Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

Recent advancements in hardware and software systems have been driven by the
deployment of emerging smart health and mobility applications. These developments have …

We study the problem of improving the performance of online algorithms by incorporating
machine-learned predictions. The goal is to design algorithms that are both consistent and …

The secretary problem is probably the purest model of decision making under uncertainty. In
this paper we ask which advice can we give the algorithm to improve its success probability …

This paper studies online algorithms augmented with multiple machine-learned predictions.
We give a generic algorithmic framework for online covering problems with multiple …

Online algorithms with predictions is a popular and elegant framework for bypassing
pessimistic lower bounds in competitive analysis. In this model, online algorithms are …

We characterize the measurement complexity of compressed sensing of signals drawn from
a known prior distribution, even when the support of the prior is the entire space (rather than …

Estimating frequencies of elements appearing in a data stream is a key task in large-scale
data analysis. Popular sketching approaches to this problem (eg, CountMin and …

Large matrices arise in many machine learning and data analysis applications, including as
representations of datasets, graphs, model weights, and first and second-order derivatives …