160 citations · 1.2k across the 55 of their papers we have counts for
10 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…
Posterior Sampling for Large Scale Reinforcement Learning
Georgios Theocharous, Zheng Wen, Yasin Abbasi-Yadkori +1
We propose a practical non-episodic PSRL algorithm that unlike recent state-of-the-art PSRL algorithms uses a deterministic, model-independent episode switching schedule. Our algor…
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…
A Tutorial on Thompson Sampling
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni +2
Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immed…
Profit Maximization for Online Advertising Demand-Side Platforms
Paul Grigas, Alfonso Lobos, Zheng Wen +1
We develop an optimization model and corresponding algorithm for the management of a demand-side platform (DSP), whereby the DSP aims to maximize its own profit while acquiring val…
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…
Deep Exploration via Randomized Value Functions
Ian Osband, Benjamin Van Roy, Daniel Russo +1
We study the use of randomized value functions to guide deep exploration in reinforcement learning. This offers an elegant means for synthesizing statistically and computationally…
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…
Does Weather Matter? Causal Analysis of TV Logs
Shi Zong, Branislav Kveton, Shlomo Berkovsky +3
Weather affects our mood and behaviors, and many aspects of our life. When it is sunny, most people become happier; but when it rains, some people get depressed. Despite this evide…