9 citations · 13 across the 4 of their papers we have counts for
4 papers
Optimization with Dynamic Constraint Learning (DCL)
Ezgi Oztekin, Figen Oztoprak, S. Ilker Birbil
We propose Dynamic Constraint Learning (DCL), a data-driven framework for constrained optimization when constraint functions are unknown and cannot be queried during optimization.…
Bolstering Stochastic Gradient Descent with Model Building
S. Ilker Birbil, Ozgur Martin, Gonenc Onay +1
Stochastic gradient descent method and its variants constitute the core optimization algorithms that achieve good convergence rates for solving machine learning problems. These rat…
Parallel Stochastic Gradient Markov Chain Monte Carlo for Matrix Factorisation Models
Umut Şimşekli, Hazal Koptagel, Hakan Güldaş +3
For large matrix factorisation problems, we develop a distributed Markov Chain Monte Carlo (MCMC) method based on stochastic gradient Langevin dynamics (SGLD) that we call Parallel…
A Second-Order Method for Convex -Regularized Optimization with Active Set Prediction
Nitish Shirish Keskar, Jorge Nocedal, Figen Oztoprak +1
We describe an active-set method for the minimization of an objective function that is the sum of a smooth convex function and an -regularization term. A distinctive fe…