World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
12504
World Ranking
10974
National Ranking
190

Overview

Martin Jaggi is affiliated with the École Polytechnique Fédérale de Lausanne in Switzerland. Their research primarily falls within the domain of computer science, with a notable focus on artificial intelligence. The scientist has contributed extensively to areas including artificial intelligence, computer vision and pattern recognition, computer networks and communications, computational mechanics, and electrical and electronic engineering.

The main topical areas of their work include stochastic gradient optimization techniques, privacy-preserving technologies in data, topic modeling, natural language processing techniques, adversarial robustness in machine learning, advanced neural network applications, and sparse and compressive sensing techniques.

Martin Jaggi has published numerous articles, with a considerable number in the following venues:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • npj Digital Medicine
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • SSRN Electronic Journal

Some of their recent publications include:

  • "Advances and Open Problems in Federated Learning," 2020, Foundations and Trends® in Machine Learning
  • "Ensemble Distillation for Robust Model Fusion in Federated Learning," 2020, arXiv (Cornell University)
  • "A Field Guide to Federated Optimization," 2021, arXiv (Cornell University)
  • "MEDITRON-70B: Scaling Medical Pretraining for Large Language Models," 2023, arXiv (Cornell University)
  • "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning," 2020, arXiv (Cornell University)

Frequent collaborators working with Martin Jaggi include:

  • Sebastian U. Stich
  • Sai Praneeth Karimireddy
  • Mary-Anne Hartley
  • Thijs Vogels
  • Amirkeivan Mohtashami

Best Publications

  • Advances and Open Problems in Federated Learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Advances and open problems in federated learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features

    Unknown

  • Ensemble Distillation for Robust Model Fusion in Federated Learning

    Tao Lin;Lingjing Kong;Sebastian U. Stich;Martin Jaggi

  • Learning Aerial Image Segmentation From Online Maps

    Pascal Kaiser;Jan Dirk Wegner;Aurelien Lucchi;Martin Jaggi

  • Unsupervised Scalable Representation Learning for Multivariate Time Series

    Jean-Yves Franceschi;Aymeric Dieuleveut;Aymeric Dieuleveut;Martin Jaggi

  • Error Feedback Fixes SignSGD and other Gradient Compression Schemes.

    Sai Praneeth Reddy Karimireddy;Quentin Rebjock;Sebastian Urban Stich;Martin Jaggi

  • Evaluating The Search Phase of Neural Architecture Search

    Kaicheng Yu;Christian Sciuto;Martin Jaggi;Claudiu Musat

  • Don't Use Large Mini-batches, Use Local SGD

    Tao Lin;Sebastian U. Stich;Kumar Kshitij Patel;Martin Jaggi

  • On the Relationship between Self-Attention and Convolutional Layers

    Jean-Baptiste Cordonnier;Andreas Loukas;Martin Jaggi

  • Distributed optimization with arbitrary local solvers

    Chenxin Ma;Jakub Konečný;Martin Jaggi;Virginia Smith

  • A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

    Anastasiia Koloskova;Nicolas Loizou;Sadra Boreiri;Martin Jaggi

  • A Field Guide to Federated Optimization

    Jianyu Wang;Zachary Charles;Zheng Xu;Gauri Joshi

  • PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

    Thijs Vogels;Sai Praneeth Karimireddy;Martin Jaggi

  • Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

    Jan Deriu;Aurelien Lucchi;Valeria De Luca;Aliaksei Severyn

  • SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

    Jan Deriu;Maurice Gonzenbach;Fatih Uzdilli;Aurélien Lucchi

  • Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

    Sai Praneeth Karimireddy;Martin Jaggi;Satyen Kale;Mehryar Mohri

  • Dynamic Model Pruning with Feedback

    Tao Lin;Sebastian U. Stich;Luis Barba;Daniil Dmitriev

  • Decentralized Deep Learning with Arbitrary Communication Compression

    Anastasia Koloskova;Tao Lin;Sebastian U Stich;Martin Jaggi

  • Sparse Convex Optimization Methods for Machine Learning

    Unknown

  • Multi-Head Attention: Collaborate Instead of Concatenate

    Jean-Baptiste Cordonnier;Andreas Loukas;Martin Jaggi

  • Model Fusion via Optimal Transport

    Sidak Pal Singh;Martin Jaggi

Frequent Co-Authors

Mehryar Mohri
Mehryar Mohri Google (United States)
Mathieu Salzmann
Mathieu Salzmann École Polytechnique Fédérale de Lausanne
H. Brendan McMahan
H. Brendan McMahan Google (United States)
Dawn Song
Dawn Song University of California, Berkeley
Peter Richtárik
Peter Richtárik King Abdullah University of Science and Technology
Felix X. Yu
Felix X. Yu Google (United States)
Satyen Kale
Satyen Kale Google (United States)
Phillip B. Gibbons
Phillip B. Gibbons Carnegie Mellon University
Sashank J. Reddi
Sashank J. Reddi Google (United States)

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