140 citations · 1.3k across the 125 of their papers we have counts for
7 papers · 1 filter
Stochastic Low-Rank Bandits
Branislav Kveton, Csaba Szepesvari, Anup Rao +3
Many problems in computer vision and recommender systems involve low-rank matrices. In this work, we study the problem of finding the maximum entry of a stochastic low-rank matrix…
SpectralLeader: Online Spectral Learning for Single Topic Models
Tong Yu, Branislav Kveton, Zheng Wen +2
We study the problem of learning a latent variable model from a stream of data. Latent variable models are popular in practice because they can explain observed data in terms of un…
Finding Subcube Heavy Hitters in Analytics Data Streams
Branislav Kveton, S. Muthukrishnan, Hoa T. Vu +1
Data streams typically have items of large number of dimensions. We study the fundamental heavy-hitters problem in this setting. Formally, the data stream consists of -dimension…
Bernoulli Rank- Bandits for Click Feedback
Sumeet Katariya, Branislav Kveton, Csaba Szepesvári +2
The probability that a user will click a search result depends both on its relevance and its position on the results page. The position based model explains this behavior by ascrib…
Online Learning to Rank in Stochastic Click Models
Masrour Zoghi, Tomas Tunys, Mohammad Ghavamzadeh +3
Online learning to rank is a core problem in information retrieval and machine learning. Many provably efficient algorithms have been recently proposed for this problem in specific…
Model-Independent Online Learning for Influence Maximization
Sharan Vaswani, Branislav Kveton, Zheng Wen +3
We consider influence maximization (IM) in social networks, which is the problem of maximizing the number of users that become aware of a product by selecting a set of "seed" users…