Statistical Learning
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy Anastasia Koloskova
We study gradient descent under linearly correlated noise.
Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games
Technically, our online quantum algorithm "quantizes" classical algorithms based
Ordering-based Conditions for Global Convergence of Policy Gradient Methods
The conditions on the representation that imply global convergence are different between these two algorithms.
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations.
On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis
Probabilistic principal component analysis (PPCA) is currently one of the most used statistical tools to reduce the ambient dimension of the data.
