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cs.LG2026
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…
cs.LG2025
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…
cs.LG2025
Safe-EF: Error Feedback for Nonsmooth Constrained Optimization
Rustem Islamov, Yarden As, Ilyas Fatkhullin
Federated learning faces severe communication bottlenecks due to the high dimensionality of model updates. Communication compression with contractive compressors (e.g., Top-K) is o…