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Found 22 papers, 10 papers with code
Spurious Stationarity and Hardness Results for Mirror Descent
Despite the considerable success of Bregman proximal-type algorithms, such as mirror descent, in machine learning, a critical question remains: Can existing stationarity measures, often based on Bregman divergence, reliably distinguish between stationary and non-stationary points?
Automatic Outlier Rectification via Optimal Transport
In this paper, we propose a novel conceptual framework to detect outliers using optimal transport with a concave cost function.
Unifying Distributionally Robust Optimization via Optimal Transport Theory
In the past few years, there has been considerable interest in two prominent approaches for Distributionally Robust Optimization (DRO): Divergence-based and Wasserstein-based methods.
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
This observation allows us to provide an approximation bound for the distance between the fixed-point set of BAPG and the critical point set of GW.
Outlier-Robust Gromov-Wasserstein for Graph Data
Gromov-Wasserstein (GW) distance is a powerful tool for comparing and aligning probability distributions supported on different metric spaces.
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
As the graphs to be aligned are usually constructed from different sources, the inconsistency issues of structures and features between two graphs are ubiquitous in real-world applications.
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls
The optimal design of experiments typically involves solving an NP-hard combinatorial optimization problem.
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
Distributionally robust optimization has been shown to offer a principled way to regularize learning models.
Nonsmooth Nonconvex-Nonconcave Minimax Optimization: Primal-Dual Balancing and Iteration Complexity Analysis
Specifically, we consider the setting where the primal function has a nonsmooth composite structure and the dual function possesses the Kurdyka-Lojasiewicz (KL) property with exponent $\theta \in [0, 1)$.
Rethinking Graph Neural Networks for Anomaly Detection
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection.
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data
In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks.
Learning Proximal Operators to Discover Multiple Optima
We present an end-to-end method to learn the proximal operator of a family of training problems so that multiple local minima can be quickly obtained from initial guesses by iterating the learned operator, emulating the proximal-point algorithm that has fast convergence.
Modified Frank Wolfe in Probability Space
We propose a novel Frank-Wolfe (FW) procedure for the optimization of infinite-dimensional functionals of probability measures - a task which arises naturally in a wide range of areas including statistical learning (e. g. variational inference) and artificial intelligence (e. g. generative adversarial networks).
Deconvolutional Networks on Graph Data
In this paper, we consider an inverse problem in graph learning domain -- ``given the graph representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct the input graph signal?"
Accurate and fast detection of copy number variations from short-read whole-genome sequencing with deep convolutional neural network
Here, we propose CNV-Net, a novel approach for CNV detection using a six-layer convolutional neural network.
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine
In this paper, we focus on a family of Wasserstein distributionally robust support vector machine (DRSVM) problems and propose two novel epigraphical projection-based incremental algorithms to solve them.
Dirichlet Graph Variational Autoencoder
In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors.
Understanding Notions of Stationarity in Non-Smooth Optimization
Many contemporary applications in signal processing and machine learning give rise to structured non-convex non-smooth optimization problems that can often be tackled by simple iterative methods quite effectively.
The Gambler's Problem and Beyond
We analyze the Gambler's problem, a simple reinforcement learning problem where the gambler has the chance to double or lose the bets until the target is reached.
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression
In this paper, we take a first step towards resolving the above difficulty by developing a first-order algorithmic framework for tackling a class of Wasserstein distance-based distributionally robust logistic regression (DRLR) problem.
A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression
In this paper, we take a first step towards resolving the above difficulty by developing a first-order algorithmic framework for tackling a class of Wasserstein distance-based distributionally robust logistic regression (DRLR) problem.
Policy Optimization with Second-Order Advantage Information
Policy optimization on high-dimensional continuous control tasks exhibits its difficulty caused by the large variance of the policy gradient estimators.
