160 citations · 1.2k across the 55 of their papers we have counts for
36 papers · 1 filter
Budgeted Online Influence Maximization
Pierre Perrault, Jennifer Healey, Zheng Wen +1
We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a cho…
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping
Vikranth Dwaracherla, Zheng Wen, Ian Osband +3
In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions. A common approach to uncertainty estimation maintains…
An Analysis of Ensemble Sampling
Chao Qin, Zheng Wen, Xiuyuan Lu +1
Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable. In…
The Neural Testbed: Evaluating Joint Predictions
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +7
Predictive distributions quantify uncertainties ignored by point estimates. This paper introduces The Neural Testbed: an open-source benchmark for controlled and principled evaluat…
From Predictions to Decisions: The Importance of Joint Predictive Distributions
Zheng Wen, Ian Osband, Chao Qin +5
A fundamental challenge for any intelligent system is prediction: given some inputs, can you predict corresponding outcomes? Most work on supervised learning has focused on produci…
Epistemic Neural Networks
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +4
Intelligence relies on an agent's knowledge of what it does not know. This capability can be assessed based on the quality of joint predictions of labels across multiple inputs. In…
Joint Online Learning and Decision-making via Dual Mirror Descent
Alfonso Lobos, Paul Grigas, Zheng Wen
We consider an online revenue maximization problem over a finite time horizon subject to lower and upper bounds on cost. At each period, an agent receives a context vector sampled…
Reinforcement Learning, Bit by Bit
Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla +3
Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency poses an impediment to carrying this success over to real environ…
Neural Contextual Bandits with Deep Representation and Shallow Exploration
Pan Xu, Zheng Wen, Handong Zhao +1
We study a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the reward generating function is unknown. We propose a…
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization
Wenlong Mou, Zheng Wen, Xi Chen
We study the optimal sample complexity in large-scale Reinforcement Learning (RL) problems with policy space generalization, i.e. the agent has a prior knowledge that the optimal p…
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
Tong Yu, Branislav Kveton, Zheng Wen +2
We propose a novel framework for structured bandits, which we call an influence diagram bandit. Our framework captures complex statistical dependencies between actions, latent vari…
Hypermodels for Exploration
Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi +3
We study the use of hypermodels to represent epistemic uncertainty and guide exploration. This generalizes and extends the use of ensembles to approximate Thompson sampling. The co…
Waterfall Bandits: Learning to Sell Ads Online
Branislav Kveton, Saied Mahdian, S. Muthukrishnan +2
A popular approach to selling online advertising is by a waterfall, where a publisher makes sequential price offers to ad networks for an inventory, and chooses the winner in that…
Stochastic Online Learning with Probabilistic Graph Feedback
Shuai Li, Wei Chen, Zheng Wen +1
We consider a problem of stochastic online learning with general probabilistic graph feedback, where each directed edge in the feedback graph has probability . Two cases ar…
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…
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…
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…
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…