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Chaoli Zhang

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4 papers

1 author row

IJCAI Conference 2023 Conference Paper

Transformers in Time Series: A Survey

Qingsong Wen
Tian Zhou
Chaoli Zhang
Weiqi Chen
Ziqing Ma
Junchi Yan
Liang Sun

Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applications. In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. In particular, we examine the development of time series Transformers in two perspectives. From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis. From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification. Empirically, we perform robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series. Finally, we discuss and suggest future directions to provide useful research guidance.

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AAAI Conference 2020 Conference Paper

Mechanism Design with Predicted Task Revenue for Bike Sharing Systems

Hongtao Lv
Chaoli Zhang
Zhenzhe Zheng
Tie Luo
Fan Wu
Guihai Chen

Bike sharing systems have been widely deployed around the world in recent years. A core problem in such systems is to reposition the bikes so that the distribution of bike supply is reshaped to better match the dynamic bike demand. When the bike-sharing company or platform is able to predict the revenue of each reposition task based on historic data, an additional constraint is to cap the payment for each task below its predicted revenue. In this paper, we propose an incentive mechanism called TruPreTar to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand. TruPreTar possesses four important economic and computational properties such as truthfulness and budget feasibility. Furthermore, we prove that even when the payment budget is tight, the total revenue still exceeds or equals the budget. Otherwise, TruPre- Tar achieves 2-approximation as compared to the optimal (revenue-maximizing) solution, which is close to the lower bound of at least √ 2 that we also prove. Using an industrial dataset obtained from a large bike-sharing company, our experiments show that TruPreTar is effective in rebalancing bike supply and demand and, as a result, generates high revenue that outperforms several benchmark mechanisms.

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TCS Journal 2020 Journal Article

Pricing and allocation algorithm designs in dynamic ridesharing system

Chaoli Zhang
Jiapeng Xie
Fan Wu
Xiaofeng Gao
Guihai Chen

Traffic congestion and car pollution are becoming serious plagues nowadays. High travel cost brings a great burden to people and society. A ridesharing system mitigates traffic congestion and car pollution by allowing passengers to share their travel costs with others. Traditional ridesharing platforms usually require passengers to submit their plans in advance and then design schedules for the drivers who would like to offer a ride. Nowadays, with the development of the smartphone technology, dynamic ridesharing systems enable passengers request a car anytime and anywhere. This paper mainly considers the problems of how to allocate passengers to drivers, how to charge the passengers and how to design feasible schedules for the driver in such online environment. The allocation problem is modeled as an online weighted matching problem with the graph changing over time. Firstly, we give a fair pricing method which is easy to be understood and accepted by the passengers. We develop a greedy algorithm called LiqMax_Gre for the purpose of maximizing liquidity and an algorithm called UtiMax for the purpose of maximizing utility. LiqMax_Gre achieves a competitive ratio of 1 λ + 1, where λ is the maximal number of passengers a car can take. In general, we prove that no online algorithm can have a good guarantee for the design goal of maximizing utility. Innovatively, UtiMax considers not only the current maximal utility, but also the opportunity cost, which is the utility contributed by occupying a seat. It reflects the utility in the future and thus can be used to handle the difficulty in online design. We prove that, our algorithm has a competitive ratio of 1/3 in a special case. The schedule problem is NP-hard and we design a heuristic nearest neighbor algorithm to solve it.

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AAMAS Conference 2018 Conference Paper

Efficient Auctions with Identity-Dependent Negative Externalities

Chaoli Zhang
Xiang Wang
Fan Wu
Xiaohui Bei

We investigate a class of single-item multi-supply auctions (including digital goods auctions with unlimited supply) with bidders who have identity-based negative externalities. In such an auction, each bidder has a set of competitors. Her private valuation from winning an item decreases with the number of her winning competitors. Negative externalities are prevalent in many applications, in which the auctioned goods play a role in future interactions among the auction’s participants, such as patent licensing and sponsored search auctions. However, the development of auctions with such externalities is stymied by the computational difficulty of the underlying welfare maximization allocation problem; even without consideration of truthfulness, the problem of social welfare maximization with general competition relations is NP-hard and even hard to approximate within a constant factor (unless P=NP). In this work, we design polynomial time and strategy-proof mechanisms under different restrictions on the underlying competition graph structure. Our results can be summarized as follows. (1) When each bidder has only one competitor, we propose a truthful and welfare maximizing mechanism. (2) We design a truthful and (1 + ϵ)-approximation mechanism when the underlying competition graph is planar. (3) We give two truthful mechanisms when bidders have arbitrary competition relations, with welfare approximation ratio (n/ logn) and ⌈(d + 1)/3⌉, respectively, where d is the maximum degree of the “undirected” competition graph.

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