Zheng Wen - Publications

   

In Progress

  • S. Vaswani, B. Kveton, Z. Wen, A. Rao, M. Schmidt, and Y. Abbasi-Yadkori, "New Insights into Bootstrapping for Bandits", submitted. [arXiv]

  • S. Katariya, B. Kveton, Z. Wen, and V. K. Potluru, "Conservative Exploration using Interleaving", submitted. [arXiv]

  • Y. Cao, Z. Wen, B. Kveton, and Y. Xie, "Nearly Optimal Adaptive Procedure for Piecewise-Stationary Bandit: a Change-Point Detection Approach", submitted. [arXiv]

  • B. Kveton, C. Szepesvari, A. Rao, Z. Wen, Y. Abbasi-Yadkori, and S. Muthukrishnan, "Stochastic Low-Rank Bandits", submitted. [arXiv]

  • Z. Wen, E. Bax, and J. Li, "Revenue-Maximizing Mechanism Design for Quasi-Proportional Auctions", to be submitted. [arXiv]

Book Chapters

Journal Papers

  • [J4] B. Kveton, Z. Wen, A. Ashkan, and M. Valko, "Learning to Act Greedily: Polymatroid Semi-Bandits", accepted by Journal of Machine Learning Research, conditioned on minor revisions. [arXiv]

Full Conference Papers

  • [C23] T. Yu, B. Kveton, Z. Wen, H. Bui, O. J. Mengshoel, "SpectralFPL: Online Spectral Learning for Single Topic Models", accepted by ECML-PKDD 2018, Dublin, Ireland. [arXiv]

  • [C22] S. Li, Y. Abbasi-Yadkori, B. Kveton, S. Muthukrishnan, V. Vinay, and Z. Wen, "Offline Evaluation of Ranking Policies with Click Models", accepted by KDD 2018, London, United Kingdom. [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]

  • [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

  • [SC4] X. Lu, Z. Wen, B. Kveton, "Efficient Online Recommendation via Low-Rank Ensemble Sampling", short paper in RecSys 2018 Proceedings, Vancouver, Canada.

Workshop Papers & Other Publication

  • [W7] 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]

  • [W6] 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.

  • [W5] C. Vernade, B. Kveton, Y. Abbasi-Yadkori, M. Ghavamzadeh, and Z. Wen, "Rank-1 A/B Testing", Women in Machine Learning Workshop 2017.

  • [W4] P. Grigas, A. Lobos, Z. Wen, and K. Lee, "Pro fit Maximization for Online Advertising Demand-Side Platforms", AdKDDTargetAd2017 Workshop at KDD 2017, Halifax, Canada. [arXiv]

Patents & Filed Patents

  • [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.

  • [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.

  • [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.

  • [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.

Selected Talks

  • Z. Wen and B. Van Roy, "Generalization and Exploration via Randomized Value Functions", INFORMS Annual Meeting 2014, San Francisco, CA.

  • Z. Wen and B. Van Roy, "Efficient Reinforcement Learning with Compact Value Function Approximations", INFORMS Annual Meeting 2013, Minneapolis, MN.

  • Z. Wen, B. Kveton, and S. Bhamidipati, "Learning to Discover: a Bayesian Approach", NIPS Workshop on Bayesian Optimization and Decision Making, Lake Tahoe, NV, December 2012.

  • Z. Wen and B. Van Roy, "Efficient Reinforcement Learning with Sparse Representations", INFORMS Annual Meeting 2012, Phoenix, AZ.

  • Z. Wen, H. Maei, and D. O'Neill, "Optimal Demand Response using Device Based Reinforcement Learning", INFORMS Annual Meeting 2012, Phoenix, AZ.

  • Z. Wen and B. Van Roy, "Efficient Reinforcement Learning", INFORMS Annual Meeting 2011, Charlotte, NC.

  • Z. Wen, K. Aziz, L. Durlofsky, and B. Van Roy, "Reservoir Management Based on Approximate Dynamic Programming", INFORMS Annual Meeting 2009, San Diego, CA.

Theses