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Found 10 papers, 1 papers with code
Attack Detection Using Item Vector Shift in Matrix Factorisation Recommenders
This paper proposes a novel method for detecting shilling attacks in Matrix Factorization (MF)-based Recommender Systems (RS), in which attackers use false user-item feedback to promote a specific item.
High Accuracy and Low Regret for User-Cold-Start Using Latent Bandits
We develop a novel latent-bandit algorithm for tackling the cold-start problem for new users joining a recommender system.
Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders
In practice, users of a Recommender System (RS) fall into a few clusters based on their preferences.
Bandit Convex Optimisation Revisited: FTRL Achieves $\tilde{O}(t^{1/2})$ Regret
We show that a kernel estimator using multiple function evaluations can be easily converted into a sampling-based bandit estimator with expectation equal to the original kernel estimate.
Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction
We illustrate the effectiveness of the attacks against the next word prediction model used in Google's GBoard app, a widely used mobile keyboard app that has been an early adopter of federated learning for production use.
Online Caching with no Regret: Optimistic Learning via Recommendations
We build upon the Follow-the-Regularized-Leader (FTRL) framework, which is developed further here to include predictions for the file requests, and we design online caching algorithms for bipartite networks with pre-reserved or dynamic storage subject to time-average budget constraints.
Online Caching with Optimistic Learning
The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas.
The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains
We study Online Lazy Gradient Descent for optimisation on a strongly convex domain.
Lazy Online Gradient Descent is Universal on Polytopes
We prove the familiar Lazy Online Gradient Descent algorithm is universal on polytope domains.
Optimality of the Subgradient Algorithm in the Stochastic Setting
We show that the Subgradient algorithm is universal for online learning on the simplex in the sense that it simultaneously achieves $O(\sqrt N)$ regret for adversarial costs and $O(1)$ pseudo-regret for i. i. d costs.
