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07/11/2023
Random-Set Convolutional Neural Network (RS-CNN) for Epistemic Deep Learning
Machine learning is increasingly deployed in safety-critical domains whe...
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02/20/2023
Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift
We develop and analyze a principled approach to kernel ridge regression ...
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02/17/2023
Cascaded information enhancement and cross-modal attention feature fusion for multispectral pedestrian detection
Multispectral pedestrian detection is a technology designed to detect an...
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01/04/2023
Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow
Gaussian mixture models form a flexible and expressive parametric family...
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12/15/2022
Variable Clustering via Distributionally Robust Nodewise Regression
We study a multi-factor block model for variable clustering and connect ...
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12/01/2022
An introduction to optimization under uncertainty – A short survey
Optimization equips engineers and scientists in a variety of fields with...
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10/22/2022
Adaptive Data Fusion for Multi-task Non-smooth Optimization
We study the problem of multi-task non-smooth optimization that arises u...
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02/10/2022
Adaptive and Robust Multi-task Learning
We study the multi-task learning problem that aims to simultaneously ana...
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10/04/2021
Clustering a Mixture of Gaussians with Unknown Covariance
We investigate a clustering problem with data from a mixture of Gaussian...
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06/24/2020
An â„“_p theory of PCA and spectral clustering
Principal Component Analysis (PCA) is a powerful tool in statistics and ...
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03/22/2020
Efficient Clustering for Stretched Mixtures: Landscape and Optimality
This paper considers a canonical clustering problem where one receives u...
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12/31/2019
Some compact notations for concentration inequalities and user-friendly results
This paper presents compact notations for concentration inequalities and...
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06/12/2019
Communication-Efficient Accurate Statistical Estimation
When the data are stored in a distributed manner, direct application of ...
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08/12/2018
Robust high dimensional factor models with applications to statistical machine learning
Factor models are a class of powerful statistical models that have been ...
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11/28/2017
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion and Blind Deconvolution
Recent years have seen a flurry of activities in designing provably effi...
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07/31/2017
