Optimizing Neural Networks via Koopman Operator Theory. - Akshunna S. Dogra; William T. Redman (NeurIPS, 2020)

Local error quantification for efficient neural network dynamical system solvers. - Akshunna S. Dogra; William T. Redman (CoRR, 2020)

On Koopman Mode Decomposition and Tensor Component Analysis. - William T. Redman (CoRR, 2021)

Universality of Deep Neural Network Lottery Tickets: A Renormalization Group Perspective. - William T. Redman; Tianlong Chen 001; Akshunna S. Dogra; Zhangyang Wang (CoRR, 2021)

An Operator Theoretic Perspective on Pruning Deep Neural Networks. - William T. Redman; Maria Fonoberova; Ryan Mohr; Ioannis G. Kevrekidis; Igor Mezic (CoRR, 2021)

Algorithmic (Semi-)Conjugacy via Koopman Operator Theory. - William T. Redman; Maria Fonoberova; Ryan Mohr; Ioannis G. Kevrekidis; Igor Mezic (CDC, 2022)

An Operator Theoretic View On Pruning Deep Neural Networks. - William T. Redman; Maria Fonoberova; Ryan Mohr; Yannis G. Kevrekidis; Igor Mezic (ICLR, 2022)

Universality of Winning Tickets: A Renormalization Group Perspective. - William T. Redman; Tianlong Chen 001; Zhangyang Wang; Akshunna S. Dogra (ICML, 2022)

On Equivalent Optimization of Machine Learning Methods. - William T. Redman; Juan M. Bello-Rivas; Maria Fonoberova; Ryan Mohr; Ioannis G. Kevrekidis; Igor Mezic (CoRR, 2023)

Koopman Learning with Episodic Memory. - William T. Redman; Dean Huang; Maria Fonoberova; Igor Mezic (CoRR, 2023)

Identifying Equivalent Training Dynamics. - William T. Redman; Juan M. Bello-Rivas; Maria Fonoberova; Ryan Mohr; Yannis G. Kevrekidis; Igor Mezic (NeurIPS, 2024)

How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks. - William T. Redman; Zhangyang Wang; Alessandro Ingrosso; Sebastian Goldt (CPAL, 2025)