On the disturbance response and external stability of a saturating static-feedback-controlled double integrator. - Zheng Wen 002; Sandip Roy 002; Ali Saberi (Autom., 2008)

On the Dynamic Response of a Saturating Static Feedback-Controlled Single Integrator Driven by White Noise. - Zheng Wen 002; Sandip Roy 002; Ali Saberi (IEEE Trans. Autom. Control., 2010)

A novel algorithm for incompressible flow using only a coarse grid projection. - Michael Lentine; Zheng Wen 002; Ronald Fedkiw (ACM Trans. Graph., 2010)

Asynchronous Evolution for Fully-Implicit and Semi-Implicit Time Integration. - Craig A. Schroeder; Nipun Kwatra; Zheng Wen 002; Ronald Fedkiw (Comput. Graph. Forum, 2011)

Semi-implicit surface tension formulation with a Lagrangian surface mesh on an Eulerian simulation grid. - Craig A. Schroeder; Zheng Wen 002; Ronald Fedkiw (J. Comput. Phys., 2012)

Adaptive Submodular Maximization in Bandit Setting. - Victor Gabillon; Branislav Kveton; Zheng Wen 002; Brian Eriksson; S. Muthukrishnan 001 (NIPS, 2013)

Efficient Exploration and Value Function Generalization in Deterministic Systems. - Zheng Wen 002; Benjamin Van Roy (NIPS, 2013)

A new incompressibility discretization for a hybrid particle MAC grid representation with surface tension. - Zheng Wen 002 (2013)

Large-Scale Optimistic Adaptive Submodularity. - Victor Gabillon; Branislav Kveton; Zheng Wen 002; Brian Eriksson; S. Muthukrishnan 001 (AAAI, 2014)

Diversified Utility Maximization for Recommendations. - Azin Ashkan; Branislav Kveton; Shlomo Berkovsky; Zheng Wen 002 (RecSys Posters, 2014)

DUM: Diversity-Weighted Utility Maximization for Recommendations. - Azin Ashkan; Branislav Kveton; Shlomo Berkovsky; Zheng Wen 002 (CoRR, 2014)

Generalization and Exploration via Randomized Value Functions. - Benjamin Van Roy; Zheng Wen 002 (CoRR, 2014)

Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits. - Branislav Kveton; Zheng Wen 002; Azin Ashkan; Csaba Szepesvári (AISTATS, 2015)

Cascading Bandits: Learning to Rank in the Cascade Model. - Branislav Kveton; Csaba Szepesvári; Zheng Wen 002; Azin Ashkan (ICML, 2015)

Optimal Greedy Diversity for Recommendation. - Azin Ashkan; Branislav Kveton; Shlomo Berkovsky; Zheng Wen 002 (IJCAI, 2015)

Combinatorial Cascading Bandits. - Branislav Kveton; Zheng Wen 002; Azin Ashkan; Csaba Szepesvári (NIPS, 2015)

Cascading Bandits. - Branislav Kveton; Csaba Szepesvári; Zheng Wen 002; Azin Ashkan (CoRR, 2015)

A new incompressibility discretization for a hybrid particle MAC grid representation with surface tension. - Zheng Wen 002; Bo Zhu 002; ByungMoon Kim; Ronald Fedkiw (J. Comput. Phys., 2015)

DCM Bandits: Learning to Rank with Multiple Clicks. - Sumeet Katariya; Branislav Kveton; Csaba Szepesvári; Zheng Wen 002 (ICML, 2016)

Generalization and Exploration via Randomized Value Functions. - Ian Osband; Benjamin Van Roy; Zheng Wen 002 (ICML, 2016)

Stochastic Rank-1 Bandits. - Sumeet Katariya; Branislav Kveton; Csaba Szepesvári; Claire Vernade; Zheng Wen 002 (AISTATS, 2017)

Model-Independent Online Learning for Influence Maximization. - Sharan Vaswani; Branislav Kveton; Zheng Wen 002; Mohammad Ghavamzadeh; Laks V. S. Lakshmanan; Mark Schmidt 001 (ICML, 2017)

Online Learning to Rank in Stochastic Click Models. - Masrour Zoghi; Tomás Tunys; Mohammad Ghavamzadeh; Branislav Kveton; Csaba Szepesvári; Zheng Wen 002 (ICML, 2017)

Bernoulli Rank-1 Bandits for Click Feedback. - Sumeet Katariya; Branislav Kveton; Csaba Szepesvári; Claire Vernade; Zheng Wen 002 (IJCAI, 2017)

An Interactive Points of Interest Guidance System. - Georgios Theocharous; Nikos Vlassis; Zheng Wen 002 (IUI Companion, 2017)

Get to the Bottom: Causal Analysis for User Modeling. - Shi Zong; Branislav Kveton; Shlomo Berkovsky; Azin Ashkan; Zheng Wen 002 (UMAP, 2017)

Does Weather Matter?: Causal Analysis of TV Logs. - Shi Zong; Branislav Kveton; Shlomo Berkovsky; Azin Ashkan; Nikos Vlassis; Zheng Wen 002 (WWW (Companion Volume), 2017)

Diffusion Independent Semi-Bandit Influence Maximization. - Sharan Vaswani; Branislav Kveton; Zheng Wen 002; Mohammad Ghavamzadeh; Laks V. S. Lakshmanan; Mark Schmidt 001 (CoRR, 2017)

Posterior Sampling for Large Scale Reinforcement Learning. - Georgios Theocharous; Zheng Wen 002; Yasin Abbasi-Yadkori; Nikos Vlassis (CoRR, 2017)

Stochastic Low-Rank Bandits. - Branislav Kveton; Csaba Szepesvári; Anup Rao 002; Zheng Wen 002; Yasin Abbasi-Yadkori; S. Muthukrishnan 001 (CoRR, 2017)

Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization. - Zheng Wen 002; Benjamin Van Roy (Math. Oper. Res., 2017)

Offline Evaluation of Ranking Policies with Click Models. - Shuai Li 010; Yasin Abbasi-Yadkori; Branislav Kveton; S. Muthukrishnan 001; Vishwa Vinay; Zheng Wen 002 (KDD, 2018)

Scalar Posterior Sampling with Applications. - Georgios Theocharous; Zheng Wen 002; Yasin Abbasi; Nikos Vlassis (NeurIPS, 2018)

New Insights into Bootstrapping for Bandits. - Sharan Vaswani; Branislav Kveton; Zheng Wen 002; Anup Rao 002; Mark Schmidt 001; Yasin Abbasi-Yadkori (CoRR, 2018)

Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. - Branislav Kveton; Csaba Szepesvári; Zheng Wen 002; Mohammad Ghavamzadeh; Tor Lattimore (CoRR, 2018)

A Tutorial on Thompson Sampling. - Daniel Russo 001; Benjamin Van Roy; Abbas Kazerouni; Ian Osband; Zheng Wen 002 (Found. Trends Mach. Learn., 2018)

Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. - Branislav Kveton; Csaba Szepesvári; Sharan Vaswani; Zheng Wen 002; Tor Lattimore; Mohammad Ghavamzadeh (ICML, 2019)

Bootstrapping Upper Confidence Bound. - Botao Hao; Yasin Abbasi-Yadkori; Zheng Wen 002; Guang Cheng 003 (NeurIPS, 2019)

Waterfall Bandits: Learning to Sell Ads Online. - Branislav Kveton; Saied Mahdian; S. Muthukrishnan 001; Zheng Wen 002; Yikun Xian (CoRR, 2019)

Deep Exploration via Randomized Value Functions. - Ian Osband; Benjamin Van Roy; Daniel J. Russo; Zheng Wen 002 (J. Mach. Learn. Res., 2019)

Stochastic Online Learning with Probabilistic Graph Feedback. - Shuai Li 010; Wei Chen 013; Zheng Wen 002; Kwong-Sak Leung (AAAI, 2020)

Hypermodels for Exploration. - Vikranth Dwaracherla; Xiuyuan Lu; Morteza Ibrahimi; Ian Osband; Zheng Wen 002; Benjamin Van Roy (ICLR, 2020)

On Efficiency in Hierarchical Reinforcement Learning. - Zheng Wen 002; Doina Precup; Morteza Ibrahimi; André Barreto 001; Benjamin Van Roy; Satinder Singh 001 (NeurIPS, 2020)

Low-rank Tensor Bandits. - Botao Hao; Jie Zhou; Zheng Wen 002; Will Wei Sun (CoRR, 2020)

Epistemic Neural Networks. - Ian Osband; Zheng Wen 002; Mohammad Asghari; Morteza Ibrahimi; Xiyuan Lu; Benjamin Van Roy (CoRR, 2021)

Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal Predictions. - Xiuyuan Lu; Ian Osband; Benjamin Van Roy; Zheng Wen 002 (CoRR, 2021)

Evaluating Predictive Distributions: Does Bayesian Deep Learning Work? - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Botao Hao; Morteza Ibrahimi; Dieterich Lawson; Xiuyuan Lu; Brendan O'Donoghue; Benjamin Van Roy (CoRR, 2021)

The Neural Testbed: Evaluating Joint Predictions. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Xiuyuan Lu; Morteza Ibrahimi; Dieterich Lawson; Botao Hao; Brendan O'Donoghue; Benjamin Van Roy (NeurIPS, 2022)

An Analysis of Ensemble Sampling. - Chao Qin; Zheng Wen 002; Xiuyuan Lu; Benjamin Van Roy (NeurIPS, 2022)

Evaluating high-order predictive distributions in deep learning. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Xiuyuan Lu; Benjamin Van Roy (UAI, 2022)

Robustness of Epinets against Distributional Shifts. - Xiuyuan Lu; Ian Osband; Seyed Mohammad Asghari; Sven Gowal; Vikranth Dwaracherla; Zheng Wen 002; Benjamin Van Roy (CoRR, 2022)

Leveraging Demonstrations to Improve Online Learning: Quality Matters. - Botao Hao; Rahul Jain 002; Tor Lattimore; Benjamin Van Roy; Zheng Wen 002 (ICML, 2023)

Epistemic Neural Networks. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Morteza Ibrahimi; Xiuyuan Lu; Benjamin Van Roy (NeurIPS, 2023)

Approximate Thompson Sampling via Epistemic Neural Networks. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Morteza Ibrahimi; Xiuyuan Lu; Benjamin Van Roy (UAI, 2023)

Efficient Online Learning with Offline Datasets for Infinite Horizon MDPs: A Bayesian Approach. - Dengwang Tang; Rahul Jain 002; Botao Hao; Zheng Wen 002 (CoRR, 2023)

RLHF and IIA: Perverse Incentives. - Wanqiao Xu; Shi Dong 003; Xiuyuan Lu; Grace Lam; Zheng Wen 002; Benjamin Van Roy (CoRR, 2023)

Reinforcement Learning, Bit by Bit. - Xiuyuan Lu; Benjamin Van Roy; Vikranth Dwaracherla; Morteza Ibrahimi; Ian Osband; Zheng Wen 002 (Found. Trends Mach. Learn., 2023)

Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping. - Vikranth Dwaracherla; Zheng Wen 002; Ian Osband; Xiuyuan Lu; Seyed Mohammad Asghari; Benjamin Van Roy (Trans. Mach. Learn. Res., 2023)

Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale. - Botao Hao; Rahul Jain 002; Dengwang Tang; Zheng Wen 002 (Trans. Mach. Learn. Res., 2023)

Online Bandit Learning with Offline Preference Data. - Akhil Agnihotri; Rahul Jain 002; Deepak Ramachandran; Zheng Wen 002 (CoRR, 2024)

Best Policy Learning from Trajectory Preference Feedback. - Akhil Agnihotri; Rahul Jain 002; Deepak Ramachandran; Zheng Wen 002 (CoRR, 2025)

Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models. - Akhil Agnihotri; Rahul Jain 002; Deepak Ramachandran; Zheng Wen 002 (CoRR, 2025)