160 citations · 1.2k across the 55 of their papers we have counts for
7 papers · 1 filter
Online Diverse Learning to Rank from Partial-Click Feedback
Prakhar Gupta, Gaurush Hiranandani, Harvineet Singh +3
Learning to rank is an important problem in machine learning and recommender systems. In a recommender system, a user is typically recommended a list of items. Since the user is un…
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
Branislav Kveton, Csaba Szepesvari, Sharan Vaswani +3
We propose a bandit algorithm that explores by randomizing its history of rewards. Specifically, it pulls the arm with the highest mean reward in a non-parametric bootstrap sample…
Conservative Exploration using Interleaving
Sumeet Katariya, Branislav Kveton, Zheng Wen +1
In many practical problems, a learning agent may want to learn the best action in hindsight without ever taking a bad action, which is significantly worse than the default producti…
Optimal Bidding, Allocation and Budget Spending for a Demand Side Platform Under Many Auction Types
Alfonso Lobos, Paul Grigas, Zheng Wen +1
We develop a novel optimization model to maximize the profit of a Demand-Side Platform (DSP) while ensuring that the budget utilization preferences of the DSP's advertiser clients…
New Insights into Bootstrapping for Bandits
Sharan Vaswani, Branislav Kveton, Zheng Wen +3
We investigate the use of bootstrapping in the bandit setting. We first show that the commonly used non-parametric bootstrapping (NPB) procedure can be provably inefficient and est…
Offline Evaluation of Ranking Policies with Click Models
Shuai Li, Yasin Abbasi-Yadkori, Branislav Kveton +3
Many web systems rank and present a list of items to users, from recommender systems to search and advertising. An important problem in practice is to evaluate new ranking policies…
Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit
Yang Cao, Zheng Wen, Branislav Kveton +1
Multi-armed bandit (MAB) is a class of online learning problems where a learning agent aims to maximize its expected cumulative reward while repeatedly selecting to pull arms with…