The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

Text-to-3D modelling has seen exciting progress by combining generative text-to-image
models with image-to-3D methods like Neural Radiance Fields. DreamFusion recently …

We obtain global, non-asymptotic convergence guarantees for independent learning
algorithms in competitive reinforcement learning settings with two agents (ie, zero-sum …

Minimax optimization has found extensive applications in modern machine learning, in
settings such as generative adversarial networks (GANs), adversarial training and multi …

Recent applications that arise in machine learning have surged significant interest in solving
min-max saddle point games. This problem has been extensively studied in the convex …

Despite its important applications in Machine Learning, min-max optimization of objective
functions that are nonconvex-nonconcave remains elusive. Not only are there no known first …

In this work, we study the computational complexity of reducing the squared gradient
magnitude for smooth minimax optimization problems. First, we present algorithms with …

Variational inequalities have recently attracted considerable interest in machine learning as
a flexible paradigm for models that go beyond ordinary loss function minimization (such as …

The use of min-max optimization in the adversarial training of deep neural network
classifiers, and the training of generative adversarial networks has motivated the study of …

Denoising Score Matching with Annealed Langevin Sampling (DSM-ALS) has recently
found success in generative modeling. The approach works by first training a neural network …