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08/29/2023
Sampling for Remote Estimation of an Ornstein-Uhlenbeck Process through Channel with Unknown Delay Statistics
In this paper, we consider sampling an Ornstein-Uhlenbeck (OU) process t...
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08/16/2023
Two Phases of Scaling Laws for Nearest Neighbor Classifiers
A scaling law refers to the observation that the test performance of a m...
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05/31/2023
Federated Learning in the Presence of Adversarial Client Unavailability
Federated learning is a decentralized machine learning framework wherein...
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06/15/2022
Global Convergence of Federated Learning for Mixed Regression
This paper studies the problem of model training under Federated Learnin...
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05/26/2022
Deep Active Learning with Noise Stability
Uncertainty estimation for unlabeled data is crucial to active learning....
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02/27/2022
Age Optimal Sampling Under Unknown Delay Statistics
This paper revisits the problem of sampling and transmitting status upda...
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12/10/2021
Boosting Active Learning via Improving Test Performance
Central to active learning (AL) is what data should be selected for anno...
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06/29/2021
Achieving Statistical Optimality of Federated Learning: Beyond Stationary Points
Federated Learning (FL) is a promising framework that has great potentia...
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04/15/2021
Polynomial methods in statistical inference: theory and practice
This survey provides an exposition of a suite of techniques based on the...
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02/14/2020
Optimal estimation of high-dimensional Gaussian mixtures
This paper studies the optimal rate of estimation in a finite Gaussian l...
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05/26/2019
On Learning Over-parameterized Neural Networks: A Functional Approximation Prospective
We consider training over-parameterized two-layer neural networks with R...
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07/19/2018
