Variants of RMSProp and Adagrad with Logarithmic Regret Bounds. - Mahesh Chandra Mukkamala; Matthias Hein 001 (ICML, 2017)

Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions. - Quynh Nguyen 001; Mahesh Chandra Mukkamala; Matthias Hein 001 (ICML, 2018)

On the loss landscape of a class of deep neural networks with no bad local valleys. - Quynh Nguyen 001; Mahesh Chandra Mukkamala; Matthias Hein 001 (ICLR (Poster), 2019)

Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. - Mahesh Chandra Mukkamala; Peter Ochs (NeurIPS, 2019)

Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization. - Mahesh Chandra Mukkamala; Peter Ochs; Thomas Pock; Shoham Sabach (CoRR, 2019)

Bregman Proximal Framework for Deep Linear Neural Networks. - Mahesh Chandra Mukkamala; Felix Westerkamp; Emanuel Laude; Daniel Cremers; Peter Ochs (CoRR, 2019)

Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Nonconvex Optimization. - Mahesh Chandra Mukkamala; Peter Ochs; Thomas Pock; Shoham Sabach (SIAM J. Math. Data Sci., 2020)

Bregman Proximal Gradient Algorithms for Deep Matrix Factorization. - Mahesh Chandra Mukkamala; Felix Westerkamp; Emanuel Laude; Daniel Cremers; Peter Ochs (SSVM, 2021)

Global convergence of model function based Bregman proximal minimization algorithms. - Mahesh Chandra Mukkamala; Jalal Fadili; Peter Ochs (J. Glob. Optim., 2022)