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Personne

Martin Jaggi

Enseignement et PhD Doctorant·es actuel·les El Mahdi Chayti, Dongyang Fan, Alexander Hägele, Simla Burcu Harma, Bettina Ursula Messmer, Diba Hashemi, Matteo Pagliardini, Vinko Sabolcec, Simin Fan, Alejandro Hernández Cano A dirigé les thèses EPFL de Prakhar Gupta, Sai Praneeth Reddy Karimireddy, Tao Lin, Jean-Baptiste Cordonnier, Thijs Vogels, Lie He, Anastasiia Koloskova, Mohtashami Amirkeivan, Atli Kosson A co-dirigé les thèses EPFL de Mario Paulo Drumond Lages De Oliveira, Vinitra Swamy Cours Optimization for machine learning CS-439 Ce cours donne un aperçu des méthodes d'optimisation modernes, pour les applications dans l'apprentissage automatique et la science des données. En particulier, l'évolutivité des algorithmes vers de grands ensembles de données sera discutée en théorie et en mise en œuvre. Topics in Machine Learning Systems CS-723

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Cours enseignés par cette personne (2)

CS-439: Optimization for machine learning

This course teaches an overview of modern optimization methods, for applications in machine learning and data science. In particular, scalability of algorithms to large datasets will be discussed in theory and in implementation. ...

CS-723: Topics in Machine Learning Systems

This course will cover the latest technologies, platforms and research contributions in the area of machine learning systems. The students will read, review and present papers from recent venues across the systems for ML spectrum. ...

Publications associées (76)

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Daniel Kuhn is Professor of Operations Research at the College of Management of Technology at EPFL, where he holds the Chair of Risk Analytics and Optimization (RAO). His current research interests are focused on data-driven optimization, the development of efficient computational methods for the solution of stochastic and robust optimization problems and the design of approximation schemes that ensure their computational tractability. This work is primarily application-driven, the main application areas being engineered systems, machine learning, business analytics and finance. Before joining EPFL, Daniel Kuhn was a faculty member in the Department of Computing at Imperial College London (2007-2013) and a postdoctoral research associate in the Department of Management Science and Engineering at Stanford University (2005-2006). He holds a PhD degree in Economics from University of St. Gallen and an MSc degree in Theoretical Physics from ETH Zurich. He is the editor-in-chief of Mathematical Programming.

Martin Jaggi

Graph Chatbot

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

Forecasting intracranial hypertension using multi-scale waveform metrics

Unsupervised Robust Nonparametric Learning Of Hidden Community Properties