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Yexuan Shi

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3 papers

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IS Journal 2021 Journal Article

Federated Topic Discovery: A Semantic Consistent Approach

Yexuan Shi
Yongxin Tong
Zhiyang Su
Di Jiang
Zimu Zhou
Wenbin Zhang

General-purpose topic models have widespread industrial applications. Yet high-quality topic modeling is becoming increasingly challenging because accurate models require large amounts of training data typically owned by multiple parties, who are often unwilling to share their sensitive data for collaborative training without guarantees on their data privacy. To enable effective privacy-preserving multiparty topic modeling, we propose a novel federated general-purpose topic model named private and consistent topic discovery (PC-TD). On the one hand, PC-TD seamlessly integrates differential privacy in topic modeling to provide privacy guarantees on sensitive data of different parties. On the other hand, PC-TD exploits multiple sources of semantic consistency information to retain the accuracy of topic modeling while protecting data privacy. We verify the effectiveness of PC-TD on real-life datasets. Experimental results demonstrate its superiority over the state-of-the-art general-purpose topic models.

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AAAI Conference 2019 Conference Paper

A Unified Approach to Online Matching with Conflict-Aware Constraints

Pan Xu
Yexuan Shi
Hao Cheng
John Dickerson
Karthik Abinav Sankararaman
Aravind Srinivasan
Yongxin Tong
Leonidas Tsepenekas

Online bipartite matching and allocation models are widely used to analyze and design markets such as Internet advertising, online labor, and crowdsourcing. Traditionally, vertices on one side of the market are fixed and known a priori, while vertices on the other side arrive online and are matched by a central agent to the offline side. The issue of possible conflicts among offline agents emerges in various real scenarios when we need to match each online agent with a set of offline agents. For example, in event-based social networks (e. g. , Meetup), offline events conflict for some users since they will be unable to attend mutually-distant events at proximate times; in advertising markets, two competing firms may prefer not to be shown to one user simultaneously; and in online recommendation systems (e. g. , Amazon Books), books of the same type “conflict” with each other in some sense due to the diversity requirement for each online buyer. The conflict nature inherent among certain offline agents raises significant challenges in both modeling and online algorithm design. In this paper, we propose a unifying model, generalizing the conflict models proposed in (She et al. , TKDE 2016) and (Chen et al. , TKDE 16). Our model can capture not only a broad class of conflict constraints on the offline side (which is even allowed to be sensitive to each online agent), but also allows a general arrival pattern for the online side (which is allowed to change over the online phase). We propose an efficient linear programming (LP) based online algorithm and prove theoretically that it has nearly-optimal online performance. Additionally, we propose two LP-based heuristics and test them against two natural baselines on both real and synthetic datasets. Our LP-based heuristics experimentally dominate the baseline algorithms, aligning with our theoretical predictions and supporting our unified approach.

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AAAI Conference 2019 Conference Paper

Preference-Aware Task Assignment in On-Demand Taxi Dispatching: An Online Stable Matching Approach

Boming Zhao
Pan Xu
Yexuan Shi
Yongxin Tong
Zimu Zhou
Yuxiang Zeng

A central issue in on-demand taxi dispatching platforms is task assignment, which designs matching policies among dynamically arrived drivers (workers) and passengers (tasks). Previous matching policies maximize the profit of the platform without considering the preferences of workers and tasks (e. g. , workers may prefer high-rewarding tasks while tasks may prefer nearby workers). Such ignorance of preferences impairs user experience and will decrease the profit of the platform in the long run. To address this problem, we propose preference-aware task assignment using online stable matching. Specifically, we define a new model, Online Stable Matching under Known Identical Independent Distributions (OSM-KIID). It not only maximizes the expected total profits (OBJ-1), but also tries to satisfy the preferences among workers and tasks by minimizing the expected total number of blocking pairs (OBJ-2). The model also features a practical arrival assumption validated on real-world dataset. Furthermore, we present a linear program based online algorithm LP-ALG, which achieves an online ratio of at least 1−1/e on OBJ-1 and has at most 0. 6·|E| blocking pairs expectedly, where |E| is the total number of edges in the compatible graph. We also show that a natural Greedy can have an arbitrarily bad performance on OBJ-1 while maintaining around 0. 5·|E| blocking pairs. Evaluations on both synthetic and real datasets confirm our theoretical analysis and demonstrate that LP-ALG strictly dominates all the baselines on both objectives when tasks notably outnumber workers.

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