11 citations · 35 across the 20 of their papers we have counts for
4 papers · 1 filter
Non-Asymptotic Optimization and Generalization Bounds for Stochastic Gauss-Newton in Overparameterized Models
Semih Cayci
An important question in deep learning is how higher-order optimization methods affect generalization. In this work, we analyze a stochastic Gauss-Newton (SGN) method with Levenber…
Extraction of Moment Closures for Strongly Non-Equilibrium Flows via Machine Learning
Hang Song, Satyvir Singh, Manuel Torrilhon +1
We introduce a machine learning framework for moment-equation modeling of rarefied gas flows, addressing strongly non-equilibrium conditions inaccessible to conventional computatio…
Convergence of Stochastic Gradient Langevin Dynamics in the Lazy Training Regime
Noah Oberweis, Semih Cayci
Continuous-time models provide important insights into the training dynamics of optimization algorithms in deep learning. In this work, we establish a non-asymptotic convergence an…
A Variational Inequality Approach to Independent Learning in Static Mean-Field Games
Batuhan Yardim, Semih Cayci, Niao He
Competitive games involving thousands or even millions of players are prevalent in real-world contexts, such as transportation, communications, and computer networks. However, lear…