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Found 18 papers, 0 papers with code
Towards Efficient Replay in Federated Incremental Learning
Then, the client trains the local model with both the cached samples and the samples from the new task.
Denoising Time Cycle Modeling for Recommendation
We define the subset of user behaviors that are irrelevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance.
Edge-cloud Collaborative Learning with Federated and Centralized Features
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy.
Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations
First, a user has multiple embeddings to reflect various interests, and such number is fixed.
Quickest Change Detection in Anonymous Heterogeneous Sensor Networks
The problem of quickest change detection (QCD) in anonymous heterogeneous sensor networks is studied.
A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization
However, GDA has been proved to converge to stationary points for nonconvex minimax optimization, which are suboptimal compared with local minimax points.
Escaping Saddle Points in Nonconvex Minimax Optimization via Cubic-Regularized Gradient Descent-Ascent
Our result shows that Cubic-GDA achieves an orderwise faster convergence rate than the standard GDA for a wide spectrum of gradient dominant geometry.
Learning Graph Neural Networks with Approximate Gradient Descent
Two types of GNNs are investigated, depending on whether labels are attached to nodes or graphs.
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning
Using this framework, we show that space-filling sample designs, such as blue noise and Poisson disk sampling, which optimize spectral properties, outperform random designs in terms of the generalization gap and characterize this gain in a closed-form.
Prospect Theory Based Crowdsourcing for Classification in the Presence of Spammers
We consider the $M$-ary classification problem via crowdsourcing, where crowd workers respond to simple binary questions and the answers are aggregated via decision fusion.
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis
This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models.
MR-GAN: Manifold Regularized Generative Adversarial Networks
Despite the growing interest in generative adversarial networks (GANs), training GANs remains a challenging problem, both from a theoretical and a practical standpoint.
K-medoids Clustering of Data Sequences with Composite Distributions
In particular, the error exponent is characterized when either the Kolmogrov-Smirnov distance or the maximum mean discrepancy are used as the distance metric.
Decision Tree Design for Classification in Crowdsourcing Systems
In this paper, we present a novel sequential paradigm for classification in crowdsourcing systems.
Robust Decentralized Learning Using ADMM with Unreliable Agents
We also provide conditions on the erroneous updates for exact convergence to the optimal solution.
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization
Then, by exploiting the Kurdyka-{\L}ojasiewicz (\KL) property for a broad class of functions, we establish the linear and sub-linear convergence rates of the function value sequence generated by APGnc.
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models
Influential node detection is a central research topic in social network analysis.
Multi-object Classification via Crowdsourcing with a Reject Option
We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize the crowd's classification performance.
