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