Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …

We propose a new distributed algorithm based on alternating direction method of multipliers
(ADMM) to minimize sum of locally known convex functions using communication over a …

Statistical inference and information processing of high-dimensional data often require an
efficient and accurate estimation of their second-order statistics. With rapidly changing data …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

We provide a new robust convergence analysis of the well-known power method for
computing the dominant singular vectors of a matrix that we call noisy power method. Our …

We describe and analyze a simple algorithm for principal component analysis and singular
value decomposition, VR-PCA, which uses computationally cheap stochastic iterations, yet …

In this paper we provide improved guarantees for streaming principal component analysis
(PCA). Given A_1,\ldots, A_n∈\mathbbR^ d\times d sampled independently from …

We present a federated, asynchronous, and $(\varepsilon,\delta) $-differentially private
algorithm for $\PCA $ in the memory-limited setting.% Our algorithm incrementally computes …