32 citations · 32 across the 2 of their papers we have counts for
4 papers
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
Jack Parker-Holder, Luke Metz, Cinjon Resnick +6
Over the last decade, a single algorithm has changed many facets of our lives - Stochastic Gradient Descent (SGD). In the era of ever decreasing loss functions, SGD and its various…
On the Impossibility of Global Convergence in Multi-Loss Optimization
Alistair Letcher
Under mild regularity conditions, gradient-based methods converge globally to a critical point in the single-loss setting. This is known to break down for vanilla gradient descent…
Differentiable Game Mechanics
Alistair Letcher, David Balduzzi, Sebastien Racaniere +4
Deep learning is built on the foundational guarantee that gradient descent on an objective function converges to local minima. Unfortunately, this guarantee fails in settings, such…
Stable Opponent Shaping in Differentiable Games
Alistair Letcher, Jakob Foerster, David Balduzzi +2
A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel -- from GANs and intrinsic curiosity t…