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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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03/16/2023
Distributionally Robust Optimization using Cost-Aware Ambiguity Sets
We present a novel class of ambiguity sets for distributionally robust o...
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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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02/20/2023
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
This paper introduces a new extragradient-type algorithm for a class of ...
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02/17/2023
Solving stochastic weak Minty variational inequalities without increasing batch size
This paper introduces a family of stochastic extragradient-type algorith...
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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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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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07/17/2022
SPIRAL: A Superlinearly Convergent Incremental Proximal Algorithm for Nonconvex Finite Sum Minimization
We introduce SPIRAL, a SuPerlinearly convergent Incremental pRoximal ALg...
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07/09/2021
Block Alternating Bregman Majorization Minimization with Extrapolation
In this paper, we consider a class of nonsmooth nonconvex optimization p...
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03/15/2021
Lasry-Lions Envelopes and Nonconvex Optimization: A Homotopy Approach
In large-scale optimization, the presence of nonsmooth and nonconvex ter...
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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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03/09/2020
A block inertial Bregman proximal algorithm for nonsmooth nonconvex problems
In this paper, a block inertial Bregman proximal algorithm, namely [], f...
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02/29/2020
OpEn: Code Generation for Embedded Nonconvex Optimization
We present Optimization Engine (OpEn): an open-source code generation to...
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08/04/2019
Multi-block Bregman proximal alternating linearized minimization and its application to sparse orthogonal nonnegative matrix factorization
We introduce and analyze BPALM and A-BPALM, two multi-block proximal alt...
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03/05/2019
