1 citations · 2 across the 10 of their papers we have counts for
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A Diffusion Approximation for Temporal-Difference Learning with Linear Features under Markovian Noise
M. Forzo, E. Monzio Compagnoni, A. Russo +1
Temporal difference (TD) learning with linear function approximation is a core method for policy evaluation. Its classical continuous-time description is an ordinary differential e…
Beyond a Single Explanation of the Adam--SGD Gap
Chenxiang Zhang, Rustem Islamov, Enea Monzio Compagnoni +3
Prior work has identified several factors that can contribute to the performance gap between Adam and SGD, spanning data aspects, architecture design, and optimization properties.…
Adaptive Methods Are Preferable in High Privacy Settings: An SDE Perspective
Enea Monzio Compagnoni, Alessandro Stanghellini, Rustem Islamov +2
Differential Privacy (DP) is becoming central to large-scale training as privacy regulations tighten. We revisit how DP noise interacts with adaptivity in optimization through the…