36 citations · 113 across the 11 of their papers we have counts for
11 papers
Assessing SATNet's Ability to Solve the Symbol Grounding Problem
Oscar Chang, Lampros Flokas, Hod Lipson +1
SATNet is an award-winning MAXSAT solver that can be used to infer logical rules and integrated as a differentiable layer in a deep neural network. It had been shown to solve Sudok…
Principled Weight Initialization for Hypernetworks
Oscar Chang, Lampros Flokas, Hod Lipson
Hypernetworks are meta neural networks that generate weights for a main neural network in an end-to-end differentiable manner. Despite extensive applications ranging from multi-tas…
Chaos persists in large-scale multi-agent learning despite adaptive learning rates
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Konstantinos Metaxas +1
Multi-agent learning is intrinsically harder, more unstable and unpredictable than single agent optimization. For this reason, numerous specialized heuristics and techniques have b…
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…
Online Wideband Spectrum Sensing Using Sparsity
Lampros Flokas, Petros Maragos
Wideband spectrum sensing is an essential part of cognitive radio systems. Exact spectrum estimation is usually inefficient as it requires sampling rates at or above the Nyquist ra…
Hard Threshold Least Mean Squares Algorithm
Lampros Flokas, Petros Maragos
This work presents a new variation of the commonly used Least Mean Squares Algorithm (LMS) for the identification of sparse signals with an a-priori known sparsity using a hard thr…