We introduce SABRE, a novel framework for robust variational Bayesian
pe...
Understanding complex dynamics of two-sided online matching markets, whe...
We study the kernelized bandit problem, that involves designing an adapt...
Confidence intervals are a crucial building block in the analysis of var...
Deep neural networks have been shown to be vulnerable to backdoor, or tr...
Federated learning performed by a decentralized networks of agents is
be...
We consider the problem of active and sequential beam tracking at mmWave...
Machine learning models trained on imbalanced datasets can often end up
...
The problem of adaptive sampling for estimating probability mass functio...
We consider the problem of communication over the binary symmetric chann...
We examine a new class of channel coding strategies, and hypothesis test...
We propose CLEANN, the first end-to-end framework that enables online
mi...
This paper considers a target localization problem where at any given ti...
We aim to optimize a black-box function f:XR
under the assumption that f...
In the contemporary big data realm, Deep Neural Networks (DNNs) are evol...
MmWave communications aim to meet the demand for higher data rates by us...
Federated learning (FL) is a machine learning setting where many clients...
This paper introduces ASCAI, a novel adaptive sampling methodology that ...
We consider the problem of allocating samples to a finite set of discret...
We construct and analyze active learning algorithms for the problem of b...
Counterfactual learning from observational data involves learning a
clas...
We propose a decentralized learning algorithm over a general social netw...
Partially annotated clips contain rich temporal contexts that can comple...
We consider the problem of binary classification with abstention in the
...
In this paper, the problem of estimating the level set of a black-box
fu...
We consider the problem of training a machine learning model over a netw...
Millimeter wave (mmWave) communication with large antenna arrays is a
pr...
Unsupervised learning for visual perception of 3D geometry is of great
i...
One of the greatest challenges in the design of a real-time perception s...
The problem of attacking and authenticating cyber-physical systems is
co...
We consider active learning with logged data, where labeled examples are...
Network virtualization and programmability allow operators to deploy a w...
This paper considers the problem of acquiring an unknown target location...
In this paper, the problem of maximizing a black-box function f:X→R is s...
This paper proposes CuRTAIL, an end-to-end computing framework for
chara...
Multi-task learning aims to improve generalization performance of multip...
We study active learning where the labeler can not only return incorrect...
State-of-the-art object detection systems rely on an accurate set of reg...
Efficient generation of high-quality object proposals is an essential st...