Panayotis Mertikopoulos
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New research explores preference prediction in multi-agent online learning
A new paper explores the relationship between ordinal preferences and the long-term behavior of multi-agent online learning dynamics. Researchers Panayotis Mertikopoulos and colleagues demonstrate that while the structu…
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New paper explores regret, equilibrium, and learning in games
A new paper provides a comprehensive overview of learning in games, exploring both single-agent decision processes and multi-agent interactions. It introduces a family of regularized learning policies designed to balanc…
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New model analyzes stochastic mirror descent with heavy-tailed noise
Researchers have introduced a continuous-time model for stochastic mirror descent (SMD) that accounts for heavy-tailed noise. This model, termed the Lévy mirror flow (LMF), is designed to analyze SMD's convergence guara…
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New OptMuon method enhances stochastic optimization with adaptive momentum
Researchers have introduced OptMuon, a novel adaptive momentum orthogonalization method for stochastic nonconvex optimization that calibrates update magnitudes from observed trajectories. This approach combines Muon-sty…