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Found 4 papers, 1 papers with code
On the Byzantine-Resilience of Distillation-Based Federated Learning
In this work, we study the performance of such approaches in the byzantine setting, where a subset of the clients act in an adversarial manner aiming to disrupt the learning process.
Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties
In this work, we study optimization problems of the form $\min_x \max_y f(x, y)$, where $f(x, y)$ is defined on a product Riemannian manifold $\mathcal{M} \times \mathcal{N}$ and is $\mu_x$-strongly geodesically convex (g-convex) in $x$ and $\mu_y$-strongly g-concave in $y$, for $\mu_x, \mu_y \geq 0$.
Efficient Online-Bandit Strategies for Minimax Learning Problems
Several learning problems involve solving min-max problems, e. g., empirical distributional robust learning or learning with non-standard aggregated losses.
Linear Bandits on Uniformly Convex Sets
Linear bandit algorithms yield $\tilde{\mathcal{O}}(n\sqrt{T})$ pseudo-regret bounds on compact convex action sets $\mathcal{K}\subset\mathbb{R}^n$ and two types of structural assumptions lead to better pseudo-regret bounds.
