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Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout
Approximate inference in deep Bayesian networks exhibits a dilemma of ho...
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BoMb-OT: On Batch of Mini-batches Optimal Transport
Mini-batch optimal transport (m-OT) has been successfully used in practi...
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On the computational and statistical complexity of over-parameterized matrix sensing
We consider solving the low rank matrix sensing problem with Factorized ...
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Multivariate Smoothing via the Fourier Integral Theorem and Fourier Kernel
Starting with the Fourier integral theorem, we present natural Monte Car...
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Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
Relational regularized autoencoder (RAE) is a framework to learn the dis...
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Projection Robust Wasserstein Distance and Riemannian Optimization
Projection robust Wasserstein (PRW) distance, or Wasserstein projection ...
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Probabilistic Best Subset Selection via Gradient-Based Optimization
In high-dimensional statistics, variable selection is an optimization pr...
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Probabilistic Best Subset Selection by Gradient-Based Optimization
In high-dimensional statistics, variable selection is an optimization pr...
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On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression
We study the convergence rates of the EM algorithm for learning two-comp...
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Uniform Convergence Rates for Maximum Likelihood Estimation under Two-Component Gaussian Mixture Models
We derive uniform convergence rates for the maximum likelihood estimator...
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Instability, Computational Efficiency and Statistical Accuracy
Many statistical estimators are defined as the fixed point of a data-dep...
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Distributional Sliced-Wasserstein and Applications to Generative Modeling
Sliced-Wasserstein distance (SWD) and its variation, Max Sliced-Wasserst...
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Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms
We study the fixed-support Wasserstein barycenter problem (FS-WBP), whic...
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Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm
We study the fixed-support Wasserstein barycenter problem (FS-WBP), whic...
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Computational Hardness and Fast Algorithm for Fixed-Support Wasserstein Barycenter
We study in this paper the fixed-support Wasserstein barycenter problem ...
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On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
We provide a computational complexity analysis for the Sinkhorn algorith...
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Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing
We study the problem of sampling from the power posterior distribution i...
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On Scalable Variant of Wasserstein Barycenter
We study a variant of Wasserstein barycenter problem, which we refer to ...
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Computationally Efficient Tree Variants of Gromov-Wasserstein
We propose two novel variants of Gromov-Wasserstein (GW) between probabi...
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On the Complexity of Approximating Multimarginal Optimal Transport
We study the complexity of approximating the multimarginal optimal trans...
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On Efficient Multilevel Clustering via Wasserstein Distances
We propose a novel approach to the problem of multilevel clustering, whi...
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A Diffusion Process Perspective on Posterior Contraction Rates for Parameters
We show that diffusion processes can be exploited to study the posterior...
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Convergence Rates for Gaussian Mixtures of Experts
We provide a theoretical treatment of over-specified Gaussian mixtures o...
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On the Acceleration of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport
We propose and analyze a novel approach to accelerate the Sinkhorn and G...
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Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models
Dirichlet process mixture models (DPMM) play a central role in Bayesian ...
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Accelerated Primal-Dual Coordinate Descent for Computational Optimal Transport
We propose and analyze a novel accelerated primal-dual coordinate descen...
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Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and ℓ_1-Convex Clustering
We propose a class of first-order gradient-type optimization algorithms ...
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On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms
In recent years, the filtering-clustering problems have been a central t...
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Challenges with EM in application to weakly identifiable mixture models
We study a class of weakly identifiable location-scale mixture models fo...
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On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
We provide theoretical analyses for two algorithms that solve the regula...
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On posterior contraction of parameters and interpretability in Bayesian mixture modeling
We study posterior contraction behaviors for parameters of interest in t...
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Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning
Unsupervised and semi-supervised learning are important problems that ar...
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Probabilistic Multilevel Clustering via Composite Transportation Distance
We propose a novel probabilistic approach to multilevel clustering probl...
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Singularity, Misspecification, and the Convergence Rate of EM
A line of recent work has characterized the behavior of the EM algorithm...
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Multilevel Clustering via Wasserstein Means
We propose a novel approach to the problem of multilevel clustering, whi...
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Singularity structures and impacts on parameter estimation in finite mixtures of distributions
Singularities of a statistical model are the elements of the model's par...
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Identifiability and optimal rates of convergence for parameters of multiple types in finite mixtures
This paper studies identifiability and convergence behaviors for paramet...
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