160 citations · 1.2k across the 55 of their papers we have counts for
56 papers
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…
Evaluating High-Order Predictive Distributions in Deep Learning
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +3
Most work on supervised learning research has focused on marginal predictions. In decision problems, joint predictive distributions are essential for good performance. Previous wor…
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…
On the Approximation Relationship between Optimizing Ratio of Submodular (RS) and Difference of Submodular (DS) Functions
Pierre Perrault, Jennifer Healey, Zheng Wen +1
We demonstrate that from an algorithm guaranteeing an approximation factor for the ratio of submodular (RS) optimization problem, we can build another algorithm having a different…
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…
A Benchmark and Baseline for Language-Driven Image Editing
Jing Shi, Ning Xu, Trung Bui +3
Language-driven image editing can significantly save the laborious image editing work and be friendly to the photography novice. However, most similar work can only deal with a spe…
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…
Structured Policy Iteration for Linear Quadratic Regulator
Youngsuk Park, Ryan A. Rossi, Zheng Wen +2
Linear quadratic regulator (LQR) is one of the most popular frameworks to tackle continuous Markov decision process tasks. With its fundamental theory and tractable optimal policy,…
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…
Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making
Jie Zhou, Botao Hao, Zheng Wen +2
Multi-dimensional online decision making plays a crucial role in many real applications such as online recommendation and digital marketing. In these problems, a decision at each t…
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…
Improving Adversarial Text Generation by Modeling the Distant Future
Ruiyi Zhang, Changyou Chen, Zhe Gan +5
Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words…
Nested-Wasserstein Self-Imitation Learning for Sequence Generation
Ruiyi Zhang, Changyou Chen, Zhe Gan +3
Reinforcement learning (RL) has been widely studied for improving sequence-generation models. However, the conventional rewards used for RL training typically cannot capture suffic…
Bootstrapping Upper Confidence Bound
Botao Hao, Yasin Abbasi-Yadkori, Zheng Wen +1
Upper Confidence Bound (UCB) method is arguably the most celebrated one used in online decision making with partial information feedback. Existing techniques for constructing confi…
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…
Scalable Thompson Sampling via Optimal Transport
Ruiyi Zhang, Zheng Wen, Changyou Chen +1
Thompson sampling (TS) is a class of algorithms for sequential decision-making, which requires maintaining a posterior distribution over a model. However, calculating exact posteri…
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…