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stat.ML2026
On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
Sharan Sahu, Cameron J. Hogan, Martin T. Wells
In this paper, we provide a comprehensive theoretical analysis of Stochastic Gradient Descent (SGD) and its momentum variants (Polyak Heavy-Ball and Nesterov) for tracking time-var…
stat.ML2026
Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization
Sharan Sahu, Abir Sarkar, Cameron J. Hogan +1
We provide a theoretical analysis of Adam under non-stationary stochastic objectives, separating two regimes: Euclidean tracking under adaptive strong monotonicity of the Adam-prec…
stat.ML2024
Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning
Luca Candelori, Alexander G. Abanov, Jeffrey Berger +8
We propose a new data representation method based on Quantum Cognition Machine Learning and apply it to manifold learning, specifically to the estimation of intrinsic dimension of…