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Found 7 papers, 0 papers with code
Optimal Learning of Mallows Block Model
The main result of the paper is a tight sample complexity bound for learning Mallows and Generalized Mallows Model.
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Under the more challenging weak linear separability condition, we design an efficient algorithm with a mistake bound of $\min (2^{\widetilde{O}(K \log^2 (1/\gamma))}, 2^{\widetilde{O}(\sqrt{1/\gamma} \log K)})$.
The information-theoretic value of unlabeled data in semi-supervised learning
Learning with the knowledge of the distribution (a. k. a.
Finite Sample Analyses for TD(0) with Function Approximation
TD(0) is one of the most commonly used algorithms in reinforcement learning.
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach
We study the problem of online rank elicitation, assuming that rankings of a set of alternatives obey the Plackett-Luce distribution.
Online F-Measure Optimization
In this paper, we study the problem of F-measure maximization in the setting of online learning.
Optimistic Planning in Markov Decision Processes Using a Generative Model
We consider the problem of online planning in a Markov decision process with discounted rewards for any given initial state.
