
-
Adaptive Sampling for Minimax Fair Classification
Machine learning models trained on imbalanced datasets can often end up ...
read it
-
Adaptive Sampling for Estimating Distributions: A Bayesian Upper Confidence Bound Approach
The problem of adaptive sampling for estimating probability mass functio...
read it
-
Causal Posterior Matching and its Applications
We consider the problem of communication over the binary symmetric chann...
read it
-
On Error Exponents of Almost-Fixed-Length Channel Codes and Hypothesis Tests
We examine a new class of channel coding strategies, and hypothesis test...
read it
-
CLEANN: Accelerated Trojan Shield for Embedded Neural Networks
We propose CLEANN, the first end-to-end framework that enables online mi...
read it
-
Low Complexity Sequential Search with Measurement Dependent Noise
This paper considers a target localization problem where at any given ti...
read it
-
Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS
We aim to optimize a black-box function f:XR under the assumption that f...
read it
-
GeneCAI: Genetic Evolution for Acquiring Compact AI
In the contemporary big data realm, Deep Neural Networks (DNNs) are evol...
read it
-
Sequential Learning of CSI for MmWave Initial Alignment
MmWave communications aim to meet the demand for higher data rates by us...
read it
-
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
read it
-
ASCAI: Adaptive Sampling for acquiring Compact AI
This paper introduces ASCAI, a novel adaptive sampling methodology that ...
read it
-
Adaptive Sampling for Estimating Multiple Probability Distributions
We consider the problem of allocating samples to a finite set of discret...
read it
-
Active Learning for Binary Classification with Abstention
We construct and analyze active learning algorithms for the problem of b...
read it
-
The Label Complexity of Active Learning from Observational Data
Counterfactual learning from observational data involves learning a clas...
read it
-
Decentralized Bayesian Learning over Graphs
We propose a decentralized learning algorithm over a general social netw...
read it
-
Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features Learning
Partially annotated clips contain rich temporal contexts that can comple...
read it
-
Binary Classification with Bounded Abstention Rate
We consider the problem of binary classification with abstention in the ...
read it
-
Multiscale Gaussian Process Level Set Estimation
In this paper, the problem of estimating the level set of a black-box fu...
read it
-
Peer-to-peer Federated Learning on Graphs
We consider the problem of training a machine learning model over a netw...
read it
-
Active Learning and CSI acquisition for mmWave Initial Alignment
Millimeter wave (mmWave) communication with large antenna arrays is a pr...
read it
-
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
Unsupervised learning for visual perception of 3D geometry is of great i...
read it
-
Efficient Video Understanding via Layered Multi Frame-Rate Analysis
One of the greatest challenges in the design of a real-time perception s...
read it
-
Authentication of cyber-physical systems under learning-based attacks
The problem of attacking and authenticating cyber-physical systems is co...
read it
-
Active Learning with Logged Data
We consider active learning with logged data, where labeled examples are...
read it
-
Dynamic Cloud Network Control under Reconfiguration Delay and Cost
Network virtualization and programmability allow operators to deploy a w...
read it
-
Improved Target Acquisition Rates with Feedback Codes
This paper considers the problem of acquiring an unknown target location...
read it
-
Gaussian Process bandits with adaptive discretization
In this paper, the problem of maximizing a black-box function f:X→R is s...
read it
-
CuRTAIL: ChaRacterizing and Thwarting AdversarIal deep Learning
This paper proposes CuRTAIL, an end-to-end computing framework for chara...
read it
-
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification
Multi-task learning aims to improve generalization performance of multip...
read it
-
Active Learning from Imperfect Labelers
We study active learning where the labeler can not only return incorrect...
read it
-
Adaptive Object Detection Using Adjacency and Zoom Prediction
State-of-the-art object detection systems rely on an accurate set of reg...
read it
-
Efficient Object Detection for High Resolution Images
Efficient generation of high-quality object proposals is an essential st...
read it