Statistical Learning
A Probabilistic Graph Coupling View of Dimension Reduction
They are well understood and unified in the kernel PCA framework [16].
Meta-Adaptive Nonlinear Control: Theory and Algorithms
The goal is to control a nonlinear system subject to adversarial disturbance and unknown environment-dependent nonlinear dynamics, under the assumption that the environment-dependent dynamics can be well captured with some shared representation. Our approach is motivated by robot control, where a robotic system encounters a sequence of new environmental conditions that it must quickly adapt to.
Generalization Bounds for Gradient Methods via Discrete and Continuous Prior
Proving algorithm-dependent generalization error bounds for gradient-type optimization methods has attracted significant attention recently in learning theory. However, most existing trajectory-based analyses require either restrictive assumptions on the learning rate (e.g., fast decreasing learning rate), or continuous injected
Near-Optimal Private and Scalable k-Clustering
This high demand has stimulated an important research effort to design algorithmic techniques enabling privacy-preserving algorithms.
How Sampling Impacts the Robustness of Stochastic Neural Networks
Stochastic neural networks (SNNs) are random functions whose predictions are gained by averaging over multiple realizations.
