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Found 17 papers, 1 papers with code
Boosting Gradient Ascent for Continuous DR-submodular Maximization
The fundamental idea of our boosting technique is to exploit non-oblivious search to derive a novel auxiliary function $F$, whose stationary points are excellent approximations to the global maximum of the original DR-submodular objective $f$.
Multi-channel Integrated Recommendation with Exposure Constraints
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms.
Decentralized Weakly Convex Optimization Over the Stiefel Manifold
We focus on a class of non-smooth optimization problems over the Stiefel manifold in the decentralized setting, where a connected network of $n$ agents cooperatively minimize a finite-sum objective function with each component being weakly convex in the ambient Euclidean space.
An Online Algorithm for Chance Constrained Resource Allocation
This paper studies the online stochastic resource allocation problem (RAP) with chance constraints.
Flattened Graph Convolutional Networks For Recommendation
Third, we propose a layer ensemble technique which improves the expressiveness of the learned representations by assembling the layer-wise neighborhood representations at the final layer.
Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization
Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics.
Online Learning for Non-monotone Submodular Maximization: From Full Information to Bandit Feedback
In this paper, we revisit the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, which finds wide real-world applications in the domain of machine learning, economics, and operations research.
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation
The experimental results verify that our proposed NEGCN framework can significantly enhance the performance for various typical GCN models on both node classification and recommendation tasks.
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
In the online setting, for the first time we consider the adversarial delays for stochastic gradient feedback, under which we propose a boosting online gradient algorithm with the same non-oblivious function $F$.
Non-Recursive Graph Convolutional Networks
In this way, each node can be directly represented by concatenating the information extracted independently from each hop of its neighbors thereby avoiding the recursive neighborhood expansion across layers.
Sparse High-Order Portfolios via Proximal DCA and SCA
Optimization for the MVSKC model is of great difficulty in two parts.
Single-Layer Graph Convolutional Networks For Recommendation
The results verify that the proposed model outperforms existing GCN models considerably and yields up to a few orders of magnitude speedup in training, in terms of the recommendation performance.
Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning
Second, we propose a stochastic variant of ManPPA called StManPPA, which is well suited for large-scale computation, and establish its sublinear convergence rate.
Weakly Convex Optimization over Stiefel Manifold Using Riemannian Subgradient-Type Methods
To the best of our knowledge, these are the first convergence guarantees for using Riemannian subgradient-type methods to optimize a class of nonconvex nonsmooth functions over the Stiefel manifold.
Label-Aware Graph Convolutional Networks
In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models.
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT
The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more challenging but more useful in large-scale IoT.
An Efficient Augmented Lagrangian Based Method for Constrained Lasso
Variable selection is one of the most important tasks in statistics and machine learning.
