2 citations · 3 across the 7 of their papers we have counts for
8 papers
Lower Bounds for Anytime Acceleration of Gradient Descent
Chung-En Tsai, Ilyas Fatkhullin, Liang Zhang +1
Recent work suggests that the convergence rate of gradient descent (GD) in smooth convex optimization can be significantly improved by employing large stepsizes that may violate th…
Global Optimality for Constrained Exploration via Penalty Regularization
Florian Wolf, Ilyas Fatkhullin, Niao He
Efficient exploration is a central problem in reinforcement learning and is often formalized as maximizing the entropy of the state-action occupancy measure. While unconstrained ma…
Global Solutions to Non-Convex Functional Constrained Problems with Hidden Convexity
Ilyas Fatkhullin, Niao He, Guanghui Lan +1
Constrained non-convex optimization is fundamentally challenging, as global solutions are generally intractable and constraint qualifications may not hold. However, in many applica…
Natural Gradient VI: Guarantees for Non-Conjugate Models
Fangyuan Sun, Ilyas Fatkhullin, Niao He
Stochastic Natural Gradient Variational Inference (NGVI) is a widely used method for approximating posterior distribution in probabilistic models. Despite its empirical success and…
From Gradient Clipping to Normalization for Heavy Tailed SGD
Florian Hübler, Ilyas Fatkhullin, Niao He
Recent empirical evidence indicates that many machine learning applications involve heavy-tailed gradient noise, which challenges the standard assumptions of bounded variance in st…
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence
Ilyas Fatkhullin, Niao He
This paper revisits the convergence of Stochastic Mirror Descent (SMD) in the contemporary nonconvex optimization setting. Existing results for batch-free nonconvex SMD restrict th…
Stochastic Optimization under Hidden Convexity
Ilyas Fatkhullin, Niao He, Yifan Hu
In this work, we consider constrained stochastic optimization problems under hidden convexity, i.e., those that admit a convex reformulation via non-linear (but invertible) map $c(…
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergence
Jiduan Wu, Anas Barakat, Ilyas Fatkhullin +1
Zero-sum Linear Quadratic (LQ) games are fundamental in optimal control and can be used (i)~as a dynamic game formulation for risk-sensitive or robust control and (ii)~as a benchma…