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Found 7 papers, 1 papers with code
Probabilistic Label Trees for Extreme Multi-label Classification
We first introduce the PLT model and discuss training and inference procedures and their computational costs.
On the computational complexity of the probabilistic label tree algorithms
First, we show that finding a tree with optimal training cost is NP-complete, nevertheless there are some tractable special cases with either perfect approximation or exact solution that can be obtained in linear time in terms of the number of labels $m$.
Learning to Crawl
Web crawling is the problem of keeping a cache of webpages fresh, i. e., having the most recent copy available when a page is requested.
Preference-based Online Learning with Dueling Bandits: A Survey
The aim of this paper is to provide a survey of the state of the art in this field, referred to as preference-based multi-armed bandits or dueling bandits.
Ranking Distributions based on Noisy Sorting
We also introduce a generalization of the model, in which the constraints on pairwise preferences are relaxed, and for which maximum likelihood estimation can be carried out based on a variation of the generalized iterative scaling algorithm.
Distributed Stochastic Optimization via Adaptive SGD
Scaling up the training process of these models is crucial, but the most popular algorithm, Stochastic Gradient Descent (SGD), is a serial method that is surprisingly hard to parallelize.
Multi-objective Bandits: Optimizing the Generalized Gini Index
We study the multi-armed bandit (MAB) problem where the agent receives a vectorial feedback that encodes many possibly competing objectives to be optimized.
