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07/25/2023
Solution Path of Time-varying Markov Random Fields with Discrete Regularization
We study the problem of inferring sparse time-varying Markov random fiel...
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05/28/2023
Heterogeneous Matrix Factorization: When Features Differ by Datasets
In myriad statistical applications, data are collected from related but ...
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05/24/2023
Personalized Dictionary Learning for Heterogeneous Datasets
We introduce a relevant yet challenging problem named Personalized Dicti...
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02/21/2023
On the Optimization Landscape of Burer-Monteiro Factorization: When do Global Solutions Correspond to Ground Truth?
In low-rank matrix recovery, the goal is to recover a low-rank matrix, g...
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10/23/2022
Simple Alternating Minimization Provably Solves Complete Dictionary Learning
This paper focuses on complete dictionary learning problem, where the go...
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10/01/2022
Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition
This work analyzes the solution trajectory of gradient-based algorithms ...
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07/15/2022
Blessing of Nonconvexity in Deep Linear Models: Depth Flattens the Optimization Landscape Around the True Solution
This work characterizes the effect of depth on the optimization landscap...
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06/21/2022
Efficient Inference of Spatially-varying Gaussian Markov Random Fields with Applications in Gene Regulatory Networks
In this paper, we study the problem of inferring spatially-varying Gauss...
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06/07/2022
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer–Monteiro Factorization with Global Optimality Certification
We consider using gradient descent to minimize the nonconvex function f(...
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02/17/2022
Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization
In this work, we study the performance of sub-gradient method (SubGM) on...
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02/06/2021
Scalable Inference of Sparsely-changing Markov Random Fields with Strong Statistical Guarantees
In this paper, we study the problem of inferring time-varying Markov ran...
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02/05/2021
Implicit Regularization of Sub-Gradient Method in Robust Matrix Recovery: Don't be Afraid of Outliers
It is well-known that simple short-sighted algorithms, such as gradient ...
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10/08/2020
Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples
In this work, we study the problem of learning partially observed linear...
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09/21/2019
Efficient Learning of Distributed Linear-Quadratic Controllers
In this work, we propose a robust approach to design distributed control...
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04/20/2019
Learning Sparse Dynamical Systems from a Single Sample Trajectory
This paper addresses the problem of identifying sparse linear time-invar...
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12/30/2018
Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Robust Principal Component Analysis
This work is concerned with the non-negative robust principal component ...
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03/21/2018
Data-Driven Sparse System Identification
In this paper, we study the system identification porblem for sparse lin...
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02/14/2018
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
The sparse inverse covariance estimation problem is commonly solved usin...
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02/14/2018
Linear-Time Algorithm for Learning Large-Scale Sparse Graphical Models
The sparse inverse covariance estimation problem is commonly solved usin...
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11/24/2017
Sparse Inverse Covariance Estimation for Chordal Structures
In this paper, we consider the Graphical Lasso (GL), a popular optimizat...
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08/30/2017