Convergence of a local regularization approach for mathematical programmes with complementarity or vanishing constraints. - Tim Hoheisel; Christian Kanzow; Alexandra Schwartz (Optim. Methods Softw., 2012)

A smoothing-regularization approach to mathematical programs with vanishing constraints. - Wolfgang Achtziger; Tim Hoheisel; Christian Kanzow (Comput. Optim. Appl., 2013)

Theoretical and numerical comparison of relaxation methods for mathematical programs with complementarity constraints. - Tim Hoheisel; Christian Kanzow; Alexandra Schwartz (Math. Program., 2013)

Epi-convergent Smoothing with Applications to Convex Composite Functions. - James V. Burke; Tim Hoheisel (SIAM J. Optim., 2013)

On a Smooth Dual Gap Function for a Class of Quasi-Variational Inequalities. - Nadja Harms; Tim Hoheisel; Christian Kanzow (J. Optim. Theory Appl., 2014)

On a Smooth Dual Gap Function for a Class of Player Convex Generalized Nash Equilibrium Problems. - Nadja Harms; Tim Hoheisel; Christian Kanzow (J. Optim. Theory Appl., 2015)

Matrix Support Functionals for Inverse Problems, Regularization, and Learning. - James V. Burke; Tim Hoheisel (SIAM J. Optim., 2015)

Convex Geometry of the Generalized Matrix-Fractional Function. - James V. Burke; Yuan Gao; Tim Hoheisel (SIAM J. Optim., 2018)

Variational Properties of Matrix Functions via the Generalized Matrix-Fractional Function. - James V. Burke; Yuan Gao; Tim Hoheisel (SIAM J. Optim., 2019)

A principled approach for generating adversarial images under non-smooth dissimilarity metrics. - Aram-Alexandre Pooladian; Chris Finlay; Tim Hoheisel; Adam M. Oberman (AISTATS, 2020)

Maximum Entropy on the Mean: A Paradigm Shift for Regularization in Image Deblurring. - Gabriel Rioux; Rustum Choksi; Tim Hoheisel; Christopher Scarvelis (CoRR, 2020)

A Study of Convex Convex-Composite Functions via Infimal Convolution with Applications. - James V. Burke; Tim Hoheisel; Quang V. Nguyen (Math. Oper. Res., 2021)

Blind Deblurring of Barcodes via Kullback-Leibler Divergence. - Gabriel Rioux; Christopher Scarvelis; Rustum Choksi; Tim Hoheisel; Pierre Maréchal (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Maximum Entropy on the Mean and the Cramér Rate Function in Statistical Estimation and Inverse Problems: Properties, Models, and Algorithms. - Yakov Vaisbourd; Rustum Choksi; Ariel Goodwin; Tim Hoheisel; Carola-Bibiane Schönlieb (CoRR, 2022)

A study of one-parameter regularization methods for mathematical programs with vanishing constraints. - Tim Hoheisel; Blanca Pablos; Aram-Alexandre Pooladian; Alexandra Schwartz; Luke Steverango (Optim. Methods Softw., 2022)

Difference of submodular minimization via DC programming. - Marwa El Halabi; George Orfanides; Tim Hoheisel (ICML, 2023)

Uniqueness in Nuclear Norm Minimization: Flatness of the Nuclear Norm Sphere and Simultaneous Polarization. - Tim Hoheisel; Elliot Paquette (J. Optim. Theory Appl., 2023)

From Perspective Maps to Epigraphical Projections. - Michael P. Friedlander; Ariel Goodwin; Tim Hoheisel (Math. Oper. Res., 2023)

LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing. - Aaron Berk; Simone Brugiapaglia; Tim Hoheisel (SIAM J. Math. Data Sci., 2023)

Data-Driven Priors in the Maximum Entropy on the Mean Method for Linear Inverse Problems. - Matthew King-Roskamp; Rustum Choksi; Tim Hoheisel (CoRR, 2024)

Square Root LASSO: Well-Posedness, Lipschitz Stability, and the Tuning Trade-Off. - Aaron Berk; Simone Brugiapaglia; Tim Hoheisel (SIAM J. Optim., 2024)

Discrete and Continuous Difference of Submodular Minimization. - George Orfanides; Tim Hoheisel; Marwa El Halabi (CoRR, 2025)