Chun Wang

Xiaoming Li, Hubert Normandin-Taillon, Chun Wang et al.

In the realm of Mobility-on-Demand (MoD) systems, the forecasting of rider demand is a cornerstone for operational decision-making and system optimization. Traditional forecasting methodologies primarily yield point estimates, thereby neglecting the inherent uncertainty within demand projections. Moreover, MoD demand levels are profoundly influenced by both endogenous and exogenous factors, leading to high and dynamic volatility. This volatility significantly undermines the efficacy of conventional time series forecasting methods. In response, we propose an Extended Recurrent Mixture Density Network (XRMDN), a novel deep learning framework engineered to address these challenges. XRMDN leverages a sophisticated architecture to process demand residuals and variance through correlated modules, allowing for the flexible incorporation of endogenous and exogenous data. This architecture, featuring recurrent connections within the weight, mean, and variance neural networks, adeptly captures demand trends, thus significantly enhancing forecasting precision, particularly in high-volatility scenarios. Our comprehensive experimental analysis, utilizing real-world MoD datasets, demonstrates that XRMDN surpasses the existing benchmark models across various metrics, notably excelling in high-demand volatility contexts. This advancement in probabilistic demand forecasting marks a significant contribution to the field, offering a robust tool for enhancing operational efficiency and customer satisfaction in MoD systems.

Tianyu Wang, Ningyuan Chen, Chun Wang

In prescriptive analytics, the decision-maker observes historical samples of $(X, Y)$, where $Y$ is the uncertain problem parameter and $X$ is the concurrent covariate, without knowing the joint distribution. Given an additional covariate observation $x$, the goal is to choose a decision $z$ conditional on this observation to minimize the cost $\mathbb{E}[c(z,Y)|X=x]$. This paper proposes a new distributionally robust approach under Wasserstein ambiguity sets, in which the nominal distribution of $Y|X=x$ is constructed based on the Nadaraya-Watson kernel estimator concerning the historical data. We show that the nominal distribution converges to the actual conditional distribution under the Wasserstein distance. We establish the out-of-sample guarantees and the computational tractability of the framework. Through synthetic and empirical experiments about the newsvendor problem and portfolio optimization, we demonstrate the strong performance and practical value of the proposed framework.

Ningyuan Chen, Chun Wang, Longlin Wang

A standard assumption adopted in the multi-armed bandit (MAB) framework is that the mean rewards are constant over time. This assumption can be restrictive in the business world as decision-makers often face an evolving environment where the mean rewards are time-varying. In this paper, we consider a non-stationary MAB model with $K$ arms whose mean rewards vary over time in a periodic manner. The unknown periods can be different across arms and scale with the length of the horizon $T$ polynomially. We propose a two-stage policy that combines the Fourier analysis with a confidence-bound-based learning procedure to learn the periods and minimize the regret. In stage one, the policy correctly estimates the periods of all arms with high probability. In stage two, the policy explores the periodic mean rewards of arms using the periods estimated in stage one and exploits the optimal arm in the long run. We show that our learning policy incurs a regret upper bound $\tilde{O}(\sqrt{T\sum_{k=1}^K T_k})$ where $T_k$ is the period of arm $k$. Moreover, we establish a general lower bound $Ω(\sqrt{T\max_{k}\{ T_k\}})$ for any policy. Therefore, our policy is near-optimal up to a factor of $\sqrt{K}$.

Chun Wang, Shirui Pan, Ruiqi Hu et al.

Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.

Chun Wang, Steve T. K. Jan, Hang Hu et al.

Leaked passwords from data breaches can pose a serious threat to users if the password is reused elsewhere. With more online services getting breached today, there is still a lack of large-scale quantitative understanding of the risks of password reuse across services. In this paper, we analyze a large collection of 28.8 million users and their 61.5 million passwords across 107 services. We find that 38% of the users have reused exactly the same password across different sites, while 20% have modified an existing password to create new ones. In addition, we find that the password modification patterns are highly consistent across different user demographics, indicating a high predictability. To quantify the risk, we build a new training-based guessing algorithm, and show that more than 16 million password pairs can be cracked within just 10 attempts (30% of the modified passwords and all the reused passwords).

Steve T. K. Jan, Chun Wang, Qing Zhang et al.

Community-based question answering (CQA) services are facing key challenges to motivate domain experts to provide timely answers. Recently, CQA services are exploring new incentive models to engage experts and celebrities by allowing them to set a price on their answers. In this paper, we perform a data-driven analysis on two emerging payment-based CQA systems: Fenda (China) and Whale (US). By analyzing a large dataset of 220K questions (worth 1 million USD collectively), we examine how monetary incentives affect different players in the system. We find that, while monetary incentive enables quick answers from experts, it also drives certain users to aggressively game the system for profits. In addition, in this supplier-driven marketplace, users need to proactively adjust their price to make profits. Famous people are unwilling to lower their price, which in turn hurts their income and engagement over time. Finally, we discuss the key implications to future CQA design.