19 citations · 22 across the 9 of their papers we have counts for
9 papers
Stability and Convergence of Optimistic Exponential Weights with Asymmetric Step Sizes in Bimatrix Games
Hédi Hadiji, Sarah Sachs
We study bimatrix two-player games and investigate the last-iterate convergence and stability of equilibria for the iterates generated by the optimistic exponential weights method.…
An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems
Sarah Sachs, Hedi Hadiji, Tim van Erven +1
We consider a repeatedly played generalized Nash equilibrium game. This induces a multi-agent online learning problem with joint constraints. An important challenge in this setting…
Tracking solutions of time-varying variational inequalities
Hédi Hadiji, Sarah Sachs, Cristóbal Guzmán
Tracking the solution of time-varying variational inequalities is an important problem with applications in game theory, optimization, and machine learning. Existing work considers…
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
Sarah Sachs, Tim van Erven, Liam Hodgkinson +2
Algorithm- and data-dependent generalization bounds are required to explain the generalization behavior of modern machine learning algorithms. In this context, there exists informa…
Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games
Hédi Hadiji, Sarah Sachs, Tim van Erven +1
In the first-order query model for zero-sum matrix games, players observe the expected pay-offs for all their possible actions under the randomized action played by the…
Accelerated Rates between Stochastic and Adversarial Online Convex Optimization
Sarah Sachs, Hedi Hadiji, Tim van Erven +1
Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamen…
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
Sarah Sachs, Hédi Hadiji, Tim van Erven +1
Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamen…
Robust Online Convex Optimization in the Presence of Outliers
Tim van Erven, Sarah Sachs, Wouter M. Koolen +1
We consider online convex optimization when a number k of data points are outliers that may be corrupted. We model this by introducing the notion of robust regret, which measures t…
A Century of Portraits: A Visual Historical Record of American High School Yearbooks
Shiry Ginosar, Kate Rakelly, Sarah Sachs +4
Imagery offers a rich description of our world and communicates a volume and type of information that cannot be captured by text alone. Since the invention of the camera, an ever-i…