Program for stream Optimization for Inverse Problems and Machine Learning

WF-07: Regularization methods for Machine Learning and Inverse Problems
Stream: Optimization for Inverse Problems and Machine Learning
Room: M:I
Chair(s): Emanuele Naldi

• On the Bredies-Chenchene-Lorenz-Naldi algorithm
  Shambhavi Singh
• An optimal structured zeroth-order algorithm for non-smooth optimization
  Marco Rando, Cesare Molinari, Lorenzo Rosasco, Silvia Villa
• On learning the optimal regularization parameter in inverse problems
  Jonathan Chirinos Rodriguez
• Adaptive Bregman-Kaczmarz: An Approach to Solve Linear Inverse Problems with Independent Noise Exactly
  LIONEL TONDJI

TD-07: Accelerated methods in modern optimization
Stream: Optimization for Inverse Problems and Machine Learning
Room: M:I
Chair(s): Enis Chenchene

• Near-optimal Closed-loop Method via Lyapunov Damping for Convex Optimization
  Camille Castera, Severin Maier, Peter Ochs
• Inertial methods beyond minimizer uniqueness
  Hippolyte Labarrière
• Variance reduction techniques for stochastic proximal point algorithms
  Cheik Traoré

FD-07: Optimal Transport for Machine Learning and Inverse Problems
Stream: Optimization for Inverse Problems and Machine Learning
Room: M:I
Chair(s): Cheik Traoré

• Gradient flows and kernelization in the Hellinger-Kantorovich (a.k.a. Wasserstein-Fisher-Rao) space
  Jia-Jie Zhu
• Learning Total-Variation Regularization Parameters via Weak Optimal Transport
  Enis Chenchene, Kristian Bredies
• An Optimal Transport-based approach to Total-Variation regularization for the Diffusion MRI problem
  Rodolfo Assereto
• Application of the Opial property in Wasserstein spaces to inexact JKO schemes
  Emanuele Naldi