi

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Publications associées (84)

Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study

Martin Jaggi, Mariko Makhmutova

Background: In 2017, an estimated 17.3 million adults in the United States experienced at least one major depressive episode, with 35% of them not receiving any treatment. Underdiagnosis of depression has been attributed to many reasons, including stigma s ...

Jmir Publications, Inc2022

Effective Interplay Between Sparsity and Quantization: from Theory to Practice

Babak Falsafi, Martin Jaggi

The increasing size of deep neural networks (DNNs) necessitates effective model compression to reduce their computational and memory footprints. Sparsity and quantization are two prominent compression methods that have been shown to reduce DNNs' computatio ...

2022

High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

Martin Jaggi, Luis Felipe Barba Flores

The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task. A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-sc ...

SPRINGERNATURE2022

Masked Training of Neural Networks with Partial Gradients

Martin Jaggi, Amirkeivan Mohtashami, Sebastian Urban Stich

State-of-the-art training algorithms for deep learning models are based on stochastic gradient descent (SGD). Recently, many variations have been explored: perturbing parameters for better accuracy (such as in Extra-gradient), limiting SGD updates to a sub ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

what If...? Pandemic Policy-decision-support to Guide a Cost-benefit-optimised, Country- Specific Response

Martin Jaggi, Mary-Anne Hartley, Thierry Bossy, Tatjana Chavdarova, Prakhar Gupta

Background After 18 months of responding to the COVID-19 pandemic, there is still no agreement on the optimal combination of mitigation strategies. The efficacy and collateral damage of pandemic policies are dependent on constantly evolving viral epidemiol ...

PUBLIC LIBRARY SCIENCE2022

Learning from History for Byzantine Robust Optimization

Martin Jaggi, Sai Praneeth Reddy Karimireddy, Lie He

Byzantine robustness has received significant attention recently given its importance for distributed and federated learning. In spite of this, we identify severe flaws in existing algorithms even when the data across the participants is identically distri ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Semantic Perturbations with Normalizing Flows for Improved Generalization

Martin Jaggi, Tatjana Chavdarova, Sebastian Urban Stich

Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often limited to a fixed set of hard-coded transformations. Recently, several work ...

IEEE2021

Advances and Open Problems in Federated Learning

Martin Jaggi, Sebastian Urban Stich, Yang Liu, Lie He, Ayfer Özgür Aydin, Florian Tramèr, Qiang Yang, Ananda Theertha Suresh, Badih Ghazi

Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decen ...

NOW PUBLISHERS INC2021

Lightweight Cross-Lingual Sentence Representation Learning

Martin Jaggi, Prakhar Gupta, Zhuoyuan Mao

Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Martin Jaggi, Sebastian Urban Stich, Hossein Shokri Ghadikolaei

In distributed optimization, parameter updates from the gradient computing node devices have to be aggregated in every iteration on the orchestrating server. When these updates are sent over an arbitrary commodity network, bandwidth and latency can be limi ...

MICROTOME PUBLISHING2021

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Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study

Martin Jaggi, Mariko Makhmutova

Jmir Publications, Inc2022