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09/21/2023
Quasi-Monte Carlo for 3D Sliced Wasserstein
Monte Carlo (MC) approximation has been used as the standard computation...
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06/20/2023
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Obtaining large pre-trained models that can be fine-tuned to new tasks w...
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06/13/2023
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings
In several practical applications of federated learning (FL), the client...
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06/08/2023
Posterior Collapse in Linear Conditional and Hierarchical Variational Autoencoders
The posterior collapse phenomenon in variational autoencoders (VAEs), wh...
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04/30/2023
Control Variate Sliced Wasserstein Estimators
The sliced Wasserstein (SW) distances between two probability measures a...
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04/26/2023
Energy-Based Sliced Wasserstein Distance
The sliced Wasserstein (SW) distance has been widely recognized as a sta...
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01/27/2023
On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances
Dirichlet Process mixture models (DPMM) in combination with Gaussian ker...
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01/12/2023
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Max sliced Wasserstein (Max-SW) distance has been widely known as a solu...
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01/10/2023
Markovian Sliced Wasserstein Distances: Beyond Independent Projections
Sliced Wasserstein (SW) distance suffers from redundant projections due ...
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01/01/2023
Neural Collapse in Deep Linear Network: From Balanced to Imbalanced Data
Modern deep neural networks have achieved superhuman performance in task...
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12/04/2022
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
Collecting large-scale medical datasets with fully annotated samples for...
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11/28/2022
Revisiting Over-smoothing and Over-squashing using Ollivier's Ricci Curvature
Graph Neural Networks (GNNs) had been demonstrated to be inherently susc...
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11/24/2022
Improving Multi-task Learning via Seeking Task-based Flat Regions
Multi-Task Learning (MTL) is a widely-used and powerful learning paradig...
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10/11/2022
Robustify Transformers with Robust Kernel Density Estimation
Recent advances in Transformer architecture have empowered its empirical...
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09/29/2022
Improving Generative Flow Networks with Path Regularization
Generative Flow Networks (GFlowNets) are recently proposed models for le...
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06/04/2022
Stochastic Multiple Target Sampling Gradient Descent
Sampling from an unnormalized target distribution is an essential proble...
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06/01/2022
Transformer with Fourier Integral Attentions
Multi-head attention empowers the recent success of transformers, the st...
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05/27/2022
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
We propose a novel approach to the problem of clustering hierarchically ...
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05/25/2022
Federated Self-supervised Learning for Heterogeneous Clients
Federated Learning has become an important learning paradigm due to its ...
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05/23/2022
Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures
The Expectation-Maximization (EM) algorithm has been predominantly used ...
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05/16/2022
An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models
Using gradient descent (GD) with fixed or decaying step-size is standard...
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04/04/2022
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution
The conventional sliced Wasserstein is defined between two probability m...
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03/25/2022
Amortized Projection Optimization for Sliced Wasserstein Generative Models
Seeking informative projecting directions has been an important task in ...
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03/01/2022
Global-Local Regularization Via Distributional Robustness
Despite superior performance in many situations, deep neural networks ar...
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02/17/2022
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models
We revisit convergence rates for maximum likelihood estimation (MLE) und...
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02/15/2022
Architecture Agnostic Federated Learning for Neural Networks
With growing concerns regarding data privacy and rapid increase in data ...
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02/09/2022
Improving Computational Complexity in Statistical Models with Second-Order Information
It is known that when the statistical models are singular, i.e., the Fis...
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02/05/2022
Beyond Black Box Densities: Parameter Learning for the Deviated Components
As we collect additional samples from a data population for which a know...
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01/10/2022
Bayesian Consistency with the Supremum Metric
We present simple conditions for Bayesian consistency in the supremum me...
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10/29/2021
Model Fusion of Heterogeneous Neural Networks via Cross-Layer Alignment
Layer-wise model fusion via optimal transport, named OTFusion, applies s...
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10/16/2021
Transformer with a Mixture of Gaussian Keys
Multi-head attention is a driving force behind state-of-the-art transfor...
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10/15/2021
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent
We study the statistical and computational complexities of the Polyak st...
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08/22/2021
An Efficient Mini-batch Method via Partial Transportation
Mini-batch optimal transport (m-OT) has been widely used recently to dea...
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07/22/2021
On Integral Theorems: Monte Carlo Estimators and Optimal Functions
We introduce a class of integral theorems based on cyclic functions and ...
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06/29/2021
BONuS: Multiple multivariate testing with a data-adaptivetest statistic
We propose a new adaptive empirical Bayes framework, the Bag-Of-Null-Sta...
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06/11/2021
Statistical Analysis from the Fourier Integral Theorem
Taking the Fourier integral theorem as our starting point, in this paper...
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02/16/2021
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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02/11/2021
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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01/27/2021
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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12/28/2020
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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10/05/2020
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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06/12/2020
Projection Robust Wasserstein Distance and Riemannian Optimization
Projection robust Wasserstein (PRW) distance, or Wasserstein projection ...
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06/11/2020
Probabilistic Best Subset Selection via Gradient-Based Optimization
In high-dimensional statistics, variable selection is an optimization pr...
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06/11/2020
Probabilistic Best Subset Selection by Gradient-Based Optimization
In high-dimensional statistics, variable selection is an optimization pr...
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06/04/2020
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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06/01/2020
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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05/22/2020
Instability, Computational Efficiency and Statistical Accuracy
Many statistical estimators are defined as the fixed point of a data-dep...
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02/18/2020
Distributional Sliced-Wasserstein and Applications to Generative Modeling
Sliced-Wasserstein distance (SWD) and its variation, Max Sliced-Wasserst...
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02/12/2020
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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02/12/2020
