Emerging Field “Learning-Informed Optimization”

EF Learning-Informed Optimization focuses on theory and algorithms to combine optimization with learning aspects. This, in particular, includes:

• Devising new dynamic learning algorithms that can be incorporated into optimization setups.
• Analyzing how dynamic information, learned, e.g., from a physical process, can be used to improve optimization methods, e.g., data-adaptive methods.
• Leveraging new computational paradigms to improve both optimization, learning, and their interplay, e.g., quantum-assisted machine learning.

Previous denomination: Extracting Dynamical Laws from Complex Data (EF1)

Scientists in Charge: Jens Eisert, Gabriele Steidl, Max Zimmer

• • EF1-11: Quantum Advantages in Machine Learning
    Klaus-Robert Müller, Jens Eisert
  • EF1-16: Quiver Representations in Big Data and Machine Learning
    Alexander Schmitt
  • EF1-20: Uncertainty Quantification and Design of Experiment for Data-Driven Control
    Claudia Schillings
  • EF1-21: Scaling up Flag Algebras in Combinatorics
    Sebastian Pokutta, Christoph Spiegel
  • EF1-23: On a Frank-Wolfe Approach for Abs-Smooth Optimization
    Sebastian Pokutta, Andrea Walther, Zev Woodstock
  • EF1-24: Expanding Merlin-Arthur Classifiers Interpretable Neural Networks through Interactive Proof Systems
    Sebastian Pokutta, Stephan Wäldchen

The projects EF1-14, -15, -18, -22, and -25 have been moved to Application Area 5: Variational Problems in Data-Driven Applications.

Completed projects:

• • EF1-1: Quantifying Uncertainties in Explainable AI
    Gitta Kutyniok, Klaus-Robert Müller, Wojciech Samek
  • EF1-2: Quantum Kinetics
    Klaus-Robert Müller, Frank Noé
  • EF1-3: Approximate Convex Hulls With Bounded Complexity
    Michael Joswig, Klaus-Robert Müller
  • EF1-4: Extracting Dynamical Laws by Deep Neural Networks: A Theoretical Perspective
    Jens Eisert, Frank Noé, Barbara Zwicknagl
  • EF1-5: On Robustness of Deep Neural Networks
    Christian Bayer, Peter Karl Friz
  • EF1-6: Graph Embedding for Analyzing the Microbiome
    Tim Conrad, Stefan Klus, Gregoire Montavon
  • EF1-7: Quantum Machine Learning
    Jens Eisert, Klaus-Robert Müller
  • EF1-8: Incorporating Locality into Fast(er) Learning
    Nicole Mücke
  • EF1-9: Adaptive Algorithms through Machine Learning: Exploiting Interactions in Integer Programming
    Ambros Gleixner, Sebastian Pokutta
  • EF1-10: Kernel Ensemble Kalman Filter and Inference
    Péter Koltai, Nicolas Perkowski
  • EF1-12: Learning Extremal Structures in Combinatorics
    Sebastian Pokutta, Tibor Szabó
  • EF1-13: Stochastic and Rough Aspects in Deep Neural Networks
    Christian Bayer, Peter-Karl Friz
  • EF1-17: Data-Driven Robust Model Predictive Control under Distribution Shift
    Jia-Jie Zhu, Michael Hintermüller
  • EF1-19: Machine Learning Enhanced Filtering Methods for Inverse Problems
    Claudia Schillings