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08/30/2023
Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs
Multitask learning (MTL) leverages task-relatedness to enhance performan...
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07/19/2023
A Dual Formulation for Probabilistic Principal Component Analysis
In this paper, we characterize Probabilistic Principal Component Analysi...
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07/05/2023
Unbalanced Optimal Transport: A Unified Framework for Object Detection
During training, supervised object detection tries to correctly match th...
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06/12/2023
Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nyström method
Asymmetric data naturally exist in real life, such as directed graphs. D...
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06/12/2023
Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers
In contrast to deep networks, kernel methods cannot directly take advant...
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06/09/2023
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
The goal of this paper is to revisit Kernel Principal Component Analysis...
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05/31/2023
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation
Recently, a new line of works has emerged to understand and improve self...
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05/26/2023
Duality in Multi-View Restricted Kernel Machines
We propose a unifying setting that combines existing restricted kernel m...
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03/04/2023
Tensorized LSSVMs for Multitask Regression
Multitask learning (MTL) can utilize the relatedness between multiple ta...
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02/22/2023
Deep Kernel Principal Component Analysis for Multi-level Feature Learning
Principal Component Analysis (PCA) and its nonlinear extension Kernel PC...
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01/31/2023
Semi-Supervised Classification with Graph Convolutional Kernel Machines
We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-sup...
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01/24/2023
Multi-view Kernel PCA for Time series Forecasting
In this paper, we propose a kernel principal component analysis model fo...
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07/25/2022
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer
The success of Vision Transformer (ViT) in various computer vision tasks...
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07/23/2022
Tensor-based Multi-view Spectral Clustering via Shared Latent Space
Multi-view Spectral Clustering (MvSC) attracts increasing attention due ...
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06/27/2022
Compressing Features for Learning with Noisy Labels
Supervised learning can be viewed as distilling relevant information fro...
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06/18/2022
Piecewise Linear Neural Networks and Deep Learning
As a powerful modelling method, PieceWise Linear Neural Networks (PWLNNs...
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02/03/2022
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation
Asymmetric kernels naturally exist in real life, e.g., for conditional p...
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10/26/2021
Tensor Network Kalman Filtering for Large-Scale LS-SVMs
Least squares support vector machines are a commonly used supervised lea...
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10/13/2021
On the Double Descent of Random Features Models Trained with SGD
We study generalization properties of random features (RF) regression in...
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05/28/2021
Latent Space Exploration Using Generative Kernel PCA
Kernel PCA is a powerful feature extractor which recently has seen a ref...
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05/28/2021
Towards Deterministic Diverse Subset Sampling
Determinantal point processes (DPPs) are well known models for diverse s...
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04/28/2021
Boosting Co-teaching with Compression Regularization for Label Noise
In this paper, we study the problem of learning image classification mod...
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04/06/2021
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
Commonly, machine learning models minimize an empirical expectation. As ...
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02/16/2021
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine
Detecting out-of-distribution (OOD) samples is an essential requirement ...
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11/25/2020
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
We introduce Constr-DRKM, a deep kernel method for the unsupervised lear...
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11/13/2020
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems
Semi-parametric regression models are used in several applications which...
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11/03/2020
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines
In this paper, we develop a quadrature framework for large-scale kernel ...
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10/06/2020
Kernel regression in high dimension: Refined analysis beyond double descent
In this paper, we provide a precise characterize of generalization prope...
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06/25/2020
A Theoretical Framework for Target Propagation
The success of deep learning, a brain-inspired form of AI, has sparked i...
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06/24/2020
Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes
By using the framework of Determinantal Point Processes (DPPs), some the...
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06/16/2020
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks
Generative Adversarial Networks (GANs) are performant generative methods...
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06/12/2020
Disentangled Representation Learning and Generation with Manifold Optimization
Disentanglement is an enjoyable property in representation learning whic...
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06/01/2020
Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces
In this paper, we study the asymptotical properties of least squares reg...
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05/30/2020
Generalizing Random Fourier Features via Generalized Measures
We generalize random Fourier features, that usually require kernel funct...
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04/23/2020
Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond
Random features is one of the most sought-after research topics in stati...
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02/20/2020
Diversity sampling is an implicit regularization for kernel methods
Kernel methods have achieved very good performance on large scale regres...
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02/05/2020
Wasserstein Exponential Kernels
In the context of kernel methods, the similarity between data points is ...
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02/04/2020
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
In the past decade, interest in generative models has grown tremendously...
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11/20/2019
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
In this paper, we propose a fast surrogate leverage weighted sampling st...
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06/19/2019
Generative Restricted Kernel Machines
We propose a novel method for estimating generative models based on the ...
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05/29/2019
Nyström landmark sampling and regularized Christoffel functions
Selecting diverse and important items from a large set is a problem of i...
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05/15/2019
Efficient hinging hyperplanes neural network and its application in nonlinear system identification
In this paper, the efficient hinging hyperplanes (EHH) neural network is...
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05/09/2019
Two-stage Best-scored Random Forest for Large-scale Regression
We propose a novel method designed for large-scale regression problems, ...
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11/15/2018
Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting
Long Short-Term Memory (LSTM) is a well-known method used widely on sequ...
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09/26/2018
Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions
Hyper-kernels endowed by hyper-Reproducing Kernel Hilbert Space (hyper-R...
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11/20/2017
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions
In machine learning or statistics, it is often desirable to reduce the d...
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07/18/2017
Solving â„“^p-norm regularization with tensor kernels
In this paper, we discuss how a suitable family of tensor kernels can be...
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06/19/2017
Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers
This work proposes a new algorithm for training a re-weighted L2 Support...
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02/20/2017
A Statistical Learning Approach to Modal Regression
This paper studies the nonparametric modal regression problem systematic...
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12/20/2016