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Found 17 papers, 2 papers with code
Bandits with Abstention under Expert Advice
We study the classic problem of prediction with expert advice under bandit feedback.
Multi-class Graph Clustering via Approximated Effective $p$-Resistance
We prove upper and lower bounds on this approximation and observe that it is exact when the graph is a tree.
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.
Improved Regret Bounds for Tracking Experts with Memory
We address the problem of sequential prediction with expert advice in a non-stationary environment with long-term memory guarantees in the sense of Bousquet and Warmuth [4].
Online Multitask Learning with Long-Term Memory
We provide an algorithm that predicts on each trial in time linear in the number of hypotheses when the hypothesis class is finite.
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.
Online Matrix Completion with Side Information
In this setting, we provide an example where the side information is not directly specified in advance.
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 Prediction of Switching Graph Labelings with Cluster Specialists
We address the problem of predicting the labeling of a graph in an online setting when the labeling is changing over time.
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.
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.
Mistake Bounds for Binary Matrix Completion
We study the problem of completing a binary matrix in an online learning setting.
Online Prediction at the Limit of Zero Temperature
We design an online algorithm to classify the vertices of a graph.
The VC-Dimension of Similarity Hypotheses Spaces
Given a set $X$ and a function $h:X\longrightarrow\{0, 1\}$ which labels each element of $X$ with either $0$ or $1$, we may define a function $h^{(s)}$ to measure the similarity of pairs of points in $X$ according to $h$.
Fast Prediction on a Tree
Given an $n$-vertex weighted tree with structural diameter $S$ and a subset of $m$ vertices, we present a technique to compute a corresponding $m \times m$ Gram matrix of the pseudoinverse of the graph Laplacian in $O(n+ m^2 + m S)$ time.
Online Prediction on Large Diameter Graphs
Current on-line learning algorithms for predicting the labelling of a graph have an important limitation in the case of large diameter graphs; the number of mistakes made by such algorithms may be proportional to the square root of the number of vertices, even when tackling simple problems.
