Journal Papers
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[J11]
Y. Chen, Z. Wen, and Y. Xie, "Dynamic Pricing in an Evolving and Unknown Marketplace", accepted by Management Science. [SSRN]
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[J10]
Z. Yu, J. Zhang, Z. Wen, A. Tacchetti, M. Wang, I. Gemp, "Teamwork Reinforcement Learning with Concave Utilities", accepted by IEEE Transactions on Mobile Computing.
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[J9]
B. Hao, R. Jain, D. Tang, and Z. Wen, "Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale", accepted by Transactions on Machine Learning Research, with minor revision. [arXiv]
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[J7]
X. Lu, B. Van Roy, V. Dwaracherla, M. Ibrahimi, I. Osband, and Z. Wen, "Reinforcement Learning, Bit by Bit", Foundations and Trends® in Machine Learning, Vol. 16: No. 6, pp 733-865.. [arXiv]
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[J5] D. Russo, B. Van Roy, A. Kazerouni, I. Osband, and Z. Wen, "A Tutorial on Thompson Sampling", Foundations and Trends® in Machine Learning, Vol. 11, No. 1, pp 1-96, 2018. [arXiv]
Book Chapters
Papers In Progress
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Z. Wen, I. Osband, C. Qin, X. Lu, M. Ibrahimi, V. Dwaracherla, S. M. Asghari, and B. Van Roy, "From Predictions to Decisions: The Importance of Joint Predictive Distributions", to be submitted. [arXiv]
J. Zhou, B. Hao, Z. Wen, J. Zhang, W. W. Sun, "Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making", submitted to Journal of the American Statistical Association (JASA), first-round major revision. [arXiv]
"The Target Return Strategy", with Prof. Ying Ian Xue and Prof. Xu Jiang, submitted to The Financial Review, third-round revision.
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P. Grigas, A. Lobos, Z. Wen, and K. Lee,
"Optimal Bidding, Allocation, and Budget Spending for a Demand-Side Platform with Generic Auctions", submitted to Operations Research, first-round major revision. [SSRN]
Full Conference Papers
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[C48] I. Osband, Z. Wen, S. M. Asghari, V. Dwaracherla, M. Ibrahimi, X. Lu, and B. Van Roy, "Epistemic Neural Networks", accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) as a spotlight. [arXiv]
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[C47] I. Osband, Z. Wen, S. M. Asghari, V. Dwaracherla, M. Ibrahimi, X. Lu, and B. Van Roy, "Approximate Thompson Sampling via Epistemic Neural Networks", 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023), Pittsburgh, PA. [arXiv]
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[C45]
I. Osband, Z. Wen, S. M. Asghari, V. Dwaracherla, B. Hao, M. Ibrahimi, D. Lawson, X. Lu, B. O'Donoghue, and B. Van Roy, "The Neural Testbed: Evaluating Predictive Distributions", the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA. [arXiv][OpenReview]
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[C38] T. Yu, B. Kveton, Z. Wen, R. Zhang, and O. J. Mengshoel, "Influence Diagram Bandits", Thirty-seventh International Conference on Machine Learning (ICML 2020), online conference, originally planned to be held at Vienna, Austria.
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[C36] P. Perrault, J. Healey, Z. Wen, and M. Valko, "Budgeted Online Influence Maximization", Thirty-seventh International Conference on Machine Learning (ICML 2020), online conference, originally planned to be held at Vienna, Austria.
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[C35] R. Zhang, C. Chen, Z. Gan, W. Wang, D. Shen, G. Wang, Z. Wen, and L. Carin, "Improving Adversarial Text Generation by Modeling the Distant Future", the 58th annual meeting of the Association for Computational Linguistic (ACL 2020), online conference, originally planned to be held at Seattle, Washington.
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[C34] R. Zhang, C. Chen, Z. Gan, Z. Wen, W. Wang, and L. Carin, "Nested-Wasserstein Self-Imitation Learning for Sequence Generation", the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), online conference, originally planned to be held at Palermo, Sicily, Italy. [arXiv]
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[C33] V. Dwaracherla, X. Lu, M. Ibrahimi, I. Osband, Z. Wen, and B. Van Roy, "Hypermodels for Exploration", the Eighth International Conference on Learning Representations (ICLR 2020), online conference, originally planned to be held at Addis Ababa, Ethiopia.
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[C30]
Y. Xue, Z. Wen, and X. Jiang, "The Optimal Reservation Price",
accepted for presentation at
- the 2020 Southern Finance Association (SFA) Annual Meeting, Online Conference.
- the 59th Annual Southwestern Finance Association (SWFA) Conference, San Antonio, Texas, 2020.
- the 2019 Southern Finance Association (SFA) Annual Meeting, Orlando, Florida, with title "The Optimal Price Trigger".
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[C28]
B. Kveton, C. Szepesvari, S. Vaswani, Z. Wen, M. Ghavamzadeh, and T. Lattimore, "Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits", the Thirty-sixth International Conference on Machine Learning (ICML 2019), Long Beach, California. [arXiv]
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[C27]
R. Zhang, Z. Wen, C. Chen, C. Fang, T. Yu, and L. Carin, "Scalable Thompson Sampling via Optimal Transport", Proceedings of the 22nd International Conference on
Artificial Intelligence and Statistics (AISTATS 2019), Naha, Okinawa, Japan.
[C20]
M. Zoghi, T. Tunys, M. Ghavamzadeh, B. Kveton, C. Szepesvari, and Z. Wen, "Online Learning to Rank in Stochastic Click Models", International Conference on Machine Learning (ICML),
Sydney, Australia, 2017. [arXiv]
[C19]
S. Vaswani, B. Kveton, Z. Wen, M. Ghavamzadeh, L. Lakshmanan, and M. Schmidt,
"Diffusion Independent Semi-Bandit Influence Maximization", International Conference on Machine Learning (ICML),
Sydney, Australia, 2017. [arXiv]
[C16] S. Katariya, B. Kveton, C. Szepesvari, C. Vernade, and Z. Wen, "Stochastic Rank-1 Bandits",
the 20th International Conference on
Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida, 2017. [arXiv]
[C15] S. Zong, H. Ni, K. Sung, N. Ke, Z. Wen, and B. Kveton, "Cascading Bandits for Large-Scale Recommendation Problems", Conference on Uncertainty in Artificial Intelligence (UAI), New York City, New York, 2016. [arXiv]
(acceptance rate 31%)
[C6] B. Kveton, Z. Wen, A. Ashkan, H. Eydgahi, and B. Eriksson, "Matroid Bandits: Fast Combinatorial Optimization with Learning", Conference on Uncertainty in Artificial Intelligence (UAI), Quebec City, Canada, 2014. (plenary presentation, acceptance rate 8.2%)
[C3] Z. Wen, B. Kveton, B. Eriksson, and S. Bhamidipati, "Sequential Bayesian Search", International Conference on Machine Learning (ICML), Atlanta, Georgia, 2013. (acceptance rate: 24%)
Short Conference Papers
[SC2] S. Zong, B. Kveton, S. Berkovsky, A. Ashkan, N. Vlassis, and Z. Wen, "Does Weather Matter? Causal Analysis of TV Logs",
poster at World Wide Web Conference (WWW), Perth, Western Australia, 2017. [arXiv]
Workshop Papers
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[W9]
R. Zhang, C. Chen, Z. Gan, Z. Wen, W. Wang, and L. Carin, "Nested-Wasserstein Self-Imitation Learning for Sequence Generation", Deep Reinforcement Learning Workshop, NeurIPS 2019, Vancouver, Canada.
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[W8]
R. Zhang, C. Chen, Z. Gan, W. Wang, Z. Wen, and L. Carin, "Sequence Generation with a Guider Network", ICML'19 Workshop on Real-World Sequential Decision Making, Long Beach, CA.
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[W7]
R. Zhang, Z. Wen, C. Chen, and L. Carin,
"Scalable Thompson Sampling via Optimal Transport", Infer to Control
Workshop on Probabilistic Reinforcement Learning and Structured Control at NIPS 2018, Montréal, Canada.
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[W6]
A. Lobos, P. Grigas, Z. Wen, and K. Lee,
"Optimal Bidding, Allocation and Budget Spending for a Demand Side Platform Under Many Auction Types",
AdKDDTargetAd2018 Workshop at KDD 2018, London, United Kingdom. [arXiv]
(Best Student Paper)
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[W5] S. Li, Y. Abbasi-Yadkori, B. Kveton, S. Muthukrishnan, V. Vinay, and Z. Wen, "Offline Evaluation of Ranking Policies with Click Models", CausalML 2018, Stockholm, Sweden.
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[W4] C. Vernade, B. Kveton, Y. Abbasi-Yadkori, M. Ghavamzadeh, and
Z. Wen, "Rank-1 A/B Testing", Women in Machine Learning Workshop 2017.
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[W3] P. Grigas, A. Lobos, Z. Wen, and K. Lee, "Profit Maximization for Online Advertising Demand-Side Platforms", AdKDDTargetAd2017 Workshop at KDD 2017, Halifax, Canada.
[arXiv]
arXiv Preprints & Other Publication
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X. Lu, I. Osband, S. M. Asghari, S. Gowal, V. Dwaracherla, Z. Wen, B. Van Roy, "Robustness of Epinets against Distributional Shifts", arXiv preprint. [arXiv]
W. Mou, Z. Wen, and X. Chen, "On the Sample Complexity of Reinforcement Learning with Policy Space Generalization", arXiv preprint. [arXiv]
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B. Kveton, S. Mahdian, S. Muthukrishnan, Z. Wen, and Y. Xian, "Waterfall Bandits: Learning to Sell Ads Online", arXiv preprint. [arXiv]
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S. Vaswani, B. Kveton, Z. Wen, A. Rao, M. Schmidt, and Y. Abbasi-Yadkori, "New Insights into Bootstrapping for Bandits", arXiv preprint. [arXiv]
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B. Kveton, C. Szepesvari, A. Rao, Z. Wen, Y. Abbasi-Yadkori, and S. Muthukrishnan, "Stochastic Low-Rank Bandits", arXiv preprint. [arXiv]
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Z. Wen, E. Bax, and J. Li, "Revenue-Maximizing Mechanism Design for Quasi-Proportional Auctions", arXiv preprint. [arXiv]
B. Kveton, Z. Wen, A. Ashkan, and M. Valko, "Learning to Act Greedily: Polymatroid Semi-Bandits", arXiv preprint. [arXiv]
Code
Patents & Filed Patents
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[P10] Efficient Exploration of Offline Models to Warm Start Online Bandit Learning, with G. Theocharous, Y. Abbasi-Yadkori, and Q. Wu, US Patent App., filed, 2019.
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[P9] Multi-Task Equidistant Embedding, with H. Zhao, S. Kim, S. Li and B. Kveton, US Patent App., filed, 2018.
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[P8] Change Point Detection in a Multi-Armed Bandit Recommendation System, with Y. Cao and B. Kveton, US Patent App., filed, 2018.
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[P7] Online Training of Segmentation Model via Interactions With Interactive Computing Environment, with T. Yu, B. Kveton, and H. Bui, US Patent App., filed, 2018.
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[P6] Multivariate Digital Campaign Content Testing Utilizing Rank-1 Best-Arm Identification, with Y. Abbasi-Yadkori,
M. Ghavamzadeh, B. Kveton, and C. Vernade, US Patent App., filed, 2018.
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[P5] Training And Utilizing Item-level Importance Sampling Models for Offline Evaluation and Execution of Digital Content Selection Policies, with S. Li, Y. Abbasi-Yadkori, B. Kveton, and V. Vinay, US Patent App., filed, 2018.
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[P4] Online Diverse Set Generation From Partial-Click Feedback, with B. Kveton, P. Gupta, I. A. Burhanuddin, H. Singh, and G. Hiranandani, US Patent App., filed, 2018.
[P3] Influence Maximization Determination in a Social Network System, with S. Vaswani, B. Kveton,
and M. Ghavamzadeh, US Patent App., filed, 2017.
Theses
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