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07/28/2023
Implicit neural representation for change detection
Detecting changes that occurred in a pair of 3D airborne LiDAR point clo...
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05/19/2023
Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence
Decentralized learning has recently been attracting increasing attention...
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02/20/2023
Nystrom Method for Accurate and Scalable Implicit Differentiation
The essential difficulty of gradient-based bilevel optimization using im...
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02/14/2023
Optimal Transport for Change Detection on LiDAR Point Clouds
The detection of changes occurring in multi-temporal remote sensing data...
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02/04/2023
Structural Explanations for Graph Neural Networks using HSIC
Graph neural networks (GNNs) are a type of neural model that tackle grap...
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09/30/2022
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data
SGD with momentum acceleration is one of the key components for improvin...
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08/21/2022
Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling
The research process includes many decisions, e.g., how to entitle and w...
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08/08/2022
Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images
The generation of three-dimensional (3D) medical images can have great a...
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07/22/2022
Scale dependant layer for self-supervised nuclei encoding
Recent developments in self-supervised learning give us the possibility ...
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06/24/2022
Approximating 1-Wasserstein Distance with Trees
Wasserstein distance, which measures the discrepancy between distributio...
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06/01/2022
Feature Selection for Discovering Distributional Treatment Effect Modifiers
Finding the features relevant to the difference in treatment effects is ...
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05/23/2022
Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization
In recent years, decentralized learning has emerged as a powerful tool n...
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05/08/2022
Communication Compression for Decentralized Learning with Operator Splitting Methods
In decentralized learning, operator splitting methods using a primal-dua...
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10/16/2021
Nys-Curve: Nyström-Approximated Curvature for Stochastic Optimization
The quasi-Newton methods generally provide curvature information by appr...
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09/30/2021
Adversarial Regression with Doubly Non-negative Weighting Matrices
Many machine learning tasks that involve predicting an output response c...
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09/08/2021
Fixed Support Tree-Sliced Wasserstein Barycenter
The Wasserstein barycenter has been widely studied in various fields, in...
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05/30/2021
Re-evaluating Word Mover's Distance
The word mover's distance (WMD) is a fundamental technique for measuring...
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03/01/2021
Computationally Efficient Wasserstein Loss for Structured Labels
The problem of estimating the probability distribution of labels has bee...
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02/08/2021
Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares
A recently introduced technique for a sparse optimization problem called...
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01/27/2021
Supervised Tree-Wasserstein Distance
To measure the similarity of documents, the Wasserstein distance is a po...
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10/29/2020
Post-selection inference with HSIC-Lasso
Detecting influential features in complex (non-linear and/or high-dimens...
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10/19/2020
Poincare: Recommending Publication Venues via Treatment Effect Estimation
Choosing a publication venue for an academic paper is a crucial step in ...
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06/13/2020
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Neural architecture search (NAS) automates the design of deep neural net...
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06/09/2020
Neural Methods for Point-wise Dependency Estimation
Since its inception, the neural estimation of mutual information (MI) ha...
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06/04/2020
Fast Unbalanced Optimal Transport on Tree
This study examines the time complexities of the unbalanced optimal tran...
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05/25/2020
Feature Robust Optimal Transport for High-dimensional Data
Optimal transport is a machine learning technique with applications incl...
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03/25/2020
Volumization as a Natural Generalization of Weight Decay
We propose a novel regularization method, called volumization, for neura...
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02/08/2020
Random Features Strengthen Graph Neural Networks
Graph neural networks (GNNs) are powerful machine learning models for va...
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02/05/2020
Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces
The problem of comparing distributions endowed with their own geometry a...
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01/23/2020
FsNet: Feature Selection Network on High-dimensional Biological Data
Biological data are generally high-dimensional and require efficient mac...
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01/17/2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
Graph structured data has wide applicability in various domains such as ...
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10/27/2019
Kernel Stein Tests for Multiple Model Comparison
We address the problem of non-parametric multiple model comparison: give...
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10/14/2019
More Powerful Selective Kernel Tests for Feature Selection
Refining one's hypotheses in the light of data is a commonplace scientif...
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10/10/2019
On Scalable Variant of Wasserstein Barycenter
We study a variant of Wasserstein barycenter problem, which we refer to ...
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10/10/2019
Computationally Efficient Tree Variants of Gromov-Wasserstein
We propose two novel variants of Gromov-Wasserstein (GW) between probabi...
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09/05/2019
LSMI-Sinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport
Estimating mutual information is an important machine learning and stati...
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08/30/2019
Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel
Transformer is a powerful architecture that achieves superior performanc...
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05/24/2019
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
In this paper, from a theoretical perspective, we study how powerful gra...
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02/26/2019
Topological Bayesian Optimization with Persistence Diagrams
Finding an optimal parameter of a black-box function is important for se...
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02/26/2019
Learning to Find Hard Instances of Graph Problems
Finding hard instances, which need a long time to solve, of graph proble...
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02/01/2019
Tree-Sliced Approximation of Wasserstein Distances
Optimal transport () theory provides a useful set of tools to compare pr...
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01/23/2019
Constant Time Graph Neural Networks
Recent advancements in graph neural networks (GNN) have led to state-of-...
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02/17/2018
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
Measuring divergence between two distributions is essential in machine l...
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02/15/2018
Selecting the Best in GANs Family: a Post Selection Inference Framework
"Which Generative Adversarial Networks (GANs) generates the most plausib...
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02/15/2018
"Dependency Bottleneck" in Auto-encoding Architectures: an Empirical Study
Recent works investigated the generalization properties in deep neural n...
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02/10/2018
Riemannian Manifold Kernel for Persistence Diagrams
Algebraic topology methods have recently played an important role for st...
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11/16/2017
Deep Matching Autoencoders
Increasingly many real world tasks involve data in multiple modalities o...
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05/15/2017
Convex Coupled Matrix and Tensor Completion
We propose a set of convex low rank inducing norms for a coupled matrice...
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02/21/2017
Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation
We propose an inlier-based outlier detection method capable of both iden...
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10/12/2016