Research
Research papers
[1] Heterogeneous Values of One Data Point: Active Label Acquisition in Assortment Optimization
Mo Liu, Junyu Cao, Zuo-jun Max Shen. To be resubmitted to Management Science. [link] [Slides]
“How much is the marginal contribution to the revenue increase when adding one new customer with some specific feature into the training set?”
By evaluating this marginal contribution of each customer, we are able to identify informative customers and reduce the size of the training set by about 80% percent while maintaining the same level of revenue.
[2] Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach
Mo Liu, Paul Grigas, Heyuan Liu, Zuo-jun Max Shen. To be resubmitted to Management Science. [link] [Slides]
- “How to identify informative samples for the decision-making?”
- Best Student Paper Nominee at INFORMS Workshop on Data Science 2023
- Second Place Poster Prize at YinzOR 2023
[3] Learning from Click Transition Data: The Effectiveness of Greedy Pricing Policy under Dynamic Product Availability
Mo Liu, Junyu Cao, Zuo-jun Max Shen. To be submitted to Management Science. [link] , [5-minute Talk] (Previous version) [Slides] (Previous version)
- Finalist at 2023 INFORMS Service Science Student Competition
- Fan Favoriate Flash Talk at YinzOR 2023
[4] Active Learning by Prediction Uncertainty: Applications in Contextual Stochastic Linear Optimization
Mo Liu, Paul Grigas, Zuo-jun Max Shen. Woking paper.
[5] End-to-End Deep Learning for Automatic Inventory Management with Fixed Ordering Cost
Mo Liu, Meng Qi, Zuo-jun Max Shen. Working paper. [link]
Patent in application
Machine Learning And Optimization With Partially Observable Time Series Data
Zachary Xue, Mo Liu, Markus Ettl, Shivaram Subramanian
Selected Talks
- INFORMS Workshop on Data Science 2023, Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach, Oct 14, 2023
- INFORMS Service Science Student Competition 2023, Pricing under the Generalized Markov Chain Choice Model: Learning through Large-scale Click Behaviors, October 16
- INFORMS Annual Meeting Talk, Active Label Acquisition with Personalized Incentives in Assortment Optimization, Oct 17
- Purdue Operations Conference, Active Label Acquisition with Personalized Incentives in the Assortment Optimization, Sept. 9, 2023
- CMU YinzOR Student Competition, Pricing under the Generalized Markov Chain Choice Model: Learning through Large-scale Click Behaviors, August, 2023
- International Conference Stochastic Programming, Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach, July, 2023
- MSOM Conference, Personalized Incentive for Active Label Acquisition in the Assortment Optimization, June, 2023
- IBM Research Intern Talk, Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach, June, 2022
- INFORMS Annual Meeting, Pricing under the Generalized Markov Chain Choice Model: Learning through Large-scale Click Behavior, Oct, 2022
- INFORMS Annual Meeting, End-to-End Deep Learning for the Inventory Management with Fixed Ordering Cost, Oct, 2020