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Found 18 papers, 3 papers with code
Lagrangian Index Policy for Restless Bandits with Average Reward
In particular, we compare the performance of LIP with the performance of the Whittle Index Policy (WIP), both heuristic policies known to be asymptotically optimal under certain natural conditions.
Tabular and Deep Learning for the Whittle Index
The Whittle index policy is a heuristic that has shown remarkably good performance (with guaranteed asymptotic optimality) when applied to the class of problems known as Restless Multi-Armed Bandit Problems (RMABPs).
Deep reinforcement learning for weakly coupled MDP's with continuous actions
This paper introduces the Lagrange Policy for Continuous Actions (LPCA), a reinforcement learning algorithm specifically designed for weakly coupled MDP problems with continuous action spaces.
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion
We extend the provably convergent Full Gradient DQN algorithm for discounted reward Markov decision processes from Avrachenkov et al. (2021) to average reward problems.
Multilayer hypergraph clustering using the aggregate similarity matrix
We consider the community recovery problem on a multilayer variant of the hypergraph stochastic block model (HSBM).
Higher-Order Spectral Clustering for Geometric Graphs
While the standard spectral clustering is often not effective for geometric graphs, we present an effective generalization, which we call higher-order spectral clustering.
Online Algorithms for Estimating Change Rates of Web Pages
We provide three novel schemes for online estimation of page change rates, all of which have extremely low running times per iteration.
LFGCN: Levitating over Graphs with Levy Flights
Due to high utility in many applications, from social networks to blockchain to power grids, deep learning on non-Euclidean objects such as graphs and manifolds, coined Geometric Deep Learning (GDL), continues to gain an ever increasing interest.
Community recovery in non-binary and temporal stochastic block models
This article studies the estimation of latent community memberships from pairwise interactions in a network of $N$ nodes, where the observed interactions can be of arbitrary type, including binary, categorical, and vector-valued, and not excluding even more general objects such as time series or spatial point patterns.
Almost exact recovery in noisy semi-supervised learning
Graph-based semi-supervised learning methods combine the graph structure and labeled data to classify unlabeled data.
Dynamic social learning under graph constraints
We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively $\alpha$-homogeneous rewards.
Change Rate Estimation and Optimal Freshness in Web Page Crawling
Specifically, we provide two novel schemes for online estimation of page change rates.
Fractional Graph Convolutional Networks (FGCN) for Semi-Supervised Learning
Due to high utility in many applications, from social networks to blockchain to power grids, deep learning on non-Euclidean objects such as graphs and manifolds continues to gain an ever increasing interest.
Graphlet Count Estimation via Convolutional Neural Networks
The number of each graphlet, called graphlet count, is a signature which characterizes the local network structure of a given graph.
The Power of Side-information in Subgraph Detection
In this work, we tackle the problem of hidden community detection.
Bayesian Inference of Online Social Network Statistics via Lightweight Random Walk Crawls
Online social networks (OSN) contain extensive amount of information about the underlying society that is yet to be explored.
Parallel and Distributed Approaches for Graph Based Semi-supervised Learning
It is based onsampling of nodes by performing a random walk on the graph.
Semi-supervised Learning with Regularized Laplacian
We study a semi-supervised learning method based on the similarity graph and RegularizedLaplacian.
