36 citations · 50 across the 5 of their papers we have counts for
6 papers
Enabling SQL-based Training Data Debugging for Federated Learning
Yejia Liu, Weiyuan Wu, Lampros Flokas +2
How can we debug a logistical regression model in a federated learning setting when seeing the model behave unexpectedly (e.g., the model rejects all high-income customers' loan ap…
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent
Lampros Flokas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Georgios Piliouras
Many recent AI architectures are inspired by zero-sum games, however, the behavior of their dynamics is still not well understood. Inspired by this, we study standard gradient desc…
No-regret learning and mixed Nash equilibria: They do not mix
Lampros Flokas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Thanasis Lianeas +2
Understanding the behavior of no-regret dynamics in general -player games is a fundamental question in online learning and game theory. A folk result in the field states that, i…
Complaint-driven Training Data Debugging for Query 2.0
Weiyuan Wu, Lampros Flokas, Eugene Wu +1
As the need for machine learning (ML) increases rapidly across all industry sectors, there is a significant interest among commercial database providers to support "Query 2.0", whi…
Efficiently avoiding saddle points with zero order methods: No gradients required
Lampros Flokas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Georgios Piliouras
We consider the case of derivative-free algorithms for non-convex optimization, also known as zero order algorithms, that use only function evaluations rather than gradients. For a…
Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
Lampros Flokas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Georgios Piliouras
We study a wide class of non-convex non-concave min-max games that generalizes over standard bilinear zero-sum games. In this class, players control the inputs of a smooth function…