TMTSP-L2.4
A convexity preserving nonconvex regularization for inverse problems under non-Gaussian noise
Wataru Yata, Keita Kume, Isao Yamada, Institute of Science Tokyo, Japan
Session:
TMTSP-L2: Optimization methods Lecture
Track:
TMTSP - Theoretical and Methodological Trends in Signal Processing
Location:
Sala Massimo
Presentation Time:
Wed, 10 Sep, 10:00 - 10:20 Italy Time (UTC +2)
Session Co-Chairs:
Isao Yamada, Institute of Science Tokyo and Adrien Meynard, ENS de Lyon
Presentation
Discussion
Resources
No resources available.
Session TMTSP-L2
TMTSP-L2.1: POST-COMPOSITION PRIMAL-DUAL ALGORITHM FOR TEXTURE, GEOMETRY, AND NOISE SEPARATION
Adrien Meynard, ENS de Lyon, France; Nelly Pustelnik, CNRS, ENS de Lyon, France; Luis Briceño-Arias, Universidad Técnica Federico Santa María, Chile; Sylvain Meignen, University Grenoble Alpes, France
TMTSP-L2.2: SCALABLE MIN-MAX OPTIMIZATION VIA PRIMAL-DUAL EXACT PARETO OPTIMIZATION
Sangwoo Park, Stefan Vlaski, Imperial College London, United Kingdom; Lajos Hanzo, University of Southampton, United Kingdom
TMTSP-L2.3: Weighted Sum-Rate Maximization for Beamforming Design Using Minorization-Maximization: Convergence Rate and Deep Unfolding
Zhexian Yang, SIST, ShanghaiTech, China; Zepeng Zhang, IMOS, EPFL, China; Ziping Zhao, SIST, ShanghaiTech, China
TMTSP-L2.4: A convexity preserving nonconvex regularization for inverse problems under non-Gaussian noise
Wataru Yata, Keita Kume, Isao Yamada, Institute of Science Tokyo, Japan
TMTSP-L2.5: A DISCRETE MEASURE FOR DEBIASED FEATURE GROUPING: A LIMIT OF MOREAU-ENHANCED OSCAR REGULARIZER AND ITS PROXIMITY OPERATOR
Kyohei Suzuki, Masahiro Yukawa, Keio University, Japan