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Found 17 papers, 1 papers with code
Bandits with Abstention under Expert Advice
We study the classic problem of prediction with expert advice under bandit feedback.
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
We study best-of-both-worlds algorithms for $K$-armed linear contextual bandits.
Sum-max Submodular Bandits
Many online decision-making problems correspond to maximizing a sequence of submodular functions.
Fast and Effective GNN Training with Linearized Random Spanning Trees
We present a new effective and scalable framework for training GNNs in node classification tasks, based on the effective resistance, a powerful tool solidly rooted in graph theory.
Adversarial Online Collaborative Filtering
We investigate the problem of online collaborative filtering under no-repetition constraints, whereby users need to be served content in an online fashion and a given user cannot be recommended the same content item more than once.
A Gang of Adversarial Bandits
Users are in a social network and the learner is aided by a-priori knowledge of the strengths of the social links between all pairs of users.
Online Learning of Facility Locations
In this paper, we provide a rigorous theoretical investigation of an online learning version of the Facility Location problem which is motivated by emerging problems in real-world applications.
Flattening a Hierarchical Clustering through Active Learning
We investigate active learning by pairwise similarity over the leaves of trees originating from hierarchical clustering procedures.
Correlation Clustering with Adaptive Similarity Queries
In this work we investigate correlation clustering as an active learning problem: each similarity score can be learned by making a query, and the goal is to minimise both the disagreements and the total number of queries.
MaxHedge: Maximising a Maximum Online
We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set.
Online Reciprocal Recommendation with Theoretical Performance Guarantees
A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e. g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists.
On Pairwise Clustering with Side Information
We measure performance not based on the recovery of the hidden similarity function, but instead on how well we classify each item.
On the Troll-Trust Model for Edge Sign Prediction in Social Networks
In the problem of edge sign prediction, we are given a directed graph (representing a social network), and our task is to predict the binary labels of the edges (i. e., the positive or negative nature of the social relationships).
Even Trolls Are Useful: Efficient Link Classification in Signed Networks
We address the problem of classifying the links of signed social networks given their full structural topology.
A Linear Time Active Learning Algorithm for Link Classification
We provide a theoretical analysis within this model, showing that we can achieve an optimal (to whithin a constant factor) number of mistakes on any graph $G = (V, E)$ such that $|E|$ is at least order of $|V|^{3/2}$ by querying at most order of $|V|^{3/2}$ edge labels.
See the Tree Through the Lines: The Shazoo Algorithm
Although it is known how to predict the nodes of an unweighted tree in a nearly optimal way, in the weighted case a fully satisfactory algorithm is not available yet.
On higher-order perceptron algorithms
A new algorithm for on-line learning linear-threshold functions is proposed which efficiently combines second-order statistics about the data with the logarithmic behavior" of multiplicative/dual-norm algorithms.
